@Article{cmes.2022.020857,
AUTHOR = {Jianchu Lin, Shuang Li, Hong Qin, Hongchang Wang, Ning Cui, Qian Jiang, Haifang Jian, Gongming Wang},
TITLE = {Overview of 3D Human Pose Estimation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {3},
PAGES = {1621--1651},
URL = {http://www.techscience.com/CMES/v134n3/49754},
ISSN = {1526-1506},
ABSTRACT = {3D human pose estimation is a major focus area in the field of computer vision, which plays an important role
in practical applications. This article summarizes the framework and research progress related to the estimation of
monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced.
Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common
problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity,
and the lack of training datasets often results in poor generalization ability of the model. Regression methods are
crucial for solving such problems. Considering image-based input, the multi-view method is commonly used to
solve occlusion problems. Here, the multi-view method is analyzed comprehensively. By referring to video-based
input, the human prior knowledge of restricted motion is used to predict human postures. In addition, structural
constraints are widely used as prior knowledge. Furthermore, weakly supervised learning methods are studied and
discussed for these two types of inputs to improve the model generalization ability. The problem of insufficient
training datasets must also be considered, especially because 3D datasets are usually biased and limited. Finally,
emerging and popular datasets and evaluation indicators are discussed. The characteristics of the datasets and the
relationships of the indicators are explained and highlighted. Thus, this article can be useful and instructive for
researchers who are lacking in experience and find this field confusing. In addition, by providing an overview of
3D human pose estimation, this article sorts and refines recent studies on 3D human pose estimation. It describes
kernel problems and common useful methods, and discusses the scope for further research.},
DOI = {10.32604/cmes.2022.020857}
}