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Multi-hop graph transformer network for 3D human pose estimation
May 7, 2024, 4:48 a.m. | Zaedul Islam, A. Ben Hamza
cs.CV updates on arXiv.org arxiv.org
Abstract: Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi-head self-attention and multi-hop graph convolutional networks with disentangled neighborhoods to capture spatio-temporal dependencies and handle long-range interactions. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph …
2d-to-3d abstract arxiv attention convolutional cs.cv graph head human multi-head network networks paper self-attention transformer transformer network type videos
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