  Human pose estimation is an important topic in computer vision with many
applications including gesture and activity recognition. However, pose
estimation from image is challenging due to appearance variations, occlusions,
clutter background, and complex activities. To alleviate these problems, we
develop a robust pose estimation method based on the recent deep conv-deconv
modules with two improvements: (1) multi-scale supervision of body keypoints,
and (2) a global regression to improve structural consistency of keypoints. We
refine keypoint detection heatmaps using layer-wise multi-scale supervision to
better capture local contexts. Pose inference via keypoint association is
optimized globally using a regression network at the end. Our method can
effectively disambiguate keypoint matches in close proximity including the
mismatch of left-right body parts, and better infer occluded parts.
Experimental results show that our method achieves competitive performance
among state-of-the-art methods on the MPII and FLIC datasets.
