Motion Guided 3D Pose Estimation from Videos

• URL: http://arxiv.org/abs/2004.13985v1
• Date: Wed, 29 Apr 2020 06:59:30 GMT
• Title: Motion Guided 3D Pose Estimation from Videos
• Authors: Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin
• Abstract summary: We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped GCN (UGCN), which captures both short-term and long-term motion information.
• Score: 81.14443206968444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped GCN (UGCN). It captures both short-term and long-term motion information to fully leverage the additional supervision from the motion loss. We experiment training UGCN with the motion loss on two large scale benchmarks: Human3.6M and MPI-INF-3DHP. Our model surpasses other state-of-the-art models by a large margin. It also demonstrates strong capacity in producing smooth 3D sequences and recovering keypoint motion.

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