Graph Stacked Hourglass Networks for 3D Human Pose Estimation

• URL: http://arxiv.org/abs/2103.16385v1
• Date: Tue, 30 Mar 2021 14:25:43 GMT
• Title: Graph Stacked Hourglass Networks for 3D Human Pose Estimation
• Authors: Tianhan Xu, Wataru Takano
• Abstract summary: We propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations.
• Score: 1.0660480034605242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art.

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