3D human pose estimation and mesh recovery have attracted widespread research interest in many areas, such as computer vision, autonomous driving, and robotics. Deep learning on 3D human pose estimation and mesh recovery has recently thrived, with numerous methods proposed to address different problems in this area. In this paper, to stimulate future research, we present a comprehensive review of recent progress over the past five years in deep learning methods for this area by delving into over 200 references. To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation, including both single-person and multi-person approaches, as well as human mesh recovery, encompassing methods based on explicit models and implicit representations. We also present comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions. A regularly updated project page can be found at https://github.com/liuyangme/SOTA-3DHPE-HMR.

本文通过研究200多篇论文，全面回顾了过去五年中3D人体姿态估计和网格恢复领域的深度学习方法进展，包括单人和多人方法，以及基于显式模型和隐式表示的人体网格恢复方法，并提供了公开数据集上的比较结果，以及深入观察和激发未来研究方向。

深度学习用于3D人体姿势估计和网格恢复的综述