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Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans

Neural Body: Implicit Neural Representations with Structured...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Peng, Sida Zhang, Yuanqing Xu, Yinghao Wang, Qianqian Shuai, Qing Bao, Hujun Zhou, Xiaowei Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. To evaluate our approach, we create a multi-view dataset named ZJU-MoCap that captures performers with complex motions. Experiments on ZJU-MoCap show that our approach outperforms prior works by a large margin in terms of novel view synthesis quality. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

关键词： Geometry Deformable models computer vision Codes Three-dimensional displays Neural networks Dynamics

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Adaptive Methods for Real-World Domain Generalization

Adaptive Methods for Real-World Domain Generalization

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dubey, Abhimanyu Ramanathan, Vignesh Pentland, Alex Mahajan, Dhruv MIT Cambridge MA 02139 USA Facebook AI Cambridge MA USA

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the unseen test samples themselves. We propose a domain-adaptive approach consisting of two steps: a) we first learn a discriminative domain embedding from unsupervised training examples, and b) use this domain embedding as supplementary information to build a domainadaptive model, that takes both the input as well as its domain into account while making predictions. For unseen domains, our method simply uses few unlabelled test examples to construct the domain embedding. This enables adaptive classification on any unseen domain. Our approach achieves state-of-the-art performance on various domain generalization benchmarks. In addition, we introduce the first real-world, large-scale domain generalization benchmark, Geo-YFCC, containing 1.1M samples over 40 training, 7 validation and 15 test domains, orders of magnitude larger than prior work. We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains. In contrast, our approach achieves a significant 1% improvement.

关键词： Training Heart Adaptation models computer vision Machine learning Benchmark testing Predictive models

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EgoGen: An Egocentric Synthetic Data Generator

EgoGen: An Egocentric Synthetic Data Generator

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Gen Zhao, Kaifeng Zhang, Siwei Lyu, Xiaozhong Dusmanu, Mihai Zhang, Yan Pollefeys, Marc Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9798350353006

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models, but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach, our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works, our model eliminates the need for a predefined global path, and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline, we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. EgoGen will be fully open-sourced, offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research.

关键词： autonomous virtual humans egocentric vision reinforcement learning synthetic data

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S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation

S2R-DepthNet: Learning a Generalizable Depth-specific Struct...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Xiaotian Wang, Yuwang Chen, Xuejin Zeng, Wenjun Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to unseen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domain-invariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the target domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-of-the-art performance under the semi-supervised setting.

关键词： Training Geometry computer vision Three-dimensional displays Semantics Estimation Feature extraction

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LAVT: Language-Aware vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zhao Wang, Jiaqi Tang, Yansong Chen, Kai Zhao, Hengshuang Torr, Philip H. S. Univ Oxford Oxford England Shanghai AI Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling this problem is to leverage a powerful vision-language ("cross-modal") decoder to fuse features independently extracted from a vision encoder and a language encoder. Recent methods have made remarkable advancements in this paradigm by exploiting Transformers as cross-modal decoders, concurrent to the Transformer's overwhelming success in many other vision-language tasks. Adopting a different approach in this work, we show that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network. By conducting cross-modal feature fusion in the visual feature encoding stage, we can leverage the well-proven correlation modeling power of a Transformer encoder for excavating helpful multi-modal context. This way, accurate segmentation results are readily harvested with a light-weight mask predictor. Without bells and whistles, our method surpasses the previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins.

关键词： Image segmentation Visualization Image coding Shape Linguistics Transformers Feature extraction

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Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation

Embedded Discriminative Attention Mechanism for Weakly Super...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wu, Tong Huang, Junshi Gao, Guangyu Wei, Xiaoming Wei, Xiaolin Luo, Xuan Liu, Chi Harold Beijing Inst Technol Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly Supervised Semantic Segmentation (WSSS) with image-level annotation uses class activation maps from the classifier as pseudo-labels for semantic segmentation. However, such activation maps usually highlight the local discriminative regions rather than the whole object, which deviates from the requirement of semantic segmentation. To explore more comprehensive class-specific activation maps, we propose an Embedded Discriminative Attention Mechanism (EDAM) by integrating the activation map generation into the classification network directly for WSSS. Specifically, a Discriminative Activation (DA) layer is designed to explicitly produce a series of normalized class-specific masks, which are then used to generate class-specific pixellevel pseudo-labels demanded in segmentation. For learning the pseudo-labels, the masks are multiplied with the feature maps after the backbone to generate the discriminative activation maps, each of which encodes the specific information of the corresponding category in the input images. Given such class-specific activation maps, a Collaborative Multi-Attention (CMA) module is proposed to extract the collaborative information of each given category from images in a batch. In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation. Based on the generated pseudo-labels, we achieve the mIoU of 70.60% on PASCAL VOC 2012 segmentation test-set, which is the new state-of-the-art, to our best knowledge. Code and pre-trained models are available online soon.

关键词： Image segmentation computer vision Codes Annotations Semantics Collaboration Feature extraction

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Visual-Semantic Matching by Exploring High-Order Attention and Distraction

Visual-Semantic Matching by Exploring High-Order Attention a...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Yongzhi Zhang, Duo Mu, Yadong Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ EECS Beijing Peoples R China Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781728171685

Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information (e.g., object-predicate-subject triplet, object-attribute pair) and visual distraction (i.e., despite the high relevance to textual query, images may also contain many prominent distracting objects or visual relations). Specifically, we build scene graphs for both visual and textual modalities. Our technical contributions are two-folds: firstly, we formulate the visual-semantic matching task as an attention-driven cross-modality scene graph matching problem. Graph convolutional networks (GCNs) are used to extract high-order information from two scene graphs. A novel cross-graph attention mechanism is proposed to contextually reweigh graph elements and calculate the inter-graph similarity;Secondly, some top-ranked samples are indeed false matching due to the co-occurrence of both highly-relevant and distracting information. We devise an information-theoretic measure for estimating semantic distraction and re-ranking the initial retrieval results. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the superiority of the proposed method and the effectiveness of both high-order attention and distraction.

关键词： computer vision

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Rank-One Prior: Toward Real-Time Scene Recovery

Rank-One Prior: Toward Real-Time Scene Recovery

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jun Liu, Ryan Wen Sun, Jianing Zeng, Tieyong Northeast Normal Univ Sch Math & Stat Changchun Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Northeast Normal Univ Jilin Natl Appl Math Ctr Changchun Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

关键词： Learning systems computer vision Visualization Imaging Estimation Training data Video surveillance

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Dynamic Domain Adaptation for Efficient Inference

Dynamic Domain Adaptation for Efficient Inference

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Shuang Zhang, JinMing Ma, Wenxuan Liu, Chi Harold Li, Wei Beijing Inst Technol Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer "easier" and "harder" target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers;2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios.

关键词： Training Deep learning computer vision Computational modeling Benchmark testing Predictive models Real-time systems

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FFNet: Video Fast-Forwarding via Reinforcement Learning 31

FFNet: Video Fast-Forwarding via Reinforcement Learning

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lan, Shuyue Panda, Rameswar Zhu, Qi Roy-Chowdhury, Amit K. Northwestern Univ Evanston IL 60208 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781538664209

For many applications with limited computation, communication, storage and energy resources, there is an imperative need of computer vision methods that could select an informative subset of the input video for efficient processing at or near real time. In the literature, there are two relevant groups of approaches: generating a "trailer" for a video or fast-forwarding while watching/processing the video. The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fast-forwarding methods depend on either manual control or automatic adaptation of playback speed, which often do not present an accurate representation and may still require processing of every frame. In this paper, we introduce FastForwardNet (FFNet), a reinforcement learning agent that gets inspiration from video summarization and does fast-forwarding differently. It is an online framework that automatically fast-forwards a video and presents a representative subset of frames to users on the fly. It does not require processing the entire video, but just the portion that is selected by the fast-forward agent, which makes the process very computationally efficient. The online nature of our proposed method also enables the users to begin fast-forwarding at any point of the video. Experiments on two real-world datasets demonstrate that our method can provide better representation of the input video (about 6%-20% improvement on coverage of important frames) with much less processing requirement (more than 80% reduction in the number of frames processed).

关键词： Streaming media Real-time systems Markov processes Microsoft Windows computer vision Time factors

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