Papers › CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
Jiefeng Li, Can Wang, Hao Zhu, Yihuan Mao, Hao-Shu Fang, Cewu Lu
Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method. Source code and dataset will be made publicly available.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Person Pose Estimation | CrowdPose | Joint-candidate SPPE + | AP Easy | 75.5 | #17 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | Joint-candidate SPPE + | AP Hard | 57.4 | #17 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | Joint-candidate SPPE + | AP Medium | 66.3 | #17 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | Joint-candidate SPPE + | FPS | 10.1 | #17 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | Joint-candidate SPPE + | mAP @0.5:0.95 | 66.0 | #17 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | OCHuman | CrowdPose | AP50 | 40.8 | #6 of 8 | Archive leaderboard | report |
| Multi-Person Pose Estimation | OCHuman | CrowdPose | AP75 | 29.9 | #6 of 8 | Archive leaderboard | report |
| Multi-Person Pose Estimation | OCHuman | CrowdPose | Validation AP | 27.5 | #6 of 8 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections