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Keypoint Communities
Duncan Zauss, Sven Kreiss, Alexandre Alahi
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Human Pose Estimation | COCO-WholeBody | Zauss et al. | WB | 60.4 | #9 of 15 | Archive leaderboard | report |
| 2D Human Pose Estimation | COCO-WholeBody | Zauss et al. | body | 69.6 | #9 of 15 | Archive leaderboard | report |
| 2D Human Pose Estimation | COCO-WholeBody | Zauss et al. | face | 85.0 | #9 of 15 | Archive leaderboard | report |
| 2D Human Pose Estimation | COCO-WholeBody | Zauss et al. | foot | 63.4 | #9 of 15 | Archive leaderboard | report |
| 2D Human Pose Estimation | COCO-WholeBody | Zauss et al. | hand | 52.9 | #9 of 15 | Archive leaderboard | report |
| Car Pose Estimation | ApolloCar3D | Zauss et al. | Detection Rate | 91.9 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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