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SMPLicit: Topology-aware Generative Model for Clothed People

SMPLicit: Topology-aware Generative Model for Clothed People

引用

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

作者： Corona, Enric Pumarola, Albert Alenya, Guillem Pons-Moll, Gerard Moreno-Noguer, Francesc UPC CSIC Inst Robot & Informat Ind Barcelona Spain Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781665445092

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://***. edu/people/ecorona/smplicit/.

关键词： Geometry Training computer vision Three-dimensional displays Shape Computational modeling Clothing

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Weak Multi-View Supervision for Surface Mapping Estimation

Weak Multi-View Supervision for Surface Mapping Estimation

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

作者： Rai, Nishant Liaudanskas, Aidas Rao, Srinivas Cayon, Rodrigo Ortiz Munaro, Matteo Holzer, Stefan Fyusion Inc San Francisco CA 94105 USA Stanford Univ Stanford CA 94305 USA Fyusion San Francisco CA USA

ISBN: (纸本)9781665448994

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360ffi camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches.

关键词： Deformable models Surface reconstruction Airplanes Annotations Rendering (computer graphics) Trajectory Automobiles

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Delving into Data: Effectively Substitute Training for Black-box Attack

Delving into Data: Effectively Substitute Training for Black...

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

作者： Wang, Wenxuan Yin, Bangjie Yao, Taiping Zhang, Li Fu, Yanwei Ding, Shouhong Li, Jilin Huang, Feiyue Xue, Xiangyang Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Fudan Univ MOE Frontiers Ctr Brain Sci Sch Data Sci Shanghai Peoples R China Tencent Youtu Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, training a substitute model for adversarial attacks has attracted wide attention. Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models. In this paper, we propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. More specifically, a diverse data generation module is proposed to synthesize large-scale data with wide distribution. And adversarial substitute training strategy is introduced to focus on the data distributed near the decision boundary. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack. Extensive experiments demonstrate the efficacy of our method against state-of-the-art competitors under non-target and target attack settings. Detailed visualization and analysis are also provided to help understand the advantage of our method.

关键词： Training computer vision Computational modeling Training data Distributed databases Data visualization Data models

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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

Farewell to Mutual Information: Variational Distillation for...

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

作者： Tian, Xudong Zhang, Zhizhong Lin, Shaohui Qu, Yanyun Xie, Yuan Ma, Lizhuang East China Normal Univ Shanghai Peoples R China Xiamen Univ Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.

关键词： Training Correlation Fitting Redundancy Estimation Robustness pattern recognition

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Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

Learning to Recommend Frame for Interactive Video Object Seg...

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

作者： Yin, Zhaoyuan Zheng, Jia Luo, Weixin Qian, Shenhan Zhang, Hanling Gao, Shenghua Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Manycore KooLab Rancho Cordova CA USA Meituan Grp Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China Hunan Univ Sch Design Changsha Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms.

关键词： Measurement computer vision Codes Annotations Object segmentation Reinforcement learning Markov processes

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LiBRe: A Practical Bayesian Approach to Adversarial Detection

LiBRe: A Practical Bayesian Approach to Adversarial Detectio...

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

作者： Deng, Zhijie Yang, Xiao Xu, Shizhen Su, Hang Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech BNRist Ctr Inst AITsinghua Bosch Joint ML CtrTHBI Lab Beijing 100084 Peoples R China RealAI Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve this problem, but they typically demonstrate restricted practicability owing to unsurmountable compromise on universality, effectiveness, or efficiency. In this work, we propose a more practical approach, Lightweight Bayesian Refinement (LiBRe), in the spirit of leveraging Bayesian neural networks (BNNs) for adversarial detection. Empowered by the task and attack agnostic modeling under Bayes principle, LiBRe can endow a variety of pre-trained task-dependent DNNs with the ability of defending heterogeneous adversarial attacks at a low cost. We develop and integrate advanced learning techniques to make LiBRe appropriate for adversarial detection. Concretely, we build the few-layer deep ensemble variational and adopt the pre-training & fine-tuning workflow to boost the effectiveness and efficiency of LiBRe. We further provide a novel insight to realise adversarial detection-oriented uncertainty quantification without inefficiently crafting adversarial examples during training. Extensive empirical studies covering a wide range of scenarios verify the practicability of LiBRe. We also conduct thorough ablation studies to evidence the superiority of our modeling and learning strategies.(1)

关键词： Training Deep learning Costs Uncertainty Neural networks Bayes methods pattern recognition

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Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms

Dynamic-OFA: Runtime DNN Architecture Switching for Performa...

引用

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

作者： Lou, Wei Xun, Lei Sabet, Amin Bi, Jia Hare, Jonathon Merrett, Geoff, V Univ Southampton Southampton Hants England

ISBN: (纸本)9781665448994

Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency.

关键词： Training Three-dimensional displays Computational modeling Pipelines Graphics processing units computer architecture Switches

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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

DivCo: Diverse Conditional Image Synthesis via Contrastive G...

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

作者： Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve this issue, previous works [47, 22] mainly focused on encouraging the correlation between the latent codes and their generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN [27] tried to encourage the diversity of the generated image but only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A novel latent-augmented contrastive loss is introduced, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent codes to be dissimilar. The proposed latent-augmented contrastive loss is well compatible with various cGAN architectures. Extensive experiments demonstrate that the proposed DivCo can produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple unpaired and paired image generation tasks. Training code and pretrained models are available at https://***/ruiliu-ai/DivCo.

关键词： Training Visualization computer vision Codes Correlation Image synthesis Generative adversarial networks

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Overlap Suppression Clustering for Offline Multi-Camera People Tracking

Overlap Suppression Clustering for Offline Multi-Camera Peop...

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ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ryuto Yoshida Junichi Okubo Junichiro Fujii Masazumi Amakata Takayoshi Yamashita Yachiyo Engineering Co. Ltd. Chubu University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks, such as Object Detection, Multiple Object Tracking, and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering, (2) representative image extraction using pose estimation for re-identification, (3) re-identification using hierarchical clustering with average linkage, and (4) low-identifiability tracklets *** RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.

关键词： Couplings computer vision Correlation conferences Pose estimation Urban areas Object detection

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Sketch, Ground, and Refine: Top-Down Dense Video Captioning

Sketch, Ground, and Refine: Top-Down Dense Video Captioning

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

作者： Deng, Chaorui Chen, Shizhe Chen, Da He, Yuan Wu, Qi Univ Adelaide Adelaide SA Australia INRIA Rocquencourt France Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the definitions of events are diverse which could be as simple as a single action or as complex as a set of events, depending on different semantic contexts. Therefore, directly detecting events based on video information is ill-defined and hurts the coherency and accuracy of generated dense captions. In this work, we reverse the predominant "detect-then-describe" fashion, proposing a top-down way to first generate paragraphs from a global view and then ground each event description to a video segment for detailed refinement. It is formulated as a Sketch, Ground, and Refine process (SGR). The sketch stage first generates a coarse-grained multi-sentence paragraph to describe the whole video, where each sentence is treated as an event and gets localised in the grounding stage. In the refining stage, we improve captioning quality via refinement-enhanced training and dual-path cross attention on both coarse-grained event captions and aligned event segments. The updated event caption can further adjust its segment boundaries. Our SGR model outperforms state-of-the-art methods on ActivityNet Captioning benchmark under traditional and story-oriented dense caption evaluations.

关键词： Training computer vision Codes Grounding Computational modeling Semantics Benchmark testing

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