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.

本文介绍了一种从多个校准相机视角估计多个人的 3D 姿势的方法，通过使用图神经网络以及特定任务的模块实现了高性能、低计算复杂度的结果。