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.

翻译：近期关于二维姿态估计的研究在公共基准测试中取得了优异性能，但其在工业领域的应用仍面临模型参数量大、延迟高的问题。为弥合这一差距，我们基于MMPose框架，通过经验性探索姿态估计中的关键因素（包括范式、模型架构、训练策略及部署方案），提出了一种高性能实时多人姿态估计框架RTMPose。其中，RTMPose-m模型在COCO数据集上达到75.8%的AP，在Intel i7-11700 CPU上实现90+ FPS，在NVIDIA GTX 1660 Ti GPU上达到430+ FPS；RTMPose-l模型在COCO-WholeBody数据集上取得67.0%的AP，帧率达130+ FPS。为进一步评估RTMPose在关键实时应用中的能力，我们还报告了其在移动设备上的部署性能：RTMPose-s在骁龙865芯片上以70+ FPS运行，在COCO数据集上达到72.2%的AP，性能超越现有开源库。相关代码与模型已发布在https://github.com/open-mmlab/mmpose/tree/1.x/projects/rtmpose。