  In the field of human pose estimation, regression-based methods have been
dominated in terms of speed, while heatmap-based methods are far ahead in terms
of performance. How to take advantage of both schemes remains a challenging
problem. In this paper, we propose a novel human pose estimation framework
termed DistilPose, which bridges the gaps between heatmap-based and
regression-based methods. Specifically, DistilPose maximizes the transfer of
knowledge from the teacher model (heatmap-based) to the student model
(regression-based) through Token-distilling Encoder (TDE) and Simulated
Heatmaps. TDE aligns the feature spaces of heatmap-based and regression-based
models by introducing tokenization, while Simulated Heatmaps transfer explicit
guidance (distribution and confidence) from teacher heatmaps into student
models. Extensive experiments show that the proposed DistilPose can
significantly improve the performance of the regression-based models while
maintaining efficiency. Specifically, on the MSCOCO validation dataset,
DistilPose-S obtains 71.6% mAP with 5.36M parameter, 2.38 GFLOPs and 40.2 FPS,
which saves 12.95x, 7.16x computational cost and is 4.9x faster than its
teacher model with only 0.9 points performance drop. Furthermore, DistilPose-L
obtains 74.4% mAP on MSCOCO validation dataset, achieving a new
state-of-the-art among predominant regression-based models.
