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