  In this paper, we present the Intra- and Inter-Human Relation Networks
(I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules.
First, the Intra-Human Relation Module operates on a single person and aims to
capture Intra-Human dependencies. Second, the Inter-Human Relation Module
considers the relation between multiple instances and focuses on capturing
Inter-Human interactions. The Inter-Human Relation Module can be designed very
lightweight by reducing the resolution of feature map, yet learn useful
relation information to significantly boost the performance of the Intra-Human
Relation Module. Even without bells and whistles, our method can compete or
outperform current competition winners. We conduct extensive experiments on
COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the
proposed model surpasses all the state-of-the-art methods. Concretely, the
proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman
dataset respectively, outperforming existing methods by a large margin.
Additionally, the ablation study and visualization analysis also prove the
effectiveness of our model.
