  Human pose estimation from image and video is a vital task in many multimedia
applications. Previous methods achieve great performance but rarely take
efficiency into consideration, which makes it difficult to implement the
networks on resource-constrained devices. Nowadays real-time multimedia
applications call for more efficient models for better interactions. Moreover,
most deep neural networks for pose estimation directly reuse the networks
designed for image classification as the backbone, which are not yet optimized
for the pose estimation task. In this paper, we propose an efficient framework
targeted at human pose estimation including two parts, the efficient backbone
and the efficient head. By implementing the differentiable neural architecture
search method, we customize the backbone network design for pose estimation and
reduce the computation cost with negligible accuracy degradation. For the
efficient head, we slim the transposed convolutions and propose a spatial
information correction module to promote the performance of the final
prediction. In experiments, we evaluate our networks on the MPII and COCO
datasets. Our smallest model has only 0.65 GFLOPs with 88.1% PCKh@0.5 on MPII
and our large model has only 2 GFLOPs while its accuracy is competitive with
the state-of-the-art large model, i.e., HRNet with 9.5 GFLOPs.
