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Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images

Authors :
Wu, Size
Jin, Sheng
Liu, Wentao
Bai, Lei
Qian, Chen
Liu, Dong
Ouyang, Wanli
Source :
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Publication Year :
2021
Publisher :
IEEE, 2021.

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.<br />Comment: Accepted by ICCV'2021

Details

Database :
OpenAIRE
Journal :
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Accession number :
edsair.doi.dedup.....018631e1cbb5335b4acaa9af483f4c13
Full Text :
https://doi.org/10.1109/iccv48922.2021.01096