Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images
Size Wu, Sheng Jin, Wentao Liu, Lei Bai, Chen Qian, Dong Liu, Wanli Ouyang
Abstract
This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into two stages, i.e. person localization and pose estimation. Both stages are processed in coarse-to-fine manners. And we propose three task-specific graph neural networks for effective message passing. For 3D person localization, we first use Multi-view Matching Graph Module (MMG) to learn the cross-view association and recover coarse human proposals. The Center Refinement Graph Module (CRG) further refines the results via flexible point-based prediction. For 3D pose estimation, the Pose Regression Graph Module (PRG) learns both the multi-view geometry and structural relations between human joints. Our approach achieves state-of-the-art performance on CMU Panoptic and Shelf datasets with significantly lower computation complexity.
BibTeX
@inproceedings{iccv2021_graphbased3dmult,
title = {Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images},
author = {Size Wu and Sheng Jin and Wentao Liu and Lei Bai and Chen Qian and Dong Liu and Wanli Ouyang},
booktitle = {ICCV 2021},
year = {2021}
}