  Multi-person pose estimation (MPPE) presents a formidable yet crucial
challenge in computer vision. Most existing methods predominantly concentrate
on isolated interaction either between instances or joints, which is inadequate
for scenarios demanding concurrent localization of both instances and joints.
This paper introduces a novel CNN-based single-stage method, named Dual-path
Hierarchical Relation Network (DHRNet), to extract instance-to-joint and
joint-to-instance interactions concurrently. Specifically, we design a
dual-path interaction modeling module (DIM) that strategically organizes
cross-instance and cross-joint interaction modeling modules in two
complementary orders, enriching interaction information by integrating merits
from different correlation modeling branches. Notably, DHRNet excels in joint
localization by leveraging information from other instances and joints.
Extensive evaluations on challenging datasets, including COCO, CrowdPose, and
OCHuman datasets, showcase DHRNet's state-of-the-art performance. The code will
be released at https://github.com/YHDang/dhrnet-multi-pose-estimation.
