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PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation

arXiv 2308.11440 ↗First posted 22 Aug 2023cs.CVcited by 0

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Abstract (arXiv)

Existing skeleton-based 3D human pose estimation methods only predict joint positions. Although the yaw and pitch of bone rotations can be derived from joint positions, the roll around the bone axis remains unresolved. We present PoseGraphNet++ (PGN++), a novel 2D-to-3D lifting Graph Convolution Network that predicts the complete human pose in 3D including joint positions and bone orientations. We employ both node and edge convolutions to utilize the joint and bone features. Our model is evaluated on multiple datasets using both position and rotation metrics. PGN++ performs on par with the state-of-the-art (SoA) on the Human3.6M benchmark. In generalization experiments, it achieves the best results in position and matches the SoA in orientation, showcasing a more balanced performance than the current SoA. PGN++ exploits the mutual relationship of joints and bones resulting in significantly SBimproved position predictions, as shown by our ablation results.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 22 Aug 2023.

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33 of 42 references.

References · 33

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21 more
  1. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
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  14. 2017Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Imagecited by 73
  15. 2019HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimationcited by 50
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  21. 2020Orientation Keypoints for 6D Human Pose Estimationcited by 3

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