  Whole-body pose estimation is a challenging task that requires simultaneous
prediction of keypoints for the body, hands, face, and feet. Whole-body pose
estimation aims to predict fine-grained pose information for the human body,
including the face, torso, hands, and feet, which plays an important role in
the study of human-centric perception and generation and in various
applications. In this work, we present RTMW (Real-Time Multi-person Whole-body
pose estimation models), a series of high-performance models for 2D/3D
whole-body pose estimation. We incorporate RTMPose model architecture with FPN
and HEM (Hierarchical Encoding Module) to better capture pose information from
different body parts with various scales. The model is trained with a rich
collection of open-source human keypoint datasets with manually aligned
annotations and further enhanced via a two-stage distillation strategy. RTMW
demonstrates strong performance on multiple whole-body pose estimation
benchmarks while maintaining high inference efficiency and deployment
friendliness. We release three sizes: m/l/x, with RTMW-l achieving a 70.2 mAP
on the COCO-Wholebody benchmark, making it the first open-source model to
exceed 70 mAP on this benchmark. Meanwhile, we explored the performance of RTMW
in the task of 3D whole-body pose estimation, conducting image-based monocular
3D whole-body pose estimation in a coordinate classification manner. We hope
this work can benefit both academic research and industrial applications. The
code and models have been made publicly available at:
https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose
