  We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the
challenging person image generation task. The proposed graph generator mainly
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. Experiments on two challenging and public datasets, i.e.,
Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN
in terms of objective quantitative scores and subjective visual realness. The
source code and trained models are available at
https://github.com/Ha0Tang/BiGraphGAN.
