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.

翻译：三维人体姿态估计与网格恢复在计算机视觉、自动驾驶及机器人等众多领域引起了广泛研究兴趣。近年来，基于深度学习的三维人体姿态估计与网格恢复研究蓬勃发展，大量方法被提出以解决该领域的各类问题。为激发未来研究，本文通过深入分析200余篇参考文献，对过去五年间该领域深度学习方法的进展进行了全面综述。据我们所知，本综述首次系统性地覆盖了深度学习在三维人体姿态估计（包括单人与多人方法）及人体网格恢复（涵盖基于显式模型与隐式表示的方法）中的应用。我们还展示了多个公开数据集上的对比结果，并提供了富有洞见的观察结论与具有启发性的未来研究方向。定期更新的项目页面可访问：https://github.com/liuyangme/SOTA-3DHPE-HMR。