Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis
A novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.
Main Characteristic
The proposed graph generator consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way.
Research areas
Integrative AI
Technical Categories
Computer vision
Last updated
26.01.2024 - 10:15