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 by effectively transferring the pose structure knowledge of a strong teacher. 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 the standard benchmark datasets, MPII Human Pose and Leeds Sports Pose.

本研究提出一种新的姿势模型学习策略，即快速姿势蒸馏（Fast Pose Distillation，FPD），旨在提高人体姿势估计模型的效率和可扩展性，并通过将强教师网络的姿势结构知识有效地转移给轻型姿势神经网络体系结构来实现，并在两个标准基准数据集MPII和Leeds Sports Pose中广泛评估，证明了FPD方法在模型节约成本方面优于广泛的最新姿势估计方法。

快速人体姿势估计