  Bottom-up approaches for image-based multi-person pose estimation consist of
two stages: (1) keypoint detection and (2) grouping of the detected keypoints
to form person instances. Current grouping approaches rely on learned embedding
from only visual features that completely ignore the spatial configuration of
human poses. In this work, we formulate the grouping task as a graph
partitioning problem, where we learn the affinity matrix with a Graph Neural
Network (GNN). More specifically, we design a Geometry-aware Association GNN
that utilizes spatial information of the keypoints and learns local affinity
from the global context. The learned geometry-based affinity is further fused
with appearance-based affinity to achieve robust keypoint association. Spectral
clustering is used to partition the graph for the formation of the pose
instances. Experimental results on two benchmark datasets show that our
proposed method outperforms existing appearance-only grouping frameworks, which
shows the effectiveness of utilizing spatial context for robust grouping.
Source code is available at: https://github.com/jiahaoLjh/PoseGrouping.
