Joint angle model based learning to refine kinematic human pose estimation

• URL: http://arxiv.org/abs/2507.11075v1
• Date: Tue, 15 Jul 2025 08:16:39 GMT
• Title: Joint angle model based learning to refine kinematic human pose estimation
• Authors: Chang Peng, Yifei Zhou, Huifeng Xi, Shiqing Huang, Chuangye Chen, Jianming Yang, Bao Yang, Zhenyu Jiang,
• Abstract summary: Current human pose estimation (HPE) suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories.<n>This paper proposed a method to overcome the difficulty through joint angle-based modeling.<n>A bidirectional recurrent network is designed as a post-processing module to refine the estimation of well-established HRNet.
• Score: 8.6527127612359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories when analyzing kinematic human poses. The performance of existing deep learning-based models for HPE refinement is considerably limited by inaccurate training datasets in which the keypoints are manually annotated. This paper proposed a novel method to overcome the difficulty through joint angle-based modeling. The key techniques include: (i) A joint angle-based model of human pose, which is robust to describe kinematic human poses; (ii) Approximating temporal variation of joint angles through high order Fourier series to get reliable "ground truth"; (iii) A bidirectional recurrent network is designed as a post-processing module to refine the estimation of well-established HRNet. Trained with the high-quality dataset constructed using our method, the network demonstrates outstanding performance to correct wrongly recognized joints and smooth their spatiotemporal trajectories. Tests show that joint angle-based refinement (JAR) outperforms the state-of-the-art HPE refinement network in challenging cases like figure skating and breaking.

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