  Human pose estimation remains a multifaceted challenge in computer vision,
pivotal across diverse domains such as behavior recognition, human-computer
interaction, and pedestrian tracking. This paper proposes an improved method
based on the spatial-temporal graph convolution net-work (UGCN) to address the
issue of missing human posture skeleton sequences in single-view videos. We
present the improved UGCN, which allows the network to process 3D human pose
data and improves the 3D human pose skeleton sequence, thereby resolving the
occlusion issue.
