Generative Partition Networks for Multi-Person Pose Estimation
This addresses the problem of efficient and accurate pose estimation in multi-person scenarios for computer vision applications, representing a novel hybrid approach rather than an incremental improvement.
The paper tackles multi-person pose estimation by introducing a Generative Partition Network (GPN) that generates partitions for multiple persons from global joint candidates and infers instance-specific joint configurations simultaneously, achieving new state-of-the-art performance on benchmarks like MPII Human Pose Multi-Person.
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.