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Start Over Topic computer vision and pattern recognition (cs.cv)
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1. {3D} Morphable Face Models -- Past, Present and Future

Author

Sami Romdhani, Michael Zollhoefer, William A. P. Smith, Volker Blanz, Adam Kortylewski, Thabo Beeler, Thomas Vetter, Florian Bernard, Christian Theobalt, Bernhard Egger, Ayush Tewari, Timo Bolkart, Stefanie Wuhrer, Massachusetts Institute of Technology (MIT), University of York [York, UK], Max Planck Institute for Informatics [Saarbrücken], Capture and Analysis of Shapes in Motion (MORPHEO), 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), Stanford University, Disney Research, Johns Hopkins University (JHU), Idemia, Universität Siegen [Siegen], University of Basel (Unibas), This survey paper was initiated at the Dagstuhl Seminar 19102 on 3D Morphable Models [Egger et al. 2019] and contains ideas resulting from discussions at this seminar. This survey paper was partially funded by Early PostDoc Mobility Grant, Swiss National Science Foundation P2BSP2_178643, ERC Consolidator Grant 4DRepLy and the Max Planck Center for Visual Computing and Communications (MPC-VCC). We thank Barış Geçer for his help on the teaser figure, and Haiwen Feng for providing the FLAME texture space. We thank the anonymous reviewers whose comments have greatly improved this manuscript., Capture and Analysis of Shapes in Motion [2016-2019] (MORPHEO [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann [2016-2019] (LJK [2016-2019]), Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Stanford University [Stanford], and University of Siegen

Subjects
FOS: Computer and information sciences, Image formation, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), Analysis-by-Synthesis, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Statistical Modelling, Machine Learning (cs.LG), Computer graphics, Computer Science - Graphics, 0202 electrical engineering, electronic engineering, information engineering, Computer Graphics, Generative Models, Graphics, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Computer Graphics and Computer-Aided Design, Data science, Graphics (cs.GR), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 3D Computer Vision, 13. Climate action, Face (geometry), 020201 artificial intelligence & image processing
Abstract

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications., ACM Transactions on Graphics (TOG)

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2. Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations

Author

Michèle Sebag, Cédric Gouy-Pailler, Yoann Isaac, Quentin Barthélemy, Jamal Atif, Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, TAckling the Underspecified (TAU), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mensia Technologies [Rennes], Mensia Technologies [Paris], Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), The work presented in this paper has been partially funded by DIGITEO under the Grant 2011-053D, Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Signal processing, Optimization problem, Noise reduction, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Machine Learning (cs.LG), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Recovery, Computer Science - Data Structures and Algorithms, Prior probability, Regularization, 0202 electrical engineering, electronic engineering, information engineering, Data Structures and Algorithms (cs.DS), Electrical and Electronic Engineering, Selection, Image restoration, Mathematics, business.industry, Linear Inverse Problems, 020206 networking & telecommunications, Pattern recognition, Sparse approximation, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Regression, Minimization, Computer Science - Learning, Image-Restoration, Control and Systems Engineering, Statistical analysis, 020201 artificial intelligence & image processing, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Computer Vision and Pattern Recognition, Decomposition method (constraint satisfaction), Artificial intelligence, Minification, Lasso, business, Software, Algorithms, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

This paper addresses the structurally constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of the paper is threefold. Firstly, a generic spatio-temporal regularization term is designed and used together with the standard ź 1 regularization term to enforce a sparse decomposition preserving the spatio-temporal structure of the signal. Secondly, an optimization algorithm based on the split Bregman approach is proposed to handle the associated optimization problem, and its convergence is analyzed. Our well-founded approach yields same accuracy as the other algorithms at the state of the art, with significant gains in terms of convergence speed. Thirdly, the empirical validation of the approach on artificial and real-world problems demonstrates the generality and effectiveness of the method. On artificial problems, the proposed regularization subsumes the Total Variation minimization and recovers the expected decomposition. On the real-world problem of electro-encephalography brainwave decomposition, the approach outperforms similar approaches in terms of P300 evoked potentials detection, using structured spatial priors to guide the decomposition. HighlightsA sparse structured decomposition method is proposed for multi-dimensional signals.Knowledge priors are encoded in a regularization to obtain plausible representations.The proposed split-Bregman based method outperforms counterparts in terms of speed.The approach is applied to EEG denoising for the extraction of P300 potentials.

Published
2017