Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2403.12440v2
• Date: Fri, 29 Mar 2024 14:55:50 GMT
• Title: Self-learning Canonical Space for Multi-view 3D Human Pose Estimation
• Authors: Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, Dinggang Shen,
• Abstract summary: Multi-view 3D human pose estimation is naturally superior to single view one. The accurate annotation of these information is hard to obtain. We propose a fully self-supervised framework, named cascaded multi-view aggregating network (CMANet) CMANet is superior to state-of-the-art methods in extensive quantitative and qualitative analysis.
• Score: 57.969696744428475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-view 3D human pose estimation is naturally superior to single view one, benefiting from more comprehensive information provided by images of multiple views. The information includes camera poses, 2D/3D human poses, and 3D geometry. However, the accurate annotation of these information is hard to obtain, making it challenging to predict accurate 3D human pose from multi-view images. To deal with this issue, we propose a fully self-supervised framework, named cascaded multi-view aggregating network (CMANet), to construct a canonical parameter space to holistically integrate and exploit multi-view information. In our framework, the multi-view information is grouped into two categories: 1) intra-view information , 2) inter-view information. Accordingly, CMANet consists of two components: intra-view module (IRV) and inter-view module (IEV). IRV is used for extracting initial camera pose and 3D human pose of each view; IEV is to fuse complementary pose information and cross-view 3D geometry for a final 3D human pose. To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL model, and propose a two-stage learning procedure. At first stage, IRV learns to estimate camera pose and view-dependent 3D human pose supervised by confident output of an off-the-shelf 2D keypoint detector. At second stage, IRV is frozen and IEV further refines the camera pose and optimizes the 3D human pose by implicitly encoding the cross-view complement and 3D geometry constraint, achieved by jointly fitting predicted multi-view 2D keypoints. The proposed framework, modules, and learning strategy are demonstrated to be effective by comprehensive experiments and CMANet is superior to state-of-the-art methods in extensive quantitative and qualitative analysis.

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