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1. Variational Shape Reconstruction via Quadric Error Metrics

Author: Zhao, Tong, Busé, Laurent, Cohen-Steiner, David, Boubekeur, Tamy, Thiery, Jean-Marc, Alliez, Pierre, Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AlgebRe, geOmetrie, Modelisation et AlgoriTHmes (AROMATH), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-National and Kapodistrian University of Athens (NKUA), Understanding the Shape of Data (DATASHAPE), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria), Adobe Research, and ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
Subjects: quadric error metrics, Surface reconstruction, concise mesh reconstruction, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 3D point cloud, clustering
Abstract: International audience; Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the input points enriched with quadric error metrics, where the generator of each cluster is the optimal 3D point for the sum of its quadric error metrics. This approach favors the placement of generators on sharp features, and tends to equidistribute the error among clusters. We reconstruct the output surface mesh from the adjacency between clusters and a constrained binary solver. We combine our clustering process with an adaptive refinement driven by the error. Compared to prior art, our method avoids dense reconstruction prior to simplification and produces immediately an optimized mesh.
Published: 2023
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2. VideoDoodles: Hand-Drawn Animations on Videos with Scene-Aware Canvases

Author: Yu, Emilie, Blackburn-Matzen, Kevin, Nguyen, Cuong, Wang, Oliver, Habib Kazi, Rubaiat, Bousseau, Adrien, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Adobe Research, Delft University of Technology (TU Delft), and European Project: 714221,H2020 Pilier ERC,ERC-2016-STG-714221,D3(2017)
Subjects: motion design, video editing, video depth, interface, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Abstract: International audience; We present an interactive system to ease the creation of so-called "video doodles" -- videos on which artists insert hand-drawn animations for entertainment or educational purposes. Video doodles are challenging to create because to be convincing, the inserted drawings must appear as if they were part of the captured scene. In particular, the drawings should undergo tracking, perspective deformations and occlusions as they move with respect to the camera and to other objects in the scene -- visual effects that are difficult to reproduce with existing 2D video editing software. Our system supports these effects by relying on planar canvases that users position in a 3D scene reconstructed from the video. Furthermore, we present a custom tracking algorithm that allows users to anchor canvases to static or dynamic objects in the scene, such that the canvases move and rotate to follow the position and direction of these objects. When testing our system, novices could create a variety of short animated clips in a dozen of minutes, while professionals praised its speed and ease of use compared to existing tools.
Published: 2023
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3. Free2CAD

Author: Changjian Li, Hao Pan, Adrien Bousseau, Niloy J. Mitra, University College of London [London] (UCL), GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Microsoft Research Asia, and Adobe Research
Subjects: CAD modeling, Procedural modeling, Shape modeling, Sketch Interface, Computer Graphics and Computer-Aided Design, Computing methodologies, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Abstract: CAD modeling, despite being the industry-standard, remains restricted to usage by skilled practitioners due to two key barriers. First, the user must be able to mentally parse a final shape into a valid sequence of supported CAD commands; and second, the user must be sufficiently conversant with CAD software packages to be able to execute the corresponding CAD commands. As a step towards addressing both these challenges, we present Free2CAD wherein the user can simply sketch the final shape and our system parses the input strokes into a sequence of commands expressed in a simplified CAD language. When executed, these commands reproduce the sketched object. Technically, we cast sketch-based CAD modeling as a sequence-to-sequence translation problem, for which we leverage the powerful Transformers neural network architecture. Given the sequence of pen strokes as input, we introduce the new task of grouping strokes that correspond to individual CAD operations. We combine stroke grouping with geometric fitting of the operation parameters, such that intermediate groups are geometrically corrected before being reused, as context, for subsequent steps in the sequence inference. Although trained on synthetically-generated data, we demonstrate that Free2CAD generalizes to sketches created from real-world CAD models as well as to sketches drawn by novice users. Code and data are at https://github.com/Enigma-li/Free2CAD.
Published: 2022
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4. Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Author: Francis, Anthony, Pérez-d'Arpino, Claudia, Li, Chengshu, Xia, Fei, Alahi, Alexandre, Alami, Rachid, Bera, Aniket, Biswas, Abhijat, Biswas, Joydeep, Chandra, Rohan, Lewis Chiang, Hao-Tien, Everett, Michael, Ha, Sehoon, Hart, Justin, How, Jonathan, Karnan, Haresh, Lee, Tsang-Wei, Manso, Luis, Mirksy, Reuth, Pirk, Sören, Singamaneni, Phani Teja, Stone, Peter, Taylor, Ada, Trautman, Peter, Tsoi, Nathan, Vázquez, Marynel, Xiao, Xuesu, Xu, Peng, Yokoyama, Naoki, Toshev, Alexander, Martín-Martín, Roberto, Logical robotics, NVIDIA (NVIDIA), Stanford University, Google Inc, Research at Google, Ecole Polytechnique Fédérale de Lausanne (EPFL), Équipe Robotique et InteractionS (LAAS-RIS), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS), Purdue University [West Lafayette], Carnegie Mellon University [Pittsburgh] (CMU), University of Texas at Austin [Austin], Northeastern University [Boston], Georgia Institute of Technology [Atlanta], Massachusetts Institute of Technology (MIT), Aston University [Birmingham], Bar-Ilan University [Israël], Adobe Research, Sony Corporation, Honda Research Institute [Mountain View] (HRI-US), Yale University [New Haven], George Mason University [Fairfax], and Apple Inc
Subjects: FOS: Computer and information sciences, Computer Science - Robotics, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, I.2.9, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO]Computer Science [cs], Robotics (cs.RO), Human-Computer Interaction (cs.HC), Machine Learning (cs.LG)
Abstract: A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets., 43 pages, 11 figures, 6 tables
Published: 2023

5. ConTesse: Accurate Occluding Contours for Subdivision Surfaces

Author: Chenxi Liu, Pierre Bénard, Aaron Hertzmann, Shayan Hoshyari, University of British Columbia (UBC), Melting the frontiers between Light, Shape and Matter (MANAO), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Photonique, Numérique et Nanosciences (LP2N), Université de Bordeaux (UB)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS), Adobe Research, and ANR-20-CE33-0002,MoStyle,Stylisation du mouvement pour l'animation 2D(2020)
Subjects: Computational Geometry (cs.CG), FOS: Computer and information sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Geometry processing, Occluding Contours, Computer Graphics and Computer-Aided Design, Graphics (cs.GR), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Non-photorealistic rendering, Computer Science - Graphics, Computer Science::Graphics, Computer Science::Computer Vision and Pattern Recognition, Computer Science - Computational Geometry, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: This paper proposes a method for computing the visible occluding contours of subdivision surfaces. The paper first introduces new theory for contour visibility of smooth surfaces. Necessary and sufficient conditions are introduced for when a sampled occluding contour is valid, that is, when it may be assigned consistent visibility. Previous methods do not guarantee these conditions, which helps explain why smooth contour visibility has been such a challenging problem in the past. The paper then proposes an algorithm that, given a subdivision surface, finds sampled contours satisfying these conditions, and then generates a new triangle mesh matching the given occluding contours. The contours of the output triangle mesh may then be rendered with standard non-photorealistic rendering algorithms, using the mesh for visibility computation. The method can be applied to any triangle mesh, by treating it as the base mesh of a subdivision surface., Accepted to ACM Transactions on Graphics (TOG)
Published: 2023
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6. Complex Wrinkle Field Evolution

Author: Chen, Zhen, Kaufman, Danny, Skouras, Mélina, Vouga, Etienne, University of Texas at Austin [Austin], Adobe Research, Authoring and directing story worlds (ANIMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)
Subjects: discrete differential geometry, wrinkle fields, complex representation, [INFO]Computer Science [cs], singularities, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Abstract: International audience; We propose a new approach for representing wrinkles, designed to capture complex and detailed wrinkle behavior on coarse triangle meshes, called Complex Wrinkle Fields. Complex Wrinkle Fields consist of an almost-everywhere-unit complex-valued phase function over the surface; a frequency one-form; and an amplitude scalar, with a soft compatibility condition coupling the frequency and phase. We develop algorithms for interpolating between two such wrinkle fields, for visualizing them as displacements of a Loop-subdivided refinement of the base mesh, and for making smooth local edits to the wrinkle amplitude, frequency, and/or orientation. These algorithms make it possible, for the first time, to create and edit animations of wrinkles on triangle meshes that are smooth in space, evolve smoothly through time, include singularities along with their complex interactions, and that represent frequencies far finer than the surface resolution.
Published: 2023

7. Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

Author: Julien Philip, Sébastien Morgenthaler, Michaël Gharbi, George Drettakis, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Adobe Research, This research was funded in part by the ERC Advanced grant FUNGRAPH N° 788065 (http://fungraph.inria.fr). The authors are grateful to the OPAL infrastructure from Université Côte d’Azur for providing resources and support., and European Project: 788065,H2020 Pilier ERC,FUNGRAPH(2018)
Subjects: FOS: Computer and information sciences, Mirror image, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Relighting, 02 engineering and technology, Rendering (computer graphics), Computer Science - Graphics, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Neural Rendering, 0202 electrical engineering, electronic engineering, information engineering, Polygon mesh, Computer vision, Image-Based Rendering, ComputingMethodologies_COMPUTERGRAPHICS, business.industry, Deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Image-based modeling and rendering, Computer Graphics and Computer-Aided Design, Sample (graphics), Graphics (cs.GR), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 020202 computer hardware & architecture, Feature (computer vision), Path tracing, Multi-View, Artificial intelligence, business
Abstract: We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a three-dimensional mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images . We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques.
Published: 2021
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8. Focal Length and Object Pose Estimation via Render and Compare

Author: Ponimatkin, Georgy, Labbé, Yann, Russell, Bryan, Aubry, Mathieu, Sivic, Josef, IMAGINE [Marne-la-Vallée], Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Models of visual object recognition and scene understanding (WILLOW), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Adobe Research, Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC), Czech Technical University in Prague (CTU), This work was partly supported by the European Regional Development Fund under the project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000468), the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140), the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute)., and ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Abstract: We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are twofold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods., Accepted to CVPR2022. Code available at http://github.com/ponimatkin/focalpose
Published: 2022
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9. Example-Based Microstructure Rendering with Constant Storage

Author: Beibei Wang, Miloš Hašan, Nicolas Holzschuch, Ling-Qi Yan, School of Computer and Software [Nanjing University] (SCS - NUIST), Nanjing University of Information Science and Technology (NUIST), Adobe Research, Models and Algorithms for Visualization and Rendering (MAVERICK), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), University of California [Santa Barbara] (UC Santa Barbara), University of California (UC), University of California [Santa Barbara] (UCSB), and University of California
Subjects: Surface microstructure, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, 02 engineering and technology, Microstructure, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Rendering, Rendering (computer graphics), Glints, Constant storage, Normal mapping, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Specular reflection, Procedural by-example noise, Algorithm, Texture synthesis
Abstract: International audience; Rendering glinty details from specular microstructure enhances the level of realism, but previous methods require heavy storage for the high-resolution height field or normal map and associated acceleration structures. In this article, we aim at dynamically generating theoretically infinite microstructure, preventing obvious tiling artifacts, while achieving constant storage cost. Unlike traditional texture synthesis, our method supports arbitrary point and range queries, and is essentially generating the microstructure implicitly. Our method fits the widely used microfacet rendering framework with multiple importance sampling (MIS), replacing the commonly used microfacet normal distribution functions (NDFs) like ground glass distribution (GGX) by a detailed local solution, with a small amount of runtime performance overhead.
Published: 2020
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10. Active Exploration for Neural Global Illumination of Variable Scenes

Author: Stavros Diolatzis, Julien Philip, George Drettakis, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Adobe Research, and ANR-11-IS10-0003,FUNGRAPH,Fabrication et applications des dispositifs de graphène fonctionnalisés(2011)
Subjects: FOS: Computer and information sciences, Neural Networks, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Graphics (cs.GR), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science - Graphics, Deep Learning, Neural Rendering, Global Illumination, Computer Graphics, MESH: OPAL-Meso, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: Neural rendering algorithms introduce a fundamentally new approach for photorealistic rendering, typically by learning a neural representation of illumination on large numbers of ground truth images. When training for a given variable scene, i.e., changing objects, materials, lights and viewpoint, the space D of possible training data instances quickly becomes unmanageable as the dimensions of variable parameters increase. We introduce a novel Active Exploration method using Markov Chain Monte Carlo, which explores D, generating samples (i.e., ground truth renderings) that best help training and interleaves training and on-the-fly sample data generation. We introduce a self-tuning sample reuse strategy to minimize the expensive step of rendering training samples. We apply our approach on a neural generator that learns to render novel scene instances given an explicit parameterization of the scene configuration. Our results show that Active Exploration trains our network much more efficiently than uniformly sampling, and together with our resolution enhancement approach, achieves better quality than uniform sampling at convergence. Our method allows interactive rendering of hard light transport paths (e.g., complex caustics) -- that require very high samples counts to be captured -- and provides dynamic scene navigation and manipulation, after training for 5-18 hours depending on required quality and variations., Comment: Project page: http://repo-sam.inria.fr/fungraph/active-exploration/
Published: 2022
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11. Fine Wrinkling on Coarsely Meshed Thin Shells

Author: ChenZhen, VougaEtienne, M KaufmanDanny, SkourasMélina, ChenHsiao-Yu, University of Texas at Austin [Austin], Adobe Research, Authoring and directing story worlds (ANIMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)
Subjects: Materials science, Physical simulation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Geometry, 02 engineering and technology, 01 natural sciences, Computer Graphics and Computer-Aided Design, cloth simulation, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 010305 fluids & plasmas, tension field theory, mechanics of thin shells, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Thin shells, Polygon mesh, [INFO]Computer Science [cs], Statics, ComputingMethodologies_COMPUTERGRAPHICS, wrinkle simulation
Abstract: International audience; We propose a new model and algorithm to capture the high-definition statics of thin shells via coarse meshes. This model predicts global, fine-scale wrinkling at frequencies much higher than the resolution of the coarse mesh; moreover, it is grounded in the geometric analysis of elasticity, and does not require manual guidance, a corpus of training examples, nor tuning of ad hoc parameters. We first approximate the coarse shape of the shell using tension field theory, in which material forces do not resist compression. We then augment this base mesh with wrinkles, parameterized by an amplitude and phase field that we solve for over the base mesh, which together characterize the geometry of the wrinkles. We validate our approach against both physical experiments and numerical simulations, and we show that our algorithm produces wrinkles qualitatively similar to those predicted by traditional shell solvers requiring orders of magnitude more degrees of freedom.
Published: 2021
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12. Informational Texture Synthesis

Author: Lefebvre, Sylvain, Wei, Li-Yi, Barnes, Connelly, Matter from Graphics (MFX), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Algorithms, Computation, Image and Geometry (LORIA - ALGO), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Adobe Research, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Geometry and Lighting (ALICE), Department of Algorithms, Computation, Image and Geometry (LORIA - ALGO), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Inria Nancy - Grand Est, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Hardware_REGISTER-TRANSFER-LEVELIMPLEMENTATION, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Abstract: This paper presents a novel technique to embed messages into textures synthesized from tiles with boundary constraints. The messages are encoded in the choices that the synthesis algorithm normally makes at random during the synthesis process. Images embedding messages are not distinguishable from images that are not embedding any message. The random sequence seed used during synthesis can act as a password.
Published: 2021

13. Point-Based Neural Rendering with Per-View Optimization

Author: George Drettakis, Julien Philip, Georgios Kopanas, Thomas Leimkühler, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Adobe Research, This research was funded by the ERC Advanced grant FUNGRAPH N° 788065 (http://fungraph.inria.fr). The authors are grateful to the OPAL infrastructure from Université Côte d’Azur for providing resources and support., and European Project: 788065,H2020 Pilier ERC,FUNGRAPH(2018)
Subjects: FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Pipeline (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Per-View Optimization, Rendering (computer graphics), Computer Science - Graphics, Neural Rendering, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Point (geometry), Image-Based Rendering, Representation (mathematics), ComputingMethodologies_COMPUTERGRAPHICS, business.industry, Probabilistic logic, Process (computing), 020207 software engineering, Image-based modeling and rendering, Computer Graphics and Computer-Aided Design, Graphics (cs.GR), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], View synthesis, Multi-View, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract: There has recently been great interest in neural rendering methods. Some approaches use 3D geometry reconstructed with Multi-View Stereo (MVS) but cannot recover from the errors of this process, while others directly learn a volumetric neural representation, but suffer from expensive training and inference. We introduce a general approach that is initialized with MVS, but allows further optimization of scene properties in the space of input views, including depth and reprojected features, resulting in improved novel-view synthesis. A key element of our approach is our new differentiable point-based pipeline, based on bi-directional Elliptical Weighted Average splatting, a probabilistic depth test and effective camera selection. We use these elements together in our neural renderer, that outperforms all previous methods both in quality and speed in almost all scenes we tested. Our pipeline can be applied to multi-view harmonization and stylization in addition to novel-view synthesis., https://repo-sam.inria.fr/fungraph/differentiable-multi-view/
Published: 2021
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14. StreamSketch: Exploring Multi-Modal Interactions in Creative Live Streams

Author: Li-Yi Wei, Karrie Karahalios, Rubaiat Habib Kazi, Zhicong Lu, Mira Dontcheva, Department of Computer Science [University of Toronto] (DCS), University of Toronto, and Adobe Research
Subjects: Computer Networks and Communications, Computer science, Process (engineering), media_common.quotation_subject, 02 engineering and technology, computer.software_genre, Formative assessment, Interactivity, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Affordance, 050107 human factors, creativity, visualization, media_common, Modalities, Modality (human–computer interaction), Multimedia, 05 social sciences, 020207 software engineering, Creativity, Visualization, Human-Computer Interaction, user engagement, Live streaming, sketching, computer, Social Sciences (miscellaneous)
Abstract: Creative live streams, where artists or designers demonstrate their creative process, have emerged as a unique and popular genre of live streams due to the real-time interactivity they afford. However, streamer-viewer interactions on most live streaming platforms only enable users to utilize text and emojis to communicate, which limits what viewers can convey and share in real time. To investigate the design space of potential visual and non-textual modalities within creative live streams, we first analyzed existing Twitch extensions and conducted a formative study with streamers who share creative activities to uncover key challenges that these streamers face. We then designed and implemented a prototype system, StreamSketch, which enables viewers and streamers to interact during live streams using multiple modalities, including freeform sketches and text. The prototype was evaluated by two professional artist streamers and their viewers during six streaming sessions. Overall, streamers and viewers found that StreamSketch provided increased engagement and new affordances compared to the traditional text-only modality, and highlighted how efficiency, moderation, and tool integration were continued challenges.
Published: 2021
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15. Learning elementary structures for 3D shape generation and matching

Author: Deprelle, Theo, Groueix, Thibault, Fisher, Matthew, Kim, Vladimir G., Russell, Bryan C., Aubry, Mathieu, Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, imagine [Marne-la-Vallée], Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Adobe Research, Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), and Groueix, Thibault
Subjects: FOS: Computer and information sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], ComputingMethodologies_COMPUTERGRAPHICS
Abstract: We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We demonstrate that the learned elementary 3D structures lead to clear improvements in 3D shape generation and matching. More precisely, we present two complementary approaches for learning elementary structures: (i) patch deformation learning and (ii) point translation learning. Both approaches can be extended to abstract structures of higher dimensions for improved results. We evaluate our method on two tasks: reconstructing ShapeNet objects and estimating dense correspondences between human scans (FAUST inter challenge). We show 16% improvement over surface deformation approaches for shape reconstruction and outperform FAUST inter challenge state of the art by 6%.
Published: 2021

16. Video‐Based Rendering of Dynamic Stationary Environments from Unsynchronized Inputs

Author: Theo Thonat, Frédo Durand, Miika Aittala, George Drettakis, Sylvain Paris, Yagiz Aksoy, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), Adobe Research, Computer Vision Laboratory - ETHZ [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Massachusetts Institute of Technology (MIT), NVIDIA (NVIDIA), and European Project: 788065,H2020 Pilier ERC,FUNGRAPH(2018)
Subjects: business.industry, Computer science, Volume (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, 02 engineering and technology, Variable bitrate, Image-based modeling and rendering, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Rendering (computer graphics), Image (mathematics), Dimension (vector space), Rippling, Simplicity (photography), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Image-Based Rendering, business, Video-Based Rendering, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: International audience; Image-Based Rendering allows users to easily capture a scene using a single camera and then navigate freely with realistic results. However, the resulting renderings are completely static, and dynamic effects – such as fire, waterfalls or small waves – cannot be reproduced. We tackle the challenging problem of enabling free-viewpoint navigation including such stationary dynamic effects, but still maintaining the simplicity of casual capture. Using a single camera – instead of previous complex synchronized multi-camera setups – means that we have unsynchronized videos of the dynamic effect from multiple views, making it hard to blend them when synthesizing novel views. We present a solution that allows smooth free-viewpoint video-based rendering (VBR) of such scenes using temporal Laplacian pyramid decomposition video, enabling spatio-temporal blending. For effects such as fire and waterfalls, that are semi-transparent and occupy 3D space, we first estimate their spatial volume. This allows us to create per-video geometries and alpha-matte videos that we can blend using our frequency-dependent method. We also extend Laplacian blending to the temporal dimension to remove additional temporal seams. We show results on scenes containing fire, waterfalls or rippling waves at the seaside, bringing these scenes to life.
Published: 2021
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17. Spherical Light Integration over Spherical Caps via Spherical Harmonics

Author: Li-Yi Wei, Zihong Zhou, South China Agricultural University (SCAU), and Adobe Research
Subjects: Physics, ray tracing, Monte Carlo method, Spherical harmonics, spherical harmonics, 020207 software engineering, 02 engineering and technology, area lighting, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Computational physics, Rendering (computer graphics), Harmonics, 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), 020201 artificial intelligence & image processing, Ray tracing (graphics)
Abstract: International audience; Spherical area light sources are widely used in synthetic rendering. However, traditional Monte Carlo methods can require an excessive number of samples for sufficient accuracy. We propose a Spherical Harmonics (SH) based method to provide a trade-off between performance and accuracy. Our key idea is an analytical integration of SH over spherical caps. The SH integration is first decomposed into a weighted sum of Zonal Harmonics (ZH) integration, which could be evaluated using recurrence formulae. The resulting integration could then be used for rendering spherical area lights efficiently, saving 50% light samples at best while maintaining competitive accuracy. Our method can easily fit into an existing SH based rendering framework to support near-field sphere lighting.
Published: 2020
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18. Improving Sound Event Detection In Domestic Environments Using Sound Separation

Author: Turpault, Nicolas, Wisdom, Scott, Erdogan, Hakan, Hershey, John, Serizel, Romain, Fonseca, Eduardo, Seetharaman, Prem, Salamon, Justin, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Google Inc., Music Technology Group (MTG), Universitat Pompeu Fabra [Barcelona] (UPF), Northwestern University [Evanston], Adobe Research, We would like to thank the other organizers of DCASE 2020 task 4: Daniel P. W. Ellis and Ankit Parag Shah., Grid'5000, ANR-18-CE23-0020,LEAUDS,Apprentissage statistique pour la compréhension de scènes audio(2018), Universitat Pompeu Fabra [Barcelona], and ANR-18-CE23-0020,LEAUDS,LEARNING TO UNDERSTAND AUDIO SCENES(2018)
Subjects: Signal Processing (eess.SP), FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Sound, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Synthetic soundscapes, Sound event detection, Audio and Speech Processing (eess.AS), TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Sound separation, Index Terms-Sound event detection, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or noise. Until now these problems were mainly tackled at the classifier level. We propose to use sound separation as a pre-processing for sound event detection. In this paper we start from a sound separation model trained on the Free Universal Sound Separation dataset and the DCASE 2020 task 4 sound event detection baseline. We explore different methods to combine separated sound sources and the original mixture within the sound event detection. Furthermore, we investigate the impact of adapting the sound separation model to the sound event detection data on both the sound separation and the sound event detection.
Published: 2020

19. Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes

Author: Romain Serizel, Hakan Erdogan, Justin Salamon, Nicolas Turpault, John R. Hershey, Scott Wisdom, Eduardo Fonseca, Prem Seetharaman, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Google Inc, Research at Google, Universitat Pompeu Fabra [Barcelona] (UPF), Descript, Inc., Adobe Research, Part of this work was made with the support of the French National Research Agency, in the framework of the project LEAUDS 'Learning to understand audio scenes' (ANR-18-CE23-0020) and the French region Grand-Est. High Performance Computing resources were partially provided by the EXPLOR centre hosted by the University de Lorraine., Grid'5000, ANR-18-CE23-0020,LEAUDS,Apprentissage statistique pour la compréhension de scènes audio(2018), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Sound localization, Sound (cs.SD), Reverberation, Soundscape, Computer science, Speech recognition, 02 engineering and technology, Computer Science - Sound, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), Robustness (computer science), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Sound (geography), synthetic soundscapes, geography, Signal processing, geography.geographical_feature_category, Event (computing), Sound event detection, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Benchmark (computing), sound separation, 020201 artificial intelligence & image processing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2021 task 4 depending on time related modifications (time position of an event and length of clips) and we study the impact of non-target sound events and reverberation. We show that the localization in time of sound events is still a problem for SED systems. We also show that reverberation and non-target sound events are severely degrading the performance of the SED systems. In the latter case, sound separation seems like a promising solution.
Published: 2020
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20. Autocomplete Animated Sculpting

Author: Mengqi Peng, Rubaiat Habib Kazi, Li-Yi Wei, Vladimir G. Kim, The University of Hong Kong (HKU), and Adobe Research
Subjects: Similarity (geometry), Autocomplete, Computer science, Interface (computing), 05 social sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, 02 engineering and technology, Animation, computer.software_genre, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], GeneralLiterature_MISCELLANEOUS, Motion (physics), Workflow, Scripting language, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], User interface, computer, ComputingMilieux_MISCELLANEOUS, 050107 human factors, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: Keyframe-based sculpting provides unprecedented freedom to author animated organic models, which can be difficult to create with other methods such as simulation, scripting, and rigging. However, sculpting animated objects can require significant artistic skill and manual labor, even more so than sculpting static 3D shapes or drawing 2D animations, which are already quite challenging. We present a keyframe-based animated sculpting system with the capability to autocomplete user editing under a simple and intuitive brushing interface. Similar to current desktop sculpting and VR brushing tools, users can brush surface details and volume structures. Meanwhile, our system analyzes their workflows and predicts what they might do in the future, both spatially and temporally. Users can accept or ignore these suggestions and thus maintain full control. We propose the first interactive suggestive keyframe sculpting system, specifically for spatio-temporal repetitive tasks, including low-level spatial details and high-level brushing structures across multiple frames. Our key ideas include a deformation-based optimization framework to analyze recorded workflows and synthesize predictions, and a semi-causal global similarity measurement to support flexible brushing stroke sequences and complex shape changes. Our system supports a variety of shape and motion styles, including those difficult to achieve via existing animation systems, such as topological changes that cannot be accomplished via simple rig-based deformations and stylized physically-implausible motions that cannot be simulated. We evaluate our system via a pilot user study that demonstrates the effectiveness of our system.
Published: 2020
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21. Autocomplete Element Fields

Author: Jian J. Zhang, Li-Yi Wei, Chen-Yuan Hsu, Lihua You, Bournemouth University [Poole] (BU), and Adobe Research
Subjects: synthesis, Autocomplete, Computer science, media_common.quotation_subject, Interface (computing), 02 engineering and technology, anisotropy, Field (computer science), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Quality (business), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], 050107 human factors, media_common, business.industry, 05 social sciences, Aggregate (data warehouse), element, 020207 software engineering, Usability, modeling, field, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], interface, business
Abstract: International audience; Aggregate elements are ubiquitous in natural and man-made objects. Interactively authoring these elements with varying anisotropy and deformability can require high artistic skills and manual labor. To reduce input workload and enhance output quality, we present an autocomplete system that can help users distribute and align such elements over different domains. Through a brushing interface, users can place and mix a few elements, and let our system automatically populate more elements for the remaining output. Furthermore, aggregate elements often require proper direction/scalar fields for proper arrangements, but fully specifying such fields across entire domains can be difficult or inconvenient for ordinary users. To address this usability challenge, we formulate "element fields" that can smoothly orient all the elements based on partial user specifications without requiring full input fields in any step.We validate our prototype system with a pilot user study and show applications in design, collage, and modeling.
Published: 2020
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22. Sound event detection in synthetic domestic environments

Author: Romain Serizel, Nicolas Turpault, Justin Salamon, Ankit Shah, Serizel, Romain, APPEL À PROJETS GÉNÉRIQUE 2018 - Apprentissage statistique pour la compréhension de scènes audio - - LEAUDS2018 - ANR-18-CE23-0020 - AAPG2018 - VALID, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Language Technologies Institute [Pittsburgh] (LTI), Carnegie Mellon University [Pittsburgh] (CMU), Adobe Research, Grid'5000, ANR-18-CE23-0020,LEAUDS,LEARNING TO UNDERSTAND AUDIO SCENES(2018), ANR-18-CE23-0020,LEAUDS,Apprentissage statistique pour la compréhension de scènes audio(2018), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: semi-supervised learning, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Soundscape, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 02 engineering and technology, 030204 cardiovascular system & hematology, Machine learning, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Impact model, 0202 electrical engineering, electronic engineering, information engineering, Index Terms-Sound event detection, business.industry, 020206 networking & telecommunications, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], synthetic data, Sound event detection, weakly labeled data, Artificial intelligence, business, computer
Abstract: International audience; We present a comparative analysis of the performance of state-of-the-art sound event detection systems. In particular, we study the robustness of the systems to noise and signal degradation, which is known to impact model generalization. Our analysis is based on the results of task 4 of the DCASE 2019 challenge, where submitted systems were evaluated on, in addition to real-world recordings, a series of synthetic soundscapes that allow us to carefully control for different soundscape characteristics. Our results show that while overall systems exhibit significant improvements compared to previous work, they still suffer from biases that could prevent them from generalizing to real-world scenarios.
Published: 2020

23. What's All the FUSS About Free Universal Sound Separation Data?

Author: Romain Serizel, Prem Seetharaman, Justin Salamon, Daniel P. W. Ellis, John R. Hershey, Scott Wisdom, Eduardo Fonseca, Nicolas Turpault, Hakan Erdogan, Google Inc, Research at Google, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Universitat Pompeu Fabra [Barcelona] (UPF), Adobe Research, Descript, Inc., and ANR-18-CE23-0020,LEAUDS,Apprentissage statistique pour la compréhension de scènes audio(2018)
Subjects: FOS: Computer and information sciences, Reverberation, Sound (cs.SD), open-source datasets, Computer science, Sound separation, Separation (aeronautics), 02 engineering and technology, Impulse (physics), Computer Science - Sound, Data modeling, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, Open domain, FOS: Electrical engineering, electronic engineering, information engineering, business.industry, Deep learning, deep learning, 020206 networking & telecommunications, Universal sound separation, variable source sep- aration, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 020201 artificial intelligence & image processing, Artificial intelligence, Variable number, business, Algorithm, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types. The dataset consists of 23 hours of single-source audio data drawn from 357 classes, which are used to create mixtures of one to four sources. To simulate reverberation, an acoustic room simulator is used to generate impulse responses of box shaped rooms with frequency-dependent reflective walls. Additional open-source data augmentation tools are also provided to produce new mixtures with different combinations of sources and room simulations. Finally, we introduce an open-source baseline separation model, based on an improved time-domain convolutional network (TDCN++), that can separate a variable number of sources in a mixture. This model achieves 9.8 dB of scale-invariant signal-to-noise ratio improvement (SI-SNRi) on mixtures with two to four sources, while reconstructing single-source inputs with 35.5 dB absolute SI-SNR. We hope this dataset will lower the barrier to new research and allow for fast iteration and application of novel techniques from other machine learning domains to the sound separation challenge.
Published: 2020
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24. Dynamic Adaptive Streaming for Augmented Reality Applications

Author: Gwendal Simon, Haoliang Wang, Stefano Petrangeli, Vishy Swaminathan, Universiteit Gent = Ghent University [Belgium] (UGENT), Adobe Research, Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Advanced technologies for operated networks (ADOPNET), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT), Adobe Systems Inc. (Adobe Advanced Technology Labs), Adobe Systems Inc., Universiteit Gent = Ghent University (UGENT), IMT Atlantique (IMT Atlantique), Université de Rennes (UR)-IMT Atlantique (IMT Atlantique), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Augmented Reality, AR Streaming, Computer science, business.industry, Digital content, Real-time computing, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], 02 engineering and technology, Video quality, Rate adaptation, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 020901 industrial engineering & automation, User experience design, 0202 electrical engineering, electronic engineering, information engineering, LOD Adaptation Heuristic, 020201 artificial intelligence & image processing, The Internet, Augmented reality, Latency (engineering), business, ComputingMilieux_MISCELLANEOUS
Abstract: Augmented Reality (AR) superimposes digital content on top of the real world, to enhance it and provide a new generation of media experiences. To provide a realistic AR experience, objects in the scene should be delivered with both high photorealism and low latency. Current AR experiences are mostly delivered with a download-and-play strategy, where the whole scene is considered a monolithic entity for delivery. This approach results in high start-up latencies and therefore a poor user experience. A similar problem in the video domain has already been tackled with the HTTP Adaptive Streaming (HAS) principle, where the video is split into segments, and a rate adaptation heuristic dynamically adapts the video quality based on the available network resources. In this paper, we apply the adaptive streaming principle from the video to the AR domain, and propose a streaming framework for AR applications. In our proposed framework, the AR objects are available at different Levels-Of-Detail (LODs) and can be streamed independently from each other. An LOD adaptation heuristic is in charge of dynamically deciding what object should be fetched from the server and at what LOD level. Our proposed heuristic prioritizes content that is more likely to be viewed by the user and selects the best LOD to maximize the object's perceived visual quality. Moreover, the adaptation takes into account the available bandwidth resources to ensure a timely delivery of the AR objects. Experiments carried out over the Internet using an AR streaming prototype developed on an iOS device allow us to show the gains brought by the proposed framework. Particularly, our approach can decrease start-up latency up to 90% with respect to a download-and-play baseline, and decrease the amount of data needed to deliver the AR experience up to 79%, without sacrificing on the visual quality of the AR objects.
Published: 2019
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25. HairBrush for Immersive Data-Driven Hair Modeling

Author: Li-Yi Wei, Haotian Xu, Weikai Chen, Koki Nagano, Byungmoon Kim, Jun Xing, Hao Li, Jingwan Lu, Yajie Zhao, USC Institute for Creative Technologies (USC-ICT), Pinscreen, Wayne State University [Detroit], and Adobe Research
Subjects: Computer science, Interface (computing), 02 engineering and technology, Virtual reality, 01 natural sciences, Data-driven, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Human–computer interaction, Component (UML), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMethodologies_COMPUTERGRAPHICS, Flexibility (engineering), Focus (computing), business.industry, 010401 analytical chemistry, hair, modeling, 020207 software engineering, Automation, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 0104 chemical sciences, user interface, machine learning, data-driven, virtual reality, User interface, business
Abstract: International audience; While hair is an essential component of virtual humans, it is also one of the most challenging digital assets to create. Existing automatic techniques lack the generality and flexibility to create rich hair variations, while manual authoring interfaces often require considerable artistic skills and efforts, especially for intricate 3D hair structures that can be difficult to navigate. We propose an interactive hair modeling system that can help create complex hairstyles in minutes or hours that would otherwise take much longer with existing tools. Modelers, including novice users, can focus on the overall hairstyles and local hair deformations, as our system intelligently suggests the desired hair parts. Our method combines the flexibility of manual authoring and the convenience of data-driven automation. Since hair contains intricate 3D structures such as buns, knots, and strands, they are inherently challenging to create using traditional 2D interfaces. Our system provides a new 3D hair author-ing interface for immersive interaction in virtual reality (VR). Users can draw high-level guide strips, from which our system predicts the most plausible hairstyles via a deep neural network trained from a professionally curated dataset. Each hairstyle in our dataset is composed of multiple variations, serving as blend-shapes to fit the user drawings via global blending and local deformation. The fitted hair models are visualized as interactive suggestions that the user can select, modify, or ignore. We conducted a user study to confirm that our system can significantly reduce manual labor while improve the output quality for modeling a variety of head and facial hairstyles that are challenging to create via existing techniques.
Published: 2019
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26. Sound event detection in domestic environments with weakly labeled data and soundscape synthesis

Author: Ankit Shah, Justin Salamon, Nicolas Turpault, Romain Serizel, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Language Technologies Institute [Pittsburgh] (LTI), Carnegie Mellon University [Pittsburgh] (CMU), Adobe Research, Grid'5000, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Soundscape, Synthetic data, Computer science, 02 engineering and technology, Semi-supervised learning, Machine learning, computer.software_genre, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030507 speech-language pathology & audiology, 03 medical and health sciences, Domestic environment, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Weakly labeled data, business.industry, 020206 networking & telecommunications, Sound event detection, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Labeled data, Artificial intelligence, 0305 other medical science, business, computer
Abstract: International audience; This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results. The task is a followup to Task 4 of DCASE 2018, and involves training systems for large-scale detection of sound events using a combination of weakly labeled data, i.e. training labels without time boundaries, and strongly-labeled synthesized data. The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that we’ll describe more in detail. We also report the performance of the submitted systems on the official evaluation (test) and development sets as well as several additional datasets. The best systems from this year outperform last year’s winning system by about 10% points in terms of F-measure.
Published: 2019

27. Line drawings from 3D models: a tutorial

Author: Pierre Bénard, Aaron Hertzmann, Melting the frontiers between Light, Shape and Matter (MANAO), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Photonique, Numérique et Nanosciences (LP2N), Université de Bordeaux (UB)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)-Centre National de la Recherche Scientifique (CNRS), Adobe Research, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest
Subjects: business.industry, Computer science, Visibility (geometry), Line drawings, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, 3d model, 02 engineering and technology, 3D rendering, Field (computer science), GeneralLiterature_MISCELLANEOUS, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Computer graphics (images), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Polygon mesh, Computer Vision and Pattern Recognition, Fast methods, business, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: International audience; This tutorial describes the geometry and algorithms for generating line drawings from 3D models, focusing on occluding contours. The geometry of occluding contours on meshes and on smooth surfaces is described in detail, together with algorithms for extracting contours, computing their visibility, and creating stylized renderings and animations. Exact methods and hardware-accelerated fast methods are both described, and the trade-offs between different methods are discussed. The tutorial brings together and organizes material that, at present, is scattered throughout the literature. It also includes some novel explanations, and implementation tips. A thorough survey of the field of non-photorealistic 3D rendering is also included, covering other kinds of line drawings and artistic shading.
Published: 2019
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28. Video Motion Stylization by 2D Rigidification

Author: Delanoy, Johanna, Bousseau, Adrien, Hertzmann, Aaron, GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Adobe Research
Subjects: Non, CCS Concepts, Computing methodologies → Non-photorealistic rendering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Motion processing, Motion processin, Computing methodologies, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], photorealistic rendering, ComputingMethodologies_COMPUTERGRAPHICS
Abstract: This paper introduces a video stylization method that increases the apparent rigidity of motion. Existing stylization methods often retain the 3D motion of the original video, making the result look like a 3D scene covered in paint rather than a 2D painting of a scene. In contrast, traditional hand-drawn animations often exhibit simplified in-plane motion, such as in the case of cut-out animations where the animator moves pieces of paper from frame to frame. Inspired by this technique, we propose to modify a video such that its content undergoes 2D rigid transforms. To achieve this goal, our approach applies motion segmentation and optimization to best approximate the input optical flow with piecewise-rigid transforms, and re-renders the video such that its content follows the simplified motion. The output of our method is a new video and its optical flow, which can be fed to any existing video stylization algorithm., ACM/EG Expressive Symposium, Art in Motion, 11, 19, Johanna Delanoy, Adrien Bousseau, and Aaron Hertzmann, CCS Concepts: Computing methodologies --> Non-photorealistic rendering; Motion processing
Published: 2019

29. Interactive Body-Driven Graphics for Augmented Video Performance

Author: Li-Yi Wei, Nazmus Saquib, Rubaiat Habib Kazi, Wilmot Li, Massachusetts Institute of Technology (MIT), and Adobe Research
Subjects: Computer science, media_common.quotation_subject, 05 social sciences, 020207 software engineering, 02 engineering and technology, User interface design, Variety (cybernetics), Craft, Presentation, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Graphics, Affordance, 050107 human factors, media_common, Gesture, Storytelling
Abstract: International audience; We present a system that augments live presentation videos with interactive graphics to create a powerful and expressive storytelling environment.Using our system, the presenter interacts with the graphical elements in real-time with gestures and postures, thus leveraging our innate, everyday skills to enhance our communication capabilities with the audience.However, crafting such an interactive and expressive performance typically requires programming, or highly-specialized tools tailored for experts.Our core contribution is a flexible, direct manipulation UI which enables amateurs and experts to craft such presentations beforehand by mapping a variety of body movements to a wide range of graphical manipulations.By simplifying the mapping between gestures, postures, and their corresponding output effects, our UI enables users to craft customized, rich interactions with the graphical elements.Our user study demonstrates the potential usage and unique affordance of this mixed-reality medium for storytelling and presentation across a range of application domains.
Published: 2019
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30. Evaluation of the Structural Phase Transition in Multiferroic (Bi1−x Prx)(Fe0.95 Mn0.05)O3 Thin Films by A Multi-Technique Approach Including Picosecond Laser Ultrasonics

Author: Sergey Avanesyan, Zheng Wen, Di Wu, Alexey M. Lomonosov, Elton de Lima Savi, Pascal Ruello, Anthony Rousseau, Qiao Jin, Alain Bulou, Samuel Raetz, Vitalyi Gusev, Maju Kuriakose, Nicolas Delorme, Nikolay Chigarev, Gwenaelle Vaudel, Equipe Ultrasons Lasers, Institut de Mécanique et d'Ingénierie de Bordeaux (I2M), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), A. M. Prokhorov General Physics Institute (GPI), Russian Academy of Sciences [Moscow] (RAS), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Institut des Molécules et Matériaux du Mans (IMMM), Le Mans Université (UM)-Centre National de la Recherche Scientifique (CNRS), Adobe Research, Unité de Dynamique et Structure des Matériaux Moléculaires (UDSMM), Université du Littoral Côte d'Opale, Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM), Institut de recherche en ingénierie moléculaire et matériaux fonctionnels de l'université du Maine (IRIMMFUM), Chinese Academy of Forestry, Laboratoire de physique de l'état condensé (LPEC), Fisk University, Le Mans Université (UM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Qingdao University of Science and Technology, National Laboratory of Solid State Microstructures [Nanjing University] (LSSMS), Nanjing University (NJU), École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-Institut Polytechnique de Bordeaux-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)
Subjects: Diffraction, Phase transition, picosecond laser ultrasonics, structural phase transition, Materials science, multiferroics, 02 engineering and technology, 01 natural sciences, lcsh:Technology, law.invention, [PHYS.PHYS.PHYS-ED-PH]Physics [physics]/Physics [physics]/Physics Education [physics.ed-ph], lcsh:Chemistry, symbols.namesake, law, Brillouin scattering, time-domain Brillouin scattering, 0103 physical sciences, General Materials Science, Thin film, 010306 general physics, Reflectometry, Instrumentation, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, Fluid Flow and Transfer Processes, non-destructive depth-profiling, business.industry, lcsh:T, Process Chemistry and Technology, General Engineering, [CHIM.MATE]Chemical Sciences/Material chemistry, 021001 nanoscience & nanotechnology, Laser, multi-technique approach, lcsh:QC1-999, [PHYS.PHYS.PHYS-GEN-PH]Physics [physics]/Physics [physics]/General Physics [physics.gen-ph], Computer Science Applications, [CHIM.POLY]Chemical Sciences/Polymers, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, symbols, Optoelectronics, 0210 nano-technology, business, Raman spectroscopy, lcsh:Engineering (General). Civil engineering (General), Ultrashort pulse, lcsh:Physics
Abstract: Picosecond laser ultrasonics is an experimental technique for the generation and detection of ultrashort acoustic pulses using ultrafast lasers. In transparent media, it is often referred to as time-domain Brillouin scattering (TDBS). It provides the opportunity to monitor the propagation of nanometers-length acoustic pulses and to determine acoustical, optical, and acousto-optical parameters of the materials. We report on the application of TDBS for evaluating the effect of Praseodymium (Pr) substitution on the elasticity of multiferroic (Bi1&minus, xPrx)(Fe0.95Mn0.05)O3 (BPFMO) thin films. The films were deposited on Si and LaAlO3 (LAO) substrates by a sol-gel method. X-ray diffraction and Raman spectra revealed earlier that a phase transition from rhombohedral to tetragonal structure occurs at about 15% Pr substitution and is accompanied by the maxima of remnant magnetization and polarization. Combining TDBS with optical spectral reflectometry, scanning electron microscopy, and topographic measurements by atomic force microscopy, we found that the structural transition is also characterized by the maximum optical dielectric constant and the minimum longitudinal sound velocity. Our results, together with earlier ones, suggest that BiFeO3-based films and ceramics with compositions near phase boundaries might be promising materials for multifunctional applications.
Published: 2019
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31. Multi-view Relighting using a Geometry-Aware Network

Author: Tinghui Zhou, Alexei A. Efros, Julien Philip, Michaël Gharbi, George Drettakis, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Adobe Research, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), European Project: 727188,H2020 Pilier Societal Challenges,H2020-SC6-CULT-COOP-2016,EMOTIVE(2016), and European Project: 788065,H2020 Pilier ERC,FUNGRAPH(2018)
Subjects: Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Geometry, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Rendering (computer graphics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Image relighting, Deep Learning, Shadow, 0202 electrical engineering, electronic engineering, information engineering, Specular reflection, ComputingMethodologies_COMPUTERGRAPHICS, Pixel, business.industry, Deep learning, 020207 software engineering, Multi-view, Computer Graphics and Computer-Aided Design, Pipeline (software), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Normal mapping, RGB color model, Artificial intelligence, business, CCS Concepts: • Computing methodologies → Image manipulation
Abstract: We propose the first learning-based algorithm that can relight images in a plausible and controllable manner given multiple views of an outdoor scene. In particular, we introduce a geometry-aware neural network that utilizes multiple geometry cues (normal maps, specular direction, etc.) and source and target shadow masks computed from a noisy proxy geometry obtained by multi-view stereo. Our model is a three-stage pipeline: two subnetworks refine the source and target shadow masks, and a third performs the final relighting. Furthermore, we introduce a novel representation for the shadow masks, which we call RGB shadow images. They reproject the colors from all views into the shadowed pixels and enable our network to cope with inacuraccies in the proxy and the non-locality of the shadow casting interactions. Acquiring large-scale multi-view relighting datasets for real scenes is challenging, so we train our network on photorealistic synthetic data. At train time, we also compute a noisy stereo-based geometric proxy, this time from the synthetic renderings. This allows us to bridge the gap between the real and synthetic domains. Our model generalizes well to real scenes. It can alter the illumination of drone footage, image-based renderings, textured mesh reconstructions, and even internet photo collections.
Published: 2019
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32. Scale-free adaptive planning for deterministic dynamics & discounted rewards

Author: Bartlett, Peter, Gabillon, Victor, Healey, Jennifer, Valko, Michal, Queensland University of Technology [Brisbane] (QUT), Huawei Noah's Ark Lab, Huawei Technologies [Shenzhen], Adobe Research, Sequential Learning (SEQUEL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), and Huawei Noah's Ark Lab [China]
Subjects: [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Abstract: International audience; We address the problem of planning in an environment with deterministic dynamics and stochas-tic discounted rewards under a limited numerical budget where the ranges of both rewards and noise are unknown. We introduce PlaTγPOOS, an adaptive, robust, and efficient alternative to the OLOP (open-loop optimistic planning) algorithm. Whereas OLOP requires a priori knowledge of the ranges of both rewards and noise, PlaTγPOOS dynamically adapts its behavior to both. This allows PlaTγPOOS to be immune to two vulnerabil-ities of OLOP: failure when given underestimated ranges of noise and rewards and inefficiency when these are overestimated. PlaTγPOOS additionally adapts to the global smoothness of the value function. PlaTγPOOS acts in a provably more efficient manner vs. OLOP when OLOP is given an overestimated reward and show that in the case of no noise, PlaTγPOOS learns exponentially faster.
Published: 2019

33. Localizing Moments in Video with Temporal Language

Author: Oliver Wang, Josef Sivic, Bryan Russell, Lisa Anne Hendricks, Trevor Darrell, Eli Shechtman, Adobe Research, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Models of visual object recognition and scene understanding (WILLOW), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC), Czech Technical University in Prague (CTU), Université Paris sciences et lettres (PSL), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: FOS: Computer and information sciences, Computer Science - Computation and Language, Computer science, Intersection (set theory), business.industry, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, Object (computer science), computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Task (project management), Moment (mathematics), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Artificial intelligence, business, Computation and Language (cs.CL), computer, Natural language processing, Natural language
Abstract: Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language)., EMNLP 2018
Published: 2018

34. Best of both worlds: Stochastic & adversarial best-arm identification

Author: Abbasi-Yadkori, Yasin, Bartlett, Peter, Gabillon, Victor, Malek, Alan, Valko, Michal, Adobe Research, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Queensland University of Technology [Brisbane] (QUT), Massachusetts Institute of Technology (MIT), Sequential Learning (SEQUEL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS), ANR-14-CE24-0010,ExTra-Learn,Extraction et transfert de connaissances dans l'apprentissage par renforcement(2014), ANR-16-CE23-0003,BoB,Inférence bayésienne à ressources limitées - données massives et modèles coûteux(2016), European Project: CHIST-ERA,DELTA, Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of California [Berkeley], Queensland University of Technology ( QUT ), Massachusetts Institute of Technology ( MIT ), Sequential Learning ( SEQUEL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Ecole Centrale de Lille-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), ANR-14-CE24-0010,ExTra-Learn,Extraction et transfert de connaissances dans l'apprentissage par renforcement ( 2014 ), and ANR-16-CE23-0003,BoB,Bayes on a Budget - big data and expensive models ( 2016 )
Subjects: Computer Science::Machine Learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], best-arm identification, multi-armed bandits, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], adversarial and stochastic rewards
Abstract: International audience; We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial scenario. However, this type of strategy is suboptimal when the rewards are sampled stochastically. Therefore, we ask: Can we design a learner that performs optimally in both the stochastic and adversarial problems while not being aware of the nature of the rewards? First, we show that designing such a learner is impossible in general. In particular, to be robust to adversarial rewards, we can only guarantee optimal rates of error on a subset of the stochastic problems. We give a lower bound that characterizes the optimal rate in stochastic problems if the strategy is constrained to be robust to adversarial rewards. Finally, we design a simple parameter-free algorithm and show that its probability of error matches (up to log factors) the lower bound in stochastic problems, and it is also robust to adversarial ones.
Published: 2018

35. Brushing Element Fields

Author: Lihua You, Chen-Yuan Hsu, Li-Yi Wei, Jian J. Zhang, Bournemouth University [Poole] (BU), and Adobe Research
Subjects: synthesis, Computer science, media_common.quotation_subject, Interface (computing), 02 engineering and technology, anisotropy, Field (computer science), Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Quality (business), Graphics, 050107 human factors, media_common, business.industry, 05 social sciences, Aggregate (data warehouse), element, 020207 software engineering, modeling, Graphic design, field, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Visualization, Variety (cybernetics), packing, interface, business
Abstract: Aggregate elements following certain directions have a variety\ud of applications in graphics, design, and visualization. However,\ud authoring oriented elements in various output domains, especially\ud in 3D, remains challenging. We propose a novel brushing system to\ud facilitate interactive authoring of aggregate elements with diverse\ud properties over given output domains via an element synthesis\ud approach. To increase output quality and reduce input workload,\ud we further propose element fields that can automatically orient\ud the entire elements in better alignments over the output domains\ud according to partially user-specified strokes. The proposed system\ud can effectively synthesize distinct types of elements within various\ud output domains in higher quality and efficiency and offer more user\ud friendliness than existing practices. Our method can be applied to\ud practical applications such as graphic design, artistic collage, and\ud aggregate modeling
Published: 2018
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36. Best of both worlds:Stochastic & adversarial best-arm identification

Author: Abbasi-Yadkori, Y. (Yasin), Bartlett, P. (Peter), Gabillon, V. (Victor), Malek, A. (Alan), Valko, M. (Michal), Adobe Research, Lawrence Berkeley National Laboratory [Berkeley] [LBNL], Queensland University of Technology [Brisbane] [QUT], Massachusetts Institute of Technology [MIT], and Sequential Learning [SEQUEL]
Subjects: multi-armed bandits, adversarial and stochastic rewards, best-arm identification
Published: 2018

37. Online influence maximization under independent cascade model with semi-bandit feedback

Author: Wen, Zheng, Kveton, Branislav, Valko, Michal, Vaswani, Sharan, Adobe Research, Sequential Learning (SEQUEL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), and ANR-16-CE23-0003,BoB,Inférence bayésienne à ressources limitées - données massives et modèles coûteux(2016)
Subjects: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Learning, Artificial Intelligence (cs.AI), [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Computer Science - Artificial Intelligence, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Computer Science - Social and Information Networks, Machine Learning (stat.ML), Mathematics - Optimization and Control, Machine Learning (cs.LG)
Abstract: We study the online influence maximization problem in social networks under the independent cascade model. Specifically, we aim to learn the set of "best influencers" in a social network online while repeatedly interacting with it. We address the challenges of (i) combinatorial action space, since the number of feasible influencer sets grows exponentially with the maximum number of influencers, and (ii) limited feedback, since only the influenced portion of the network is observed. Under a stochastic semi-bandit feedback, we propose and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our bounds on the cumulative regret are polynomial in all quantities of interest, achieve near-optimal dependence on the number of interactions and reflect the topology of the network and the activation probabilities of its edges, thereby giving insights on the problem complexity. To the best of our knowledge, these are the first such results. Our experiments show that in several representative graph topologies, the regret of IMLinUCB scales as suggested by our upper bounds. IMLinUCB permits linear generalization and thus is both statistically and computationally suitable for large-scale problems. Our experiments also show that IMLinUCB with linear generalization can lead to low regret in real-world online influence maximization., Comment: Compared with the previous version, this version has fixed a mistake. This version is also consistent with the NIPS camera-ready version
Published: 2017

38. Localizing Moments in Video with Natural Language

Author: Hendricks, Lisa Anne, Wang, Oliver, Shechtman, Eli, Sivic, Josef, Darrell, Trevor, Russell, Bryan, Adobe Research, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Models of visual object recognition and scene understanding (WILLOW), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL), Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC), Czech Technical University in Prague (CTU), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Abstract: We consider retrieving a specific temporal segment, or moment, from a video given a natural language text description. Methods designed to retrieve whole video clips with natural language determine what occurs in a video but not when. To address this issue, we propose the Moment Context Network (MCN) which effectively localizes natural language queries in videos by integrating local and global video features over time. A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment. Therefore, we collect the Distinct Describable Moments (DiDeMo) dataset which consists of over 10,000 unedited, personal videos in diverse visual settings with pairs of localized video segments and referring expressions. We demonstrate that MCN outperforms several baseline methods and believe that our initial results together with the release of DiDeMo will inspire further research on localizing video moments with natural language., Comment: ICCV 2017
Published: 2017

39. ActionVLAD: Learning spatio-temporal aggregation for action classification

Author: Girdhar, Rohit, Ramanan, Deva, Gupta, Abhinav, Sivic, Josef, Russell, Bryan, Carnegie Mellon University [Pittsburgh] (CMU), Adobe Research, Université Paris sciences et lettres (PSL), Models of visual object recognition and scene understanding (WILLOW), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Czech Institute of Informatics, Robotics and Cybernetics [Prague] (CIIRC), Czech Technical University in Prague (CTU), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Abstract: In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks with learnable spatio-temporal feature aggregation. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as out-performs other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks., Accepted to CVPR 2017. Project page: https://rohitgirdhar.github.io/ActionVLAD/
Published: 2017

40. Deep exemplar 2d-3d detection by adapting from real to rendered views

Author: Mathieu Aubry, Bryan Russell, Francisco Massa, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), École des Ponts ParisTech (ENPC), imagine [Marne-la-Vallée], Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre Scientifique et Technique du Bâtiment (CSTB), Adobe Research, Department of Electrical Engineering and Computer Sciences (Berkeley EECS), Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Massachusetts Institute of Technology (MIT), Centre Scientifique et Technique du Bâtiment (CSTB)-École des Ponts ParisTech (ENPC)-Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-Université Paris-Est Marne-la-Vallée (UPEM), and Aubry, Mathieu
Subjects: FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Feature extraction, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Solid modeling, 010501 environmental sciences, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 01 natural sciences, Convolutional neural network, Machine Learning (cs.LG), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Neural and Evolutionary Computing (cs.NE), 0105 earth and related environmental sciences, computer.programming_language, business.industry, Deep learning, Computer Science - Neural and Evolutionary Computing, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Pascal (programming language), Object detection, Computer Science - Learning, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract: This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset, and object category detection, where we out-perform Aubry et al. for "chair" detection on a subset of the Pascal VOC dataset., To appear in CVPR 2016
Published: 2016
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41. Proceedings of the 1st Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016) co-located with the Joint Conference on Digital Libraries 2016 (JCDL 2016)

Author: Cabanac, Guillaume, Chandrasekaran, Muthu Kumar, Frommholz, Ingo, Jaidka, Kokil, Kan, Min-Yen, Mayr, Philipp, Wolfram, Dietmar, Grélaud, Françoise, Cabanac, Guillaume, Chandrasekaran, Muthu Kumar, Frommholz, Ingo, Jaidka, Kokil, Kan, Min-Yen, Mayr, Philipp, Wolfram, Dietmar, Recherche d’Information et Synthèse d’Information (IRIT-IRIS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), National University of Singapore (NUS), University of Bedfordshire, Adobe Research, Leibniz-Institute for the Social Sciences [Mannheim] (GESIS ), University of Wisconsin - Milwaukee, and ACL : Association for Computational Linguistics
Subjects: [INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract: International audience; no abstract
Published: 2016

42. Reducing simulator sickness with perceptual camera control

Author: Arie E. Kaufman, Li-Yi Wei, Qi Sun, Piotr Didyk, Ping Hu, Department of Computer Science [Stonybrook - NY], Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Adobe Research, Università della Svizzera italiana = University of Italian Switzerland (USI), and Wei, Li-Yi
Subjects: Computer science, media_common.quotation_subject, [INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Fidelity, Optical head-mounted display, 02 engineering and technology, Virtual reality, computer.software_genre, Motion (physics), human perception ACM Reference Format, Perception, Human-centered computing → Virtual reality, 0202 electrical engineering, electronic engineering, information engineering, Immersion (virtual reality), Computer vision, Motion planning, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], media_common, Vestibular system, business.industry, 020207 software engineering, Computer Graphics and Computer-Aided Design, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Com- puting methodologies → Perception Additional Key Words and Phrases: virtual reality, head-mounted display, Virtual machine, Simulator sickness, Artificial intelligence, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer
Abstract: Virtual-reality provides an immersive environment but can induce cybersickness due to the discrepancy between visual and vestibular cues. To avoid this problem, the movement of the virtual camera needs to match the motion of the user in the real world. Unfortunately, this is usually difficult due to the mismatch between the size of the virtual environments and the space available to the users in the physical domain. The resulting constraints on the camera movement significantly hamper the adoption of virtual-reality headsets in many scenarios and make the design of the virtual environments very challenging. In this work, we study how the characteristics of the virtual camera movement (e.g., translational acceleration and rotational velocity) and the composition of the virtual environment (e.g., scene depth) contribute to perceived discomfort. Based on the results from our user experiments, we devise a computational model for predicting the magnitude of the discomfort for a given scene and camera trajectory. We further apply our model to a new path planning method which optimizes the input motion trajectory to reduce perceptual sickness. We evaluate the effectiveness of our method in improving perceptual comfort in a series of user studies targeting different applications. The results indicate that our method can reduce the perceived discomfort while maintaining the fidelity of the original navigation, and perform better than simpler alternatives.
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43. Benchmark suites instead of leaderboards for evaluating AI fairness.

Author: Wang A, Hertzmann A, and Russakovsky O
Abstract: Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial intelligence (AI) models. Many critics argue against this practice, since it incentivizes optimizing for metrics in an attempt to build the "most fair" AI model. However, this is an inherently impossible task since different applications have different considerations. While we agree with the critiques against leaderboards, we believe that the use of benchmarks can be reformed. Thus far, the critiques of leaderboards and benchmarks have become unhelpfully entangled. However, benchmarks, when not used for leaderboards, offer important tools for understanding a model. We advocate for collecting benchmarks into carefully curated "benchmark suites," which can provide researchers and practitioners with tools for understanding the wide range of potential harms and trade-offs among different aspects of fairness. We describe the research needed to build these benchmark suites so that they can better assess different usage modalities, cover potential harms, and reflect diverse perspectives. By moving away from leaderboards and instead thoughtfully designing and compiling benchmark suites, we can better monitor and improve the fairness impacts of AI technology., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)
Published: 2024
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44. Toward a theory of perspective perception in pictures.

Author: Hertzmann A
Subjects: Humans, Form Perception physiology, Depth Perception physiology, Space Perception physiology, Eye Movements physiology, Fovea Centralis physiology, Fixation, Ocular physiology
Abstract: This paper reviews projection models and their perception in realistic pictures, and proposes hypotheses for three-dimensional (3D) shape and space perception in pictures. In these hypotheses, eye fixations, and foveal vision play a central role. Many past theories and experimental studies focus solely on linear perspective. Yet, these theories fail to explain many important perceptual phenomena, including the effectiveness of nonlinear projections. Indeed, few classical paintings strictly obey linear perspective, nor do the best distortion-avoidance techniques for wide-angle computational photography. The hypotheses here employ a two-stage model for 3D human vision. When viewing a picture, the first stage perceives 3D shape for the current gaze. Each fixation has its own perspective projection, but, owing to the nature of foveal and peripheral vision, shape information is obtained primarily for a small region of the picture around the fixation. As a viewer moves their eyes, the second stage continually integrates some of the per-gaze information into an overall interpretation of a picture. The interpretation need not be geometrically stable or consistent over time. It is argued that this framework could explain many disparate pictorial phenomena, including different projection styles throughout art history and computational photography, while being consistent with the constraints of human 3D vision. The paper reviews open questions and suggests new studies to explore these hypotheses.
Published: 2024
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45. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison.

Author: Tang VH, Duong STM, Nguyen CDT, Huynh TM, Duc VT, Phan C, Le H, Bui T, and Truong SQH
Subjects: Humans, Bayes Theorem, Tomography, X-Ray Computed, Prognosis, Retrospective Studies, Carcinoma, Hepatocellular diagnostic imaging, Carcinoma, Hepatocellular pathology, Liver Neoplasms pathology
Abstract: Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve., (© 2023. The Author(s).)
Published: 2023
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46. Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping.

Author: Ichien N, Liu Q, Fu S, Holyoak KJ, Yuille AL, and Lu H
Subjects: Humans, Knowledge, Problem Solving, Artificial Intelligence, Intelligence
Abstract: Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task-specific knowledge acquired from a wealth of prior experience, or is it based on the domain-general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three-dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task-specific knowledge to analogical reasoning. We also developed a new model using part-based comparison (PCM) by applying a domain-general mapping procedure to learned representations of cars and their component parts. Across four-term analogies (Experiment 1) and open-ended analogies (Experiment 2), the domain-general PCM model, but not the task-specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human-like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity., (© 2023 Cognitive Science Society LLC.)
Published: 2023
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47. The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance.

Author: Reichman D, Lieder F, Bourgin DD, Talmon N, and Griffiths TL
Subjects: Humans, Problem Solving, Goals, Algorithms
Abstract: We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree-like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals., (© 2023 Cognitive Science Society LLC.)
Published: 2023
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48. Art and the science of generative AI.

Author: Epstein Z, Hertzmann A, Akten M, Farid H, Fjeld J, Frank MR, Groh M, Herman L, Leach N, Mahari R, Pentland AS, Russakovsky O, Schroeder H, and Smith A
Abstract: Understanding shifts in creative work will help guide AI's impact on the media ecosystem.
Published: 2023
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49. Universal Causality.

Author: Mahadevan S
Abstract: Universal Causality is a mathematical framework based on higher-order category theory, which generalizes previous approaches based on directed graphs and regular categories. We present a hierarchical framework called UCLA (Universal Causality Layered Architecture), where at the top-most level, causal interventions are modeled as a higher-order category over simplicial sets and objects. Simplicial sets are contravariant functors from the category of ordinal numbers Δ into sets, and whose morphisms are order-preserving injections and surjections over finite ordered sets. Non-random interventions on causal structures are modeled as face operators that map n -simplices into lower-level simplices. At the second layer, causal models are defined as a category, for example defining the schema of a relational causal model or a symmetric monoidal category representation of DAG models. The third layer corresponds to the data layer in causal inference, where each causal object is mapped functorially into a set of instances using the category of sets and functions between sets. The fourth homotopy layer defines ways of abstractly characterizing causal models in terms of homotopy colimits, defined in terms of the nerve of a category, a functor that converts a causal (category) model into a simplicial object. Each functor between layers is characterized by a universal arrow, which define universal elements and representations through the Yoneda Lemma, and induces a Grothendieck category of elements that enables combining formal causal models with data instances, and is related to the notion of ground graphs in relational causal models. Causal inference between layers is defined as a lifting problem, a commutative diagram whose objects are categories, and whose morphisms are functors that are characterized as different types of fibrations. We illustrate UCLA using a variety of representations, including causal relational models, symmetric monoidal categorical variants of DAG models, and non-graphical representations, such as integer-valued multisets and separoids, and measure-theoretic and topological models.
Published: 2023
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50. GPU-accelerated and pipelined methylation calling.

Author: Feng Y, Gudukbay Akbulut G, Tang X, Gunasekaran JR, Rahman A, Medvedev P, and Kandemir M
Abstract: Motivation: The third-generation DNA sequencing technologies, such as Nanopore Sequencing, can operate at very high speeds and produce longer reads, which in turn results in a challenge for the computational analysis of such massive data. Nanopolish is a software package for signal-level analysis of Oxford Nanopore sequencing data. Call-methylation module of Nanopolish can detect methylation based on Hidden Markov Model (HMM). However, Nanopolish is limited by the long running time of some serial and computationally expensive processes. Among these, Adaptive Banded Event Alignment (ABEA) is the most time-consuming step, and the prior work, f5c , has already parallelized and optimized ABEA on GPU. As a result, the remaining methylation score calculation part, which uses HMM to identify if a given base is methylated or not, has become the new performance bottleneck., Results: This article focuses on the call-methylation module that resides in the Nanopolish package. We propose Galaxy-methyl , which parallelizes and optimizes the methylation score calculation step on GPU and then pipelines the four steps of the call-methylation module. Galaxy-methyl increases the execution concurrency across CPUs and GPUs as well as hardware resource utilization for both. The experimental results collected indicate that Galaxy-methyl can achieve 3×-5× speedup compared with Nanopolish , and reduce the total execution time by 35% compared with f5c , on average., Availability and Implementation: The source code of Galaxy-methyl is available at https://github.com/fengyilin118/., (© The Author(s) 2022. Published by Oxford University Press.)
Published: 2022
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