Better 3D pose estimates in video by dynamically learning joint relationships
Better 3D pose estimates in video by dynamically learning joint relationships
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
arXiv paper abstract https://arxiv.org/abs/2109.07353v1
arXiv PDF paper https://arxiv.org/pdf/2109.07353v1.pdf
Graph Convolution Network (GCN) ... for 3D human pose estimation in videos. ... built on the fixed human-joint affinity ...
may reduce adaptation capacity of GCN to tackle complex spatio-temporal pose variations
... 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.
... discover spatial/temporal human-joint affinity for each video exemplar, depending on spatial distance/temporal movement similarity between human joints
... 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.
... DG-Net outperforms a number of recent SOTA approaches with fewer input frames and model size.
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