Papers › RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation
RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation
Peng Lu, Tao Jiang, Yining Li, Xiangtai Li, Kai Chen, Wenming Yang
Real-time multi-person pose estimation presents significant challenges in balancing speed and precision. While two-stage top-down methods slow down as the number of people in the image increases, existing one-stage methods often fail to simultaneously deliver high accuracy and real-time performance. This paper introduces RTMO, a one-stage pose estimation framework that seamlessly integrates coordinate classification by representing keypoints using dual 1-D heatmaps within the YOLO architecture, achieving accuracy comparable to top-down methods while maintaining high speed. We propose a dynamic coordinate classifier and a tailored loss function for heatmap learning, specifically designed to address the incompatibilities between coordinate classification and dense prediction models. RTMO outperforms state-of-the-art one-stage pose estimators, achieving 1.1% higher AP on COCO while operating about 9 times faster with the same backbone. Our largest model, RTMO-l, attains 74.8% AP on COCO val2017 and 141 FPS on a single V100 GPU, demonstrating its efficiency and accuracy. The code and models are available at https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Person Pose Estimation | CrowdPose | RTMO-l | AP Easy | 88.8 | #1 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | RTMO-l | AP Hard | 77.2 | #1 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | RTMO-l | AP Medium | 84.7 | #1 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | RTMO-l | FPS | 52.4 | #1 of 28 | Archive leaderboard | report |
| Multi-Person Pose Estimation | CrowdPose | RTMO-l | mAP @0.5:0.95 | 83.8 | #1 of 28 | 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.
Methods
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