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Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction

arXiv 2104.00953 ↗First posted 2 Apr 2021cs.CVcited by 0

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Abstract (arXiv)

Estimating 3D human pose and shape from a single image is highly under-constrained. To address this ambiguity, we propose a novel prior, namely kinematic dictionary, which explicitly regularizes the solution space of relative 3D rotations of human joints in the kinematic tree. Integrated with a statistical human model and a deep neural network, our method achieves end-to-end 3D reconstruction without the need of using any shape annotations during the training of neural networks. The kinematic dictionary bridges the gap between in-the-wild images and 3D datasets, and thus facilitates end-to-end training across all types of datasets. The proposed method achieves competitive results on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP, while running in real-time given the human bounding boxes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 38 of its 56 references that are in the map. First posted on arXiv 2 Apr 2021.

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References · 38

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26 more
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