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Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering

Envisioning a Next Generation Extended Reality Conferencing ...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Shen, Chuanyue Zhang, Letian Yang, Zhangsihao Mortazavi, Masood Song, Xiyun Peng, Liang Yu, Heather University of Illinois at Urbana-Champaign United States University of Miami United States Arizona State University United States Futurewei Technologies United States

ISBN: (纸本)9798350302493

Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world. © 2023 IEEE.

关键词： Three dimensional computer graphics

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Certified Adversarial Robustness Within Multiple Perturbation Bounds

Certified Adversarial Robustness Within Multiple Perturbatio...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Nandi, Soumalya Addepalli, Sravanti Rangwani, Harsh Babu, R. Venkatesh Indian Institute of Science Vision and AI Lab Bengaluru India

ISBN: (纸本)9798350302493

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to 2 norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other p norm bounds as well. In general, a specific noise distribution is optimal for defending against a given p norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel certification scheme, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel training noise distribution along with a regularized training scheme to improve the certification within both 1 and 2 perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both 1 and 2 perturbation bounds. Code available at https://***/valiisc/NU-Certified-Robustness © 2023 IEEE.

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3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intu...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Xue, Haotian Torralba, Antonio Tenenbaum, Joshua Yamins, Daniel Li, Yunzhu Tung, Hsiao-Yu Georgia Tech United States Mit United States Stanford Univeristy United States

ISBN: (纸本)9798350302493

Given a visual scene, humans have strong intuitions about how a scene can evolve over time under given actions. The intuition, often termed visual intuitive physics, is a critical ability that allows us to make effective plans to manipulate the scene to achieve desired outcomes without relying on extensive trial and error. In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids. Our method is composed of a conditional Neural Radiance Field (NeRF)-style visual frontend and a 3D point-based dynamics prediction backend, using which we can impose strong relational and structural inductive bias to capture the structure of the underlying environment. Unlike existing intuitive point-based dynamics works that rely on the supervision of dense point trajectory from simulators, we relax the requirements and only assume access to multi-view RGB images and (imperfect) instance masks acquired using color prior. This enables the proposed model to handle scenarios where accurate point estimation and tracking are hard or impossible. We generate datasets including three challenging scenarios involving fluid, granular materials, and rigid objects in the simulation. The datasets do not include any dense particle information so most previous 3D-based intuitive physics pipelines can barely deal with that. We show our model can make long-horizon future predictions by learning from raw images and significantly outperforms models that do not employ an explicit 3D representation space. We also show that once trained, our model can achieve strong generalization in complex scenarios under extrapolate settings. © 2023 IEEE.

关键词： computer vision

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NTIRE 2023 Video Colorization Challenge

NTIRE 2023 Video Colorization Challenge

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Kang, Xiaoyang Lin, Xianhui Zhang, Kai Hui, Zheng Xiang, Wangmeng He, Jun-Yan Li, Xiaoming Ren, Peiran Xie, Xuansong Timofte, Radu Pan, Jinshan Peng, Zhongzheng Zhang, Qiyan Dong, Jiangxin Tang, Jinhui Li, Jinjing Lin, Chichen Li, Qipei Liang, Qirong Gang, Ruipeng Liu, Xiaofeng Feng, Shuang Liu, Shuai Wang, Hao Feng, Chaoyu Bai, Furui Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Chen, Siqi Zhang, Yu Xu, Hanning Liu, Zheyuan Zhang, Zhao Luo, Yan Zuo, Zhichao Damo Academy Alibaba Group China Computer Vision Lab Eth Zürich Switzerland Nanyang Technological University Singapore University of Würzburg Germany Nanjing University of Science and Technology China Communication University of China Beijing100024 China Academy of Broadcasting Science Nrta Beijing100866 China Uhdtv Research and Application Laboratory Beijing100176 China Xiaomi Inc. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Zjut China China

ISBN: (纸本)9798350302493

This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url. © 2023 IEEE.

关键词： Image enhancement

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Mcmaster University Department of Electrical and Computer Engineering Hamilton Canada University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350302493

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC. © 2023 IEEE.

关键词： Convolution

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Density Map Distillation for Incremental Object Counting

Density Map Distillation for Incremental Object Counting

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wu, Chenshen Van De Weijer, Joost Computer Vision Center Barcelona Spain

ISBN: (纸本)9798350302493

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. © 2023 IEEE.

关键词： Distillation

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BeautyREC: Robust, Efficient, and Component-Specific Makeup Transfer

BeautyREC: Robust, Efficient, and Component-Specific Makeup ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Yan, Qixin Guo, Chunle Zhao, Jixin Dai, Yuekun Loy, Chen Change Li, Chongyi Tencent WeChat China Nankai University TMCC CS China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global attention, simply concatenate features, or implicitly manipulate features in latent space, we propose a component-specific correspondence to directly transfer the makeup style of a reference image to the corresponding components (e.g., skin, lips, eyes) of a source image, making elaborate and accurate local makeup transfer. As an auxiliary, the long-range visual dependencies of Transformer are introduced for effective global makeup transfer. Instead of the commonly used cycle structure that is complex and unstable, we employ a content consistency loss coupled with a content encoder to implement efficient single-path makeup transfer. The key insights of this study are modeling component-specific correspondence for local makeup transfer, capturing long-range dependencies for global makeup transfer, and enabling efficient makeup transfer via a single-path *** also contribute BeautyFace, a makeup transfer dataset to supplement existing datasets. This dataset contains 3,000 faces, covering more diverse makeup styles, face poses, and races. Each face has annotated parsing map. Extensive experiments demonstrate the effectiveness of our method against state-of-the-art methods. Besides, our method is appealing as it is with only 1M parameters, outperforming the state-of-the-art methods (BeautyGAN: 8.43M, PSGAN: 12.62M, SCGAN: 15.30M, CPM: 9.24M, SSAT: 10.48M). © 2023 IEEE.

关键词： computer vision

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A Confusion Matrix for Evaluating Feature Attribution Methods

A Confusion Matrix for Evaluating Feature Attribution Method...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Arias-Duart, Anna Mariotti, Ettore Garcia-Gasulla, Dario Alonso-Moral, Jose Maria Spain Spain

ISBN: (纸本)9798350302493

The increasing use of deep learning models in critical areas of computer vision and the consequent need for insights into model behaviour have led to the development of numerous feature attribution methods. However, these attributions must be both meaningful and plausible to end-users, which is not always the case. Recent research has emphasized the importance of faithfulness in attributions, as plausibility without faithfulness can result in misleading explanations and incorrect decisions. In this work., we propose a novel approach to evaluate the faithfulness of feature attribution methods by constructing an 'Attribution Confusion Matrix', which allows us to leverage a wide range of existing metrics from the traditional confusion matrix. This approach effectively introduces multiple evaluation measures for faithfulness in feature attribution methods in a unified and consistent framework. We demonstrate the effectiveness of our approach on various datasets, attribution methods, and models, emphasizing the importance of faithfulness in generating plausible and reliable explanations while also illustrating the distinct behaviour of different feature attribution methods. © 2023 IEEE.

关键词： computer vision

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An Investigation into the Impact of AI-Powered Image Enhancement on Forensic Facial recognition

An Investigation into the Impact of AI-Powered Image Enhance...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Justin Norman Hany Farid University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Advances in machine learning and computer vision have led to significant improvements in automated facial recognition. Many real-world forensic settings, however, are confronted with challenging low-quality and low-resolution images that often confound even state-of-the art facial recognition. We investigate if and when advances in neural-based image enhancement and restoration can be used to restore degraded images while preserving facial identity for use in forensic facial recognition.

关键词： computer vision Art Face recognition Forensics conferences Machine learning Image restoration

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Disentangling Local and Global Information for Light Field Depth Estimation

Disentangling Local and Global Information for Light Field D...

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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Yang, Xueting Deng, Junli Chen, Rongshan Cong, Ruixuan Ke, Wei Sheng, Hao Communication University of China School of Information and Communication Engineering Beijing100024 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

Accurate depth estimation from light field images is essential for various applications. Deep learning-based techniques have shown great potential in addressing this problem while still face challenges such as sensitivity to occlusions and difficulties in handling untextured areas. To overcome these limitations, we propose a novel approach that utilizes both local and global features in the cost volume for depth estimation. Specifically, our hybrid cost volume network consists of two complementary sub-modules: a 2D ContextNet for global context information and a matching cost volume for local feature information. We also introduce an occlusion-aware loss that accounts for occlusion areas to improve depth estimation quality. We demonstrate the effectiveness of our approach on the UrbanLF and HCInew datasets, showing significant improvements over existing methods, especially in occluded and untextured regions. Our method disentangles local feature and global semantic information explicitly, reducing the occlusion and untextured area reconstruction error and improving the accuracy of depth estimation. © 2023 IEEE.

关键词： Semantics

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