Single-Person 3D Human Pose Estimation Based on Deep Learning: A Review
Aoyu Xia, Shouming Hou, Zixuan Lu, Weibo Yang · Journal of Computer Science and Artificial Intelligence · 2025
With the rapid development of deep learning technologies, human pose estimation has become a hot research topic in the field of computer vision. This paper provides a systematic review of the latest advances in single-person 3D human pose estimation based on deep learning, with a focus on the main challenges faced by both single-view and multi-view approaches, such as pose estimation accuracy, occlusion issues, and the effective utilization of depth information. Firstly, the paper explores single-view human pose estimation methods based on single-frame images and video data. Then, it introduces the research status of multi-view human pose estimation, and discusses in detail how these methods address pose occlusion and multi-view data fusion. In addition, the paper reviews commonly used datasets and evaluation metrics, and assesses the performance of various methods on standard datasets through comparative analysis. Finally, the paper outlines future research directions for single-person 3D human pose estimation, particularly in improving estimation accuracy and addressing pose variations and occlusions in complex scenarios.