We present a realtime approach for multi-person 2D pose estimation that predicts vector fields, which we refer to as Part Affinity Fields (PAFs), that directly expose the association between anatomical parts in an image. The architecture is designed to jointly learn part locations and their association, via two branches of the same sequential prediction process. The sequential prediction enables the part confidence maps and the association fields to encode global context, while allowing an efficient bottom-up parsing step that maintains tractable runtime complexity. Our method has set the state-of-the-art performance on the inaugural MSCOCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
['Zhe Cao', 'Tomas Simon', 'Shih-En Wei', 'Yaser Sheikh']
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields