Abstract. This paper proposes a novel Pose Partition Network (PPN)
to address the challenging multi-person pose estimation problem. The
proposed PPN is favorably featured by low complexity and high accuracy of joint detection and partition. In particular, PPN performs
dense regressions from global joint candidates within a specific embedding space, which is parameterized by centroids of persons, to efficiently
generate robust person detection and joint partition. Then, PPN infers
body joint configurations through conducting graph partition for each
person detection locally, utilizing reliable global affinity cues. In this
way, PPN reduces computation complexity and improves multi-person
pose estimation significantly. We implement PPN with the Hourglass
architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII
Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF
show the efficiency of PPN with new state-of-the-art performance.