Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and Beyond

2022 ◽  
Author(s):  
Jielu Yan ◽  
MingLiang Zhou ◽  
Jinli Pan ◽  
Meng Yin ◽  
Bin Fang
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Recent Progress ◽  
State Of The Art ◽  
The State ◽  
Human Pose Estimation ◽  
Estimation Techniques ◽  
Comprehensive Survey ◽  
Human Pose ◽  
3D Human Pose Estimation

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.

scholarly journals Human pose, hand and mesh estimation using deep learning: a survey

2022 ◽  
Author(s):  
Mukhiddin Toshpulatov ◽  
Wookey Lee ◽  
Suan Lee ◽  
Arousha Haghighian Roudsari
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Pose Estimation ◽  
State Of The Art ◽  
Significant Problem ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Estimation Techniques ◽  
The Past ◽  
Human Pose

AbstractHuman pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

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