3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
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Abstract (arXiv)
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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References · 12
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
- 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 493
- 2017A simple yet effective baseline for 3d human pose estimationcited by 401
- 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 304
- 2016Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Posecited by 220
- 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 112
- 2018Ordinal Depth Supervision for 3D Human Pose Estimationcited by 107
- 2019Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Networkcited by 92
- 2020Motion Guided 3D Pose Estimation from Videoscited by 85
- 20163D Human Pose Estimation from a Single Image via Distance Matrix Regressioncited by 73
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