RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose

13 Mar 2023  ·  Tao Jiang, Peng Lu, Li Zhang, Ningsheng Ma, Rui Han, Chengqi Lyu, Yining Li, Kai Chen ·

Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency. In order to bridge this gap, we empirically explore key factors in pose estimation including paradigm, model architecture, training strategy, and deployment, and present a high-performance real-time multi-person pose estimation framework, RTMPose, based on MMPose. Our RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS. To further evaluate RTMPose's capability in critical real-time applications, we also report the performance after deploying on the mobile device. Our RTMPose-s achieves 72.2% AP on COCO with 70+ FPS on a Snapdragon 865 chip, outperforming existing open-source libraries. Code and models are released at https://github.com/open-mmlab/mmpose/tree/1.x/projects/rtmpose.

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Results from the Paper


Ranked #3 on Pose Estimation on OCHuman (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
2D Human Pose Estimation COCO-WholeBody RTMPose WB 65.3 # 3
body 71.4 # 7
foot 69.2 # 5
face 88.9 # 1
hand 59.0 # 4
2D Human Pose Estimation Human-Art RTMPose-l AP 0.378 # 9
AP (gt bbox) 0.753 # 6
2D Human Pose Estimation Human-Art RTMPose-m AP 0.355 # 10
AP (gt bbox) 0.728 # 8
2D Human Pose Estimation Human-Art RTMPose-s AP 0.311 # 11
AP (gt bbox) 0.698 # 9
2D Human Pose Estimation Human-Art RTMPose-t AP 0.249 # 12
AP (gt bbox) 0.655 # 10
Pose Estimation OCHuman RTMPose(RTMPose-l, GT bounding boxes) Test AP 80.3 # 3
Validation AP 80.5 # 2

Methods


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