Generative Partition Networks for Multi-Person Pose Estimation

Understand

This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem.

• Different from existing models that are either completely top-down or bottom-up, the proposed GPN introduces a novel strategy--it generates partitions for multiple persons from their global joint candidates and infers instance-specific joint configurations simultaneously.
• The GPN is favorably featured by low complexity and high accuracy of joint detection and re-organization.
• In particular, GPN designs a generative model that performs one feed-forward pass to efficiently generate robust person detections with joint partitions, relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons.