Keypoint Communities

• URL: http://arxiv.org/abs/2110.00988v1
• Date: Sun, 3 Oct 2021 11:50:34 GMT
• Title: Keypoint Communities
• Authors: Duncan Zauss, Sven Kreiss, Alexandre Alahi
• Abstract summary: We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects. We use a graph centrality measure to assign training weights to different parts of a pose. Our method generalizes to car poses.
• Score: 87.06615538315003
• License: http://creativecommons.org/licenses/by/4.0/
• 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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