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H2ONet: Hand-Occlusion-and-Orientation-Aware Network for Real-Time 3D Hand Mesh Reconstruction

H2ONet: Hand-Occlusion-and-Orientation-Aware Network for Rea...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hao Xu Tianyu Wang Xiao Tang Chi-Wing Fu Department of Computer Science and Engineering Institute of Medical Intelligence and XR Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong

Real-time 3D hand mesh reconstruction is challenging, especially when the hand is holding some object. Beyond the previous methods, we design H2ONet to fully exploit non-occluded information from multiple frames to boost the reconstruction quality. First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level nonoccluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference. Second, we propose finger-level occlusionaware feature fusion, leveraging predicted finger-level occlusion information as guidance to fuse finger-level information across time frames. Further, we design hand-level occlusion-aware feature fusion to fetch non-occluded information from nearby time frames. We conduct experiments on the Dex-YCB and HO3D-v2 datasets with challenging hand-object occlusion cases, manifesting that H2ONet is able to run in real-time and achieves state-of-the-art performance on both the hand mesh and pose precision. The code will be released on GitHub.

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NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation

NIKI: Neural Inverse Kinematics with Invertible Neural Netwo...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiefeng Li Siyuan Bian Qi Liu Jiasheng Tang Fan Wang Cewu Lu Department of Computer Science and Engineering Shanghai Jiao Tong University Alibaba Group MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bidirectional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI can learn from both the forward and inverse processes with invertible networks. In the inverse process, the model separates the error from the plausible 3D pose manifold for a robust 3D human pose estimation. In the forward process, we enforce the zero-error boundary conditions to improve the sensitivity to reliable joint positions for better mesh-image alignment. Furthermore, NIKI emulates the analytical inverse kinematics algorithms with the twist-and-swing decomposition for better interpretability. Experiments on standard and occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we exhibit robust and well-aligned results simultaneously. Code is available at https://***/Jeff-sjtu/NIKI.

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Frame Interpolation Transformer and Uncertainty Guidance

Frame Interpolation Transformer and Uncertainty Guidance

引用

conference on computer vision and pattern recognition (CVPR)

作者： Markus Plack Matthias B. Hullin Karlis Martins Briedis Markus Gross Abdelaziz Djelouah Christopher Schroers University of Bonn Bonn Germany Department of Computer Science ETH Zürich Switzerland DisneyResearch|Studios Zürich Switzerland

Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or transformers. Still, the problem remains unsolved in more challenging conditions such as complex lighting or large motion. In this work, we are bridging the gap towards video production with a novel transformer-based interpolation network architecture capable of estimating the expected error together with the interpolated frame. This offers several advantages that are of key importance for frame interpolation usage: First, we obtained improved visual quality over several datasets. The improvement in terms of quality is also clearly demonstrated through a user study. Second, our method estimates error maps for the interpolated frame, which are essential for real-life applications on longer video sequences where problematic frames need to be flagged. Finally, for rendered content a partial rendering pass of the intermediate frame, guided by the predicted error, can be utilized during the interpolation to generate a new frame of superior quality. Through this error estimation, our method can produce even higher-quality intermediate frames using only a fraction of the time compared to a full rendering.

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Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

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

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

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3D Human Mesh Estimation from Virtual Markers

3D Human Mesh Estimation from Virtual Markers

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaoxuan Ma Jiajun Su Chunyu Wang Wentao Zhu Yizhou Wang School of Computer Science Center on Frontiers of Computing Studies Peking University Microsoft Research Asia Inst. for Artificial Intelligence Peking University Nat'l Eng. Research Center of Visual Technology

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://***/ShirleyMaxx/VirtualMarker

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Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances

Learning a Simple Low-Light Image Enhancer from Paired Low-L...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhenqi Fu Yan Yang Xiaotong Tu Yue Huang Xinghao Ding Kai-Kuang Ma Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Computer Science and Technology Hangzhou Dianzi University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://***/zhenqifu/PairLIE.

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LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation

LayoutDiffusion: Controllable Diffusion Model for Layout-to-...

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conference on computer vision and pattern recognition (CVPR)

作者： Guangcong Zheng Xianpan Zhou Xuewei Li Zhongang Qi Ying Shan Xi Li College of Computer Science & Technology Zhejiang University Polytechnic Institute Zhejiang University ARC Lab Tencent PCG Shanghai Institute for Advanced Study of Zhejiang University Shanghai AI Lab Zhejiang-Singapore Innovation and AI Joint Research Lab

Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging task. In this paper, we propose a diffusion model named LayoutDiffusion that can obtain higher generation quality and greater controllability than the previous works. To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form. Moreover, Layout Fusion Module (LFM) and Object-aware Cross Attention (OaCA) are proposed to model the relationship among multiple objects and designed to be object-aware and position-sensitive, allowing for precisely controlling the spatial related information. Extensive experiments show that our LayoutDiffusion out-performs the previous SOTA methods on FID, CAS by relatively 46.35%,26.70% on COCO-stuff and 44.29%,41.82% on VG. Code is available at https://***/ZGCTroy/LayoutDiffusion.

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2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsu...

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conference on computer vision and pattern recognition (CVPR)

作者： Mikhail Kennerley Jian-Gang Wang Bharadwaj Veeravalli Robby T. Tan Department of Electrical and Computer Engineering National University of Singapore Institute for Infocomm Research A*STAR

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudolabels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%, and to supervised models trained directly on the target data. 1 1 ***/mecarill/2pcnet

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Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery

Bootstrap Your Own Prior: Towards Distribution-Agnostic Nove...

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conference on computer vision and pattern recognition (CVPR)

作者： Muli Yang Liancheng Wang Cheng Deng Hanwang Zhang School of Electronic Engineering Xidian University Xi'an China School of Computer Science and Engineering Nanyang Technological University Singapore

Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes. Existing works hold an impractical assumption that the novel class distribution prior is uniform, yet neglect the imbalanced nature of real-world data. In this paper, we relax this assumption by proposing a new challenging task: distribution-agnostic NCD, which allows data drawn from arbitrary unknown class distributions and thus renders existing methods useless or even harmful. We tackle this challenge by proposing a new method, dubbed “Boot-strapping Your Own Prior (BYOP)”, which iteratively estimates the class prior based on the model prediction it-self. At each iteration, we devise a dynamic temperature technique that better estimates the class prior by encouraging sharper predictions for less-confident samples. Thus, BYOP obtains more accurate pseudo-labels for the novel samples, which are beneficial for the next training iteration. Extensive experiments show that existing methods suffer from imbalanced class distributions, while BYOp 1 1 Code: https://***/muliyangm/BYOP. out-performs them by clear margins, demonstrating its effectiveness across various distribution scenarios.

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Iterative Proposal Refinement for Weakly-Supervised Video Grounding

Iterative Proposal Refinement for Weakly-Supervised Video Gr...

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conference on computer vision and pattern recognition (CVPR)

作者： Meng Cao Fangyun Wei Can Xu Xiubo Geng Long Chen Can Zhang Yuexian Zou Tao Shen Daxin Jiang School of Electronic and Computer Engineering Peking University Microsoft Research Asia Microsoft The Hong Kong University of Science and Technology

Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations. To date, most of the state-of-the-art WSVG methods follow a two-stage pipeline, i.e., firstly generating potential temporal proposals and then grounding with these proposal candidates. Despite the recent progress, existing proposal generation methods suffer from two draw-backs: 1) lack of explicit correspondence modeling; and 2) partial coverage of complex events. To this end, we propose a novel IteRative prOposal refiNement network (dubbed as IRON) to gradually distill the prior knowledge into each proposal and encourage proposals with more complete coverage. Specifically, we set up two lightweight distillation branches to uncover the cross-modal correspondence on both the semantic and conceptual levels. Then, an iterative Label Propagation (LP) strategy is devised to prevent the network from focusing excessively on the most discriminative events instead of the whole sentence content. Precisely, during each iteration, the proposal with the minimal distillation loss and its adjacent ones are regarded as the positive samples, which refines proposal confidence scores in a cascaded manner. Extensive experiments and ablation studies on two challenging WSVG datasets have attested to the effectiveness of our IRON. The code will be available at https://***/mengcaopku/IRON.

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