Year: 2025
Type: conference-paper
Abstract: Three-dimensional human pose estimation is pivotal in diverse fields such as motion analysis, healthcare, and smart home design. This study addresses the challenge of deriving accurate 3D human poses from single-frame two-dimensional skeleton data. We begin by analyzing the error distribution of a foundational network for estimating human pose keypoints. Based on this analysis, we propose a torso-centered grouping method and design a multi-branch network for enhanced 3D pose estimation. Furthermore, we introduce a distance constraint loss function to mitigate the structural information loss caused by grouping and to reduce estimation errors at limb extremities. Our experimental results on benchmark datasets demonstrate that the proposed network significantly improves the accuracy of 3D human pose estimation. These findings hold promise for advancing applications in motion analysis and abnormal behavior monitoring.
DOI: 10.1117/12.3061820
Authors: Luoyang Chen, Junxian Li, Zheng Liu, Ye Tao, Yazhe Cheng, Weibiao Du, Xingdong Bao, Hongxia Mao
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409785040