Keypoint Communities

Duncan Zauss; Sven Kreiss; Alexandre Alahi

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arXiv · 2110.00988

Keypoint Communities

Duncan Zauss·Sven Kreiss·Alexandre Alahi

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Abstract

We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation. We model all keypoints belonging to a human or an object -- the pose -- as a graph and leverage insights from community detection to quantify the independence of keypoints. We use a graph centrality measure to assign training weights to different parts of a pose. Our proposed measure quantifies how tightly a keypoint is connected to its neighborhood. Our experiments show that our method outperforms all previous methods for human pose estimation with fine-grained keypoint annotations on the face, the hands and the feet with a total of 133 keypoints. We also show that our method generalizes to car poses.

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BibTeX RIS

Duncan Zauss, Sven Kreiss, Alexandre Alahi. 2021-10-03. Keypoint Communities. https://arxiv.org/abs/2110.00988

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