  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. In addition, GPN formulates the inference procedure for joint
configurations of human poses as a graph partition problem, and conducts local
optimization for each person detection with reliable global affinity cues,
leading to complexity reduction and performance improvement. GPN is implemented
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 GPN with new state-of-the-art performance.
