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.

本文介绍了一种名为双通道分层关系网络 (DHRNet) 的新型基于卷积神经网络的单阶段方法，能够同时提取实例到关节和关节到实例之间的交互信息，通过整合不同相关建模分支的优点，丰富了交互信息，并且通过利用其他实例和关节的信息在关节定位方面表现出色，对于多人姿势估计 (MPPE) 提出了一种有效的解决方案。

DHRNet：用于多人姿势估计的双路径层次关系网络