Joint angle model based learning to refine kinematic human pose estimation

-- 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. Index Terms -- Human pose estimation, k inematic pose, r efinement, j oint angle, F ourier series, r ecurrent neural network . INTRODUCTION omputer vision based human pose estimation (HPE) has been widely adopted as a powerful tool to determine the configuration of human body from images and videos. This technology has found increasing applications across various fields such as human - computer interaction, motion analysis, augmented or virtual reality, and healthcare [1] . The study was financially supported by the National Natural Science Foundation of China (Grant Nos.