Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey
Yang Liu Changzhen Qiu Zhiyong Zhang
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.