  Realtime multi-person 2D pose estimation is a key component in enabling
machines to have an understanding of people in images and videos. In this work,
we present a realtime approach to detect the 2D pose of multiple people in an
image. The proposed method uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. This bottom-up system achieves high accuracy and
realtime performance, regardless of the number of people in the image. In
previous work, PAFs and body part location estimation were refined
simultaneously across training stages. We demonstrate that a PAF-only
refinement rather than both PAF and body part location refinement results in a
substantial increase in both runtime performance and accuracy. We also present
the first combined body and foot keypoint detector, based on an internal
annotated foot dataset that we have publicly released. We show that the
combined detector not only reduces the inference time compared to running them
sequentially, but also maintains the accuracy of each component individually.
This work has culminated in the release of OpenPose, the first open-source
realtime system for multi-person 2D pose detection, including body, foot, hand,
and facial keypoints.
