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Mixed-reality (MR) soundscapes blend real-world sound with virtual audio from hearing devices, presenting intricate auditory information that is hard to discern and differentiate. This is particularly challenging for blind or visually impaired individuals, who rely on sounds and descriptions in their everyday lives. To understand how complex audio information is consumed, we analyzed online forum posts within the blind community, identifying prevailing challenges, needs, and desired solutions. We synthesized the results and propose SoundShift for increasing MR sound awareness, which includes six sound manipulations: Transparency Shift, Envelope Shift, Position Shift, Style Shift, Time Shift, and Sound Append. To evaluate the effectiveness of SoundShift, we conducted a user study with 18 blind participants across three simulated MR scenarios, where participants identified specific sounds within intricate soundscapes. We found that SoundShift increased MR sound awareness and minimized cognitive load. Finally, we developed three real-world example applications to demonstrate the practicality of SoundShift., Comment: DIS 2024

Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human-in-the-loop framework that allows our target users, i.e., novice and professional UI designers, to improve the usability of interface icons efficiently. We formulate several usability criteria into a perceptual usability function and enable users to iteratively revise an icon set with an interactive design tool, EvIcon. We take a large-scale pre-trained joint image-text embedding (CLIP) and fine-tune it to embed icon visuals with icon tags in the same embedding space (IconCLIP). During the revision process, our design tool provides two types of instant perceptual usability feedback. First, we provide perceptual usability feedback modeled by deep learning models trained on IconCLIP embeddings and crowdsourced perceptual ratings. Second, we use the embedding space of IconCLIP to assist users in improving icons' visual distinguishability among icons within the user-prepared icon set. To provide the perceptual prediction, we compiled IconCEPT10K, the first large-scale dataset of perceptual usability ratings over $10,000$ interface icons, by conducting a crowdsourcing study. We demonstrated that our framework could benefit UI designers' interface icon revision process with a wide range of professional experience. Moreover, the interface icons designed using our framework achieved better semantic distance and familiarity, verified by an additional online user study.

Though Neural Radiance Field (NeRF) demonstrates compelling novel view synthesis results, it is still unintuitive to edit a pre-trained NeRF because the neural network's parameters and the scene geometry/appearance are often not explicitly associated. In this paper, we introduce the first framework that enables users to remove unwanted objects or retouch undesired regions in a 3D scene represented by a pre-trained NeRF without any category-specific data and training. The user first draws a free-form mask to specify a region containing unwanted objects over a rendered view from the pre-trained NeRF. Our framework first transfers the user-provided mask to other rendered views and estimates guiding color and depth images within these transferred masked regions. Next, we formulate an optimization problem that jointly inpaints the image content in all masked regions across multiple views by updating the NeRF model's parameters. We demonstrate our framework on diverse scenes and show it obtained visual plausible and structurally consistent results across multiple views using shorter time and less user manual efforts., Comment: Hao-Kang Liu and I-Chao Shen contributed equally to the paper. Project page: https://jdily.github.io/proj_site/nerfin_proj.html

Due to a lack of image-based "part controllers", shape manipulation of man-made shape images, such as resizing the backrest of a chair or replacing a cup handle is not intuitive. To tackle this problem, we present StylePart, a framework that enables direct shape manipulation of an image by leveraging generative models of both images and 3D shapes. Our key contribution is a shape-consistent latent mapping function that connects the image generative latent space and the 3D man-made shape attribute latent space. Our method "forwardly maps" the image content to its corresponding 3D shape attributes, where the shape part can be easily manipulated. The attribute codes of the manipulated 3D shape are then "backwardly mapped" to the image latent code to obtain the final manipulated image. We demonstrate our approach through various manipulation tasks, including part replacement, part resizing, and viewpoint manipulation, and evaluate its effectiveness through extensive ablation studies., Comment: 10 pages, Project page: https://jdily.github.io/proj_site/stylepart_proj.html

This paper presents a novel deep learning-based approach for automatically vectorizing and synthesizing the clipart of man-made objects. Given a raster clipart image and its corresponding object category (e.g., airplanes), the proposed method sequentially generates new layers, each of which is composed of a new closed path filled with a single color. The final result is obtained by compositing all layers together into a vector clipart image that falls into the target category. The proposed approach is based on an iterative generative model that (i) decides whether to continue synthesizing a new layer and (ii) determines the geometry and appearance of the new layer. We formulated a joint loss function for training our generative model, including the shape similarity, symmetry, and local curve smoothness losses, as well as vector graphics rendering accuracy loss for synthesizing clipart recognizable by humans. We also introduced a collection of man-made object clipart, ClipNet, which is composed of closed-path layers, and two designed preprocessing tasks to clean up and enrich the original raw clipart. To validate the proposed approach, we conducted several experiments and demonstrated its ability to vectorize and synthesize various clipart categories. We envision that our generative model can facilitate efficient and intuitive clipart designs for novice users and graphic designers., Comment: 15 pages, TVCG2021

We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists' creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3Dshape that matches the user's expectation in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user-assisted curve extrusion method to obtain the reference 3D shape.We render the synthesized reference 3D shape with consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that the users are able to design clipart from different viewpoints while preserving the original geometric style without losing its original shape., Comment: 12 pages, accepted in computer graphics forum (CGF)

In recent years, personalized fabrication has received considerable attention because of the widespread use of consumer-level three-dimensional (3D) printers. However, such 3D printers have drawbacks, such as long production time and limited output size, which hinder large-scale rapid-prototyping. In this paper, for the time- and cost-effective fabrication of large-scale objects, we propose a hybrid 3D fabrication method that combines 3D printing and the Zometool construction set, which is a compact, sturdy, and reusable structure for infill fabrication. The proposed method significantly reduces fabrication cost and time by printing only thin 3D outer shells. In addition, we design an optimization framework to generate both a Zometool structure and printed surface partitions by optimizing several criteria, including printability, material cost, and Zometool structure complexity. Moreover, we demonstrate the effectiveness of the proposed method by fabricating various large-scale 3D models., Comment: 10 pages

This paper proposes a robust localization system that employs deep learning for better scene representation, and enhances the accuracy of 6-DOF camera pose estimation. Inspired by the fact that global scene structure can be revealed by wide field-of-view, we leverage the large overlap of a fisheye camera between adjacent frames, and the powerful high-level feature representations of deep learning. Our main contribution is the novel network architecture that extracts both temporal and spatial information using a Recurrent Neural Network. Specifically, we propose a novel pose regularization term combined with LSTM. This leads to smoother pose estimation, especially for large outdoor scenery. Promising experimental results on three benchmark datasets manifest the effectiveness of the proposed approach.

Using (casual) images to texture 3D models is a common way to create realistic 3D models, which is a very important task in computer graphics. However, if the shape of the casual image does not look like the target model or the target mapping area, the textured model will become strange since the image will be distorted very much. In this paper, we present a novel texturing and deforming approach for mapping the pattern and shape of a casual image to a 3D model at the same time based on an alternating least-square approach. Through a photogrammetric method, we project the target model onto the source image according to the estimated camera model. Then, the target model is deformed according to the shape of the source image using a surface-based deformation method while minimizing the image distortion simultaneously. The processes are performed iteratively until convergence. Hence, our method can achieve texture mapping, shape deformation, and detail-preserving at once, and can obtain more reasonable texture mapped results than traditional methods., Comment: 6 pages. Longer version of SIGGRAPH ASIA 2012 poster

In the past few years, deep reinforcement learning has been proven to solve problems which have complex states like video games or board games. The next step of intelligent agents would be able to generalize between tasks, and using prior experience to pick up new skills more quickly. However, most reinforcement learning algorithms for now are often suffering from catastrophic forgetting even when facing a very similar target task. Our approach enables the agents to generalize knowledge from a single source task, and boost the learning progress with a semisupervised learning method when facing a new task. We evaluate this approach on Atari games, which is a popular reinforcement learning benchmark, and show that it outperforms common baselines based on pre-training and fine-tuning., Comment: 13 pages

Automatic photo cropping is an important tool for improving visual quality of digital photos without resorting to tedious manual selection. Traditionally, photo cropping is accomplished by determining the best proposal window through visual quality assessment or saliency detection. In essence, the performance of an image cropper highly depends on the ability to correctly rank a number of visually similar proposal windows. Despite the ranking nature of automatic photo cropping, little attention has been paid to learning-to-rank algorithms in tackling such a problem. In this work, we conduct an extensive study on traditional approaches as well as ranking-based croppers trained on various image features. In addition, a new dataset consisting of high quality cropping and pairwise ranking annotations is presented to evaluate the performance of various baselines. The experimental results on the new dataset provide useful insights into the design of better photo cropping algorithms., Comment: The dataset presented in this article can be found on Github

With the aim to improve the performance of feature matching, we present an unsupervised approach to fuse various local descriptors in the space of homographies. Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can select a good descriptor for matching each feature point. Specifically, the homography space serves as the common domain, in which a correspondence obtained by any descriptor is considered as a point, for integrating various heterogeneous descriptors. Both geometric coherence and spatial continuity among correspondences are considered via computing their geodesic distances in the space. In this way, mutual verification across different descriptors is allowed, and correct correspondences will be highlighted with a high degree of consistency (i.e., short geodesic distances here). It follows that one-class SVM can be applied to identifying these correct correspondences, and boosts the performance of feature matching. The proposed approach is comprehensively compared with the state-of-the-art approaches, and evaluated on four benchmarks of image matching. The promising results manifest its effectiveness.