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。

基于图像的多视角 3D 多人姿态估计

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

Estimating 3D poses of multiple humans in real-time is a classic but still
challenging task in computer vision. Its major difficulty lies in the ambiguity
in cross-view association of 2D poses and the huge state space when there are
multiple people in multiple views. In this paper, we present a novel solution
for multi-human 3D pose estimation from multiple calibrated camera views. It
takes 2D poses in different camera coordinates as inputs and aims for the
accurate 3D poses in the global coordinate. Unlike previous methods that
associate 2D poses among all pairs of views from scratch at every frame, we
exploit the temporal consistency in videos to match the 2D inputs with 3D poses
directly in 3-space. More specifically, we propose to retain the 3D pose for
each person and update them iteratively via the cross-view multi-human
tracking. This novel formulation improves both accuracy and efficiency, as we
demonstrated on widely-used public datasets. To further verify the scalability
of our method, we propose a new large-scale multi-human dataset with 12 to 28
camera views. Without bells and whistles, our solution achieves 154 FPS on 12
cameras and 34 FPS on 28 cameras, indicating its ability to handle large-scale
real-world applications. The proposed dataset is released at
this https URL

本论文提出了一种基于多个校准相机视角的、面向实时应用的多人三维姿态估计解决方案，利用视频中的时间一致性直接在三维空间中匹配二维输入与三维姿态，通过跨视图多人跟踪迭代地更新姿态，从而提高了准确性和效率，并介绍了新的大规模多人数据集。