Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation

Understand

In this paper, we propose a novel multi-stage network architecture with two branches in each stage to estimate multi-person poses in images.

• The first branch predicts the confidence maps of joints and uses a geometrical transform kernel to propagate information between neighboring joints at the confidence level.
• The second branch proposes a bi-directional graph structure information model (BGSIM) to encode rich contextual information and to infer the occlusion relationship among different joints.
• We dynamically determine the joint point with highest response of the confidence maps as base point of passing message in BGSIM.