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

• URL: http://arxiv.org/abs/2104.00953v1
• Date: Fri, 2 Apr 2021 09:24:29 GMT
• Title: Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction
• Authors: Ze Ma, Yifan Yao, Pan Ji, Chao Ma
• Abstract summary: We propose a kinematic dictionary, which explicitly regularizes the solution space of relative 3D rotations of human joints. Our method achieves end-to-end 3D reconstruction without the need of using any shape annotations during the training of neural networks. The proposed method achieves competitive results on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP.
• Score: 15.586347115568973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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