Although significant improvement has been achieved recently in 3D human pose
estimation, most of the previous methods only treat a single-person case. In
this work, we firstly propose a fully learning-based, camera distance-aware
top-down approach for 3D multi-person pose estimation from a single RGB image.
The pipeline of the proposed system consists of human detection, absolute 3D
human root localization, and root-relative 3D single-person pose estimation
modules. Our system achieves comparable results with the state-of-the-art 3D
single-person pose estimation models without any groundtruth information and
significantly outperforms previous 3D multi-person pose estimation methods on
publicly available datasets.
The code is available in this https URL ,
this https URL

本文提出了一种全面的基于学习的、视野距离感知的自顶向下的方法，用于从单个 RGB 图像中估计多人的 3D 姿态，并通过人体检测、3D 人体根位置定位和相对根姿态估计模块，实现了与最先进单人 3D 姿态估计模型可相媲美的结果，同时也比公开数据集上之前的 3D 多人姿态估计方法表现更出色。