  Human pose estimation in video relies on local information by either
estimating each frame independently or tracking poses across frames. In this
paper, we propose a novel method combining local approaches with global
context. We introduce a light weighted, unsupervised, key frame proposal
network (K-FPN) to select informative frames and a learned dictionary to
recover the entire pose sequence from these frames. The K-FPN speeds up the
pose estimation and provides robustness to bad frames with occlusion, motion
blur, and illumination changes, while the learned dictionary provides global
dynamic context. Experiments on Penn Action and sub-JHMDB datasets show that
the proposed method achieves state-of-the-art accuracy, with substantial
speed-up.
