3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
<|reference_start|>3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images: 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.<|reference_end|>
@article{zhao20243d-ugcn:,
    title={3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose
  Estimation from Monocular RGB Images},
    author={Jie Zhao, Jianing Li, Weihan Chen, Wentong Wang, Pengfei Yuan, Xu
  Zhang, Deshu Peng},
    journal={arXiv preprint arXiv:2407.16137},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16137},
    primaryClass={cs.CV}
}