A Multi-Branch Network for Pose Trajectory Smoothing and Refinement

Panpan Chen, Ying Jiang, Haidong Hu, Chuangye Wang, Haolun Li, Hao Gao

Abstract: Although human pose estimation in video has achieved significant advancements, challenges such as occlusions often result in pronounced jitters and substantial errors. Addressing these challenges is critical for the further development of this field. In this study, we propose a multi-branch network designed for trajectory and pose refinement to mitigate these issues. The network comprises four branches, with three branches focus on capturing structural and motion features of human skeletal sequences, while one branch focuses on analyzing frequency features. More specifically, the first three branches model joint, bone vector, and motion data respectively to capture the structural and dynamic motion characteristics of human skeletal sequences. The frequency branch extracts frequency features to distinguish jitter from normal motion. Furthermore, the network incorporates a spatio-temporal residual block to capture both long-range temporal dependencies of individual joints and the spatial interrelationships among joints. Our method demonstrates competitive performance across three challenging datasets involving 2D, 3D, and SMPL pose representations.

External IDs:dblp:conf/icmcs/ChenJHWLG25