Multi-person pose estimation is challenging because it localizes body
keypoints for multiple persons simultaneously. Previous methods can be divided
into two streams, i.e. top-down and bottom-up methods. The top-down methods
localize keypoints after human detection, while the bottom-up methods localize
keypoints directly and then cluster/group them for different persons, which are
generally more efficient than top-down methods. However, in existing bottom-up
methods, the keypoint grouping is usually solved independently from keypoint
detection, making them not end-to-end trainable and have sub-optimal
performance. In this paper, we investigate a new perspective of human part
grouping and reformulate it as a graph clustering task. Especially, we propose
a novel differentiable Hierarchical Graph Grouping (HGG) method to learn the
graph grouping in bottom-up multi-person pose estimation task. Moreover, HGG is
easily embedded into main-stream bottom-up methods. It takes human keypoint
candidates as graph nodes and clusters keypoints in a multi-layer graph neural
network model. The modules of HGG can be trained end-to-end with the keypoint
detection network and is able to supervise the grouping process in a
hierarchical manner. To improve the discrimination of the clustering, we add a
set of edge discriminators and macro-node discriminators. Extensive experiments
on both COCO and OCHuman datasets demonstrate that the proposed method improves
the performance of bottom-up pose estimation methods.

本文提出了一种新的人体部分聚类方法，即可学习的分层图聚类 (HGG) 方法，用于底层多人姿态估计任务，并证明该方法可以提高底层姿态估计方法的性能。