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Xiaowei Zhou
Researcher at Zhejiang University
Publications - 142
Citations - 9712
Xiaowei Zhou is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Pose. The author has an hindex of 31, co-authored 108 publications receiving 6017 citations. Previous affiliations of Xiaowei Zhou include University of Pennsylvania & Hong Kong University of Science and Technology.
Papers
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Proceedings ArticleDOI
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
TL;DR: In this paper, a fine discretization of the 3D space around the subject and train a ConvNet to predict per voxel likelihoods for each joint is proposed.
Proceedings ArticleDOI
Learning to Estimate 3D Human Pose and Shape from a Single Color Image
TL;DR: This work addresses the problem of estimating the full body 3D human pose and shape from a single color image and proposes an efficient and effective direct prediction method based on ConvNets, incorporating a parametric statistical body shape model (SMPL) within an end-to-end framework.
Journal ArticleDOI
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
TL;DR: Zhang et al. as mentioned in this paper proposed a unified framework named detecting contiguous outliers in the LOw-rank representation (DECOLOR), which integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm.
Posted Content
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
TL;DR: This paper proposes a fine discretization of the 3D space around the subject and trains a ConvNet to predict per voxel likelihoods for each joint, which creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates.
Proceedings ArticleDOI
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation
TL;DR: A Pixel-wise Voting Network (PVNet) is introduced to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations, which creates a flexible representation for localizing occluded or truncated keypoints.