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

TL;DR

This paper introduces a graph neural network framework for multi-view 3D human pose estimation, achieving state-of-the-art results with efficient computation by decomposing the task into localization and pose estimation stages.

Contribution

It proposes three specialized graph neural networks for effective multi-view human localization and pose estimation, improving accuracy and efficiency over previous methods.

Findings

State-of-the-art performance on CMU Panoptic dataset

Significantly lower computational complexity

Effective multi-view association and pose refinement

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.