  Whole-body pose estimation localizes the human body, hand, face, and foot
keypoints in an image. This task is challenging due to multi-scale body parts,
fine-grained localization for low-resolution regions, and data scarcity.
Meanwhile, applying a highly efficient and accurate pose estimator to widely
human-centric understanding and generation tasks is urgent. In this work, we
present a two-stage pose \textbf{D}istillation for \textbf{W}hole-body
\textbf{P}ose estimators, named \textbf{DWPose}, to improve their effectiveness
and efficiency. The first-stage distillation designs a weight-decay strategy
while utilizing a teacher's intermediate feature and final logits with both
visible and invisible keypoints to supervise the student from scratch. The
second stage distills the student model itself to further improve performance.
Different from the previous self-knowledge distillation, this stage finetunes
the student's head with only 20% training time as a plug-and-play training
strategy. For data limitations, we explore the UBody dataset that contains
diverse facial expressions and hand gestures for real-life applications.
Comprehensive experiments show the superiority of our proposed simple yet
effective methods. We achieve new state-of-the-art performance on
COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from
64.8% to 66.5%, even surpassing RTMPose-x teacher with 65.3% AP. We release a
series of models with different sizes, from tiny to large, for satisfying
various downstream tasks. Our codes and models are available at
https://github.com/IDEA-Research/DWPose.
