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

Jie Zhao; Jianing Li; Weihan Chen; Wentong Wang; Pengfei Yuan; Xu Zhang; Deshu Peng

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

Computer Vision and Pattern Recognition 2024-07-24 v1

Authors: Jie Zhao , Jianing Li , Weihan Chen , Wentong Wang , Pengfei Yuan , Xu Zhang , Deshu Peng

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.

Keywords

3D human pose estimation graph convolutional network monocular RGB occlusion spatial-temporal

Cite

@article{arxiv.2407.16137,
  title  = {3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images},
  author = {Jie Zhao and Jianing Li and Weihan Chen and Wentong Wang and Pengfei Yuan and Xu Zhang and Deshu Peng},
  journal= {arXiv preprint arXiv:2407.16137},
  year   = {2024}
}

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Proceedings of IEEE AICON2024

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

Abstract

Keywords

Cite

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