  We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-up parsing step that maintains high accuracy while achieving
realtime performance, irrespective of the number of people in the image. The
architecture is designed to jointly learn part locations and their association
via two branches of the same sequential prediction process. Our method placed
first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds
the previous state-of-the-art result on the MPII Multi-Person benchmark, both
in performance and efficiency.
