  Existing human pose estimation approaches often only consider how to improve
the model generalisation performance, but putting aside the significant
efficiency problem. This leads to the development of heavy models with poor
scalability and cost-effectiveness in practical use. In this work, we
investigate the under-studied but practically critical pose model efficiency
problem. To this end, we present a new Fast Pose Distillation (FPD) model
learning strategy. Specifically, the FPD trains a lightweight pose neural
network architecture capable of executing rapidly with low computational cost.
It is achieved by effectively transferring the pose structure knowledge of a
strong teacher network. Extensive evaluations demonstrate the advantages of our
FPD method over a broad range of state-of-the-art pose estimation approaches in
terms of model cost-effectiveness on two standard benchmark datasets, MPII
Human Pose and Leeds Sports Pose.
