  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.
