Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

• URL: http://arxiv.org/abs/2109.07353v1
• Date: Wed, 15 Sep 2021 15:06:19 GMT
• Title: Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
• Authors: Junhao Zhang, Yali Wang, Zhipeng Zhou, Tianyu Luan, Zhe Wang, Yu Qiao
• Abstract summary: Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. New Dynamical Graph Network (DGNet) can estimate 3D pose by adaptively learning spatial/temporal joint relations from videos.
• Score: 47.601288796052714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human skeleton. This may reduce adaptation capacity of GCN to tackle complex spatio-temporal pose variations in videos. To alleviate this problem, we propose a novel Dynamical Graph Network (DG-Net), which can dynamically identify human-joint affinity, and estimate 3D pose by adaptively learning spatial/temporal joint relations from videos. Different from traditional graph convolution, we introduce Dynamical Spatial/Temporal Graph convolution (DSG/DTG) to discover spatial/temporal human-joint affinity for each video exemplar, depending on spatial distance/temporal movement similarity between human joints in this video. Hence, they can effectively understand which joints are spatially closer and/or have consistent motion, for reducing depth ambiguity and/or motion uncertainty when lifting 2D pose to 3D pose. We conduct extensive experiments on three popular benchmarks, e.g., Human3.6M, HumanEva-I, and MPI-INF-3DHP, where DG-Net outperforms a number of recent SOTA approaches with fewer input frames and model size.

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