  Human pose estimation has seen widespread use of transformer models in recent
years. Pose transformers benefit from the self-attention map, which captures
the correlation between human joint tokens and the image. However, training
such models is computationally expensive. The recent token-Pruned Pose
Transformer (PPT) solves this problem by pruning the background tokens of the
image, which are usually less informative. However, although it improves
efficiency, PPT inevitably leads to worse performance than TokenPose due to the
pruning of tokens. To overcome this problem, we present a novel method called
Distilling Pruned-Token Transformer for human pose estimation (DPPT). Our
method leverages the output of a pre-trained TokenPose to supervise the
learning process of PPT. We also establish connections between the internal
structure of pose transformers and PPT, such as attention maps and joint
features. Our experimental results on the MPII datasets show that our DPPT can
significantly improve PCK compared to previous PPT models while still reducing
computational complexity.
