3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images

• URL: http://arxiv.org/abs/2407.16137v1
• Date: Tue, 23 Jul 2024 02:50:27 GMT
• Title: 3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
• Authors: Jie Zhao, Jianing Li, Weihan Chen, Wentong Wang, Pengfei Yuan, Xu Zhang, Deshu Peng,
• Abstract summary: 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 improvedN, which allows the network to process 3D human pose data and improves the 3D human pose skeleton sequence.
• Score: 17.673385426594418
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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