  We present a novel bipartite graph reasoning Generative Adversarial Network
(BiGraphGAN) for two challenging tasks: person pose and facial image synthesis.
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 long-range
cross relations between the source and target pose in a bipartite graph, which
mitigates some of the 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 a person's shape and
appearance in an interactive way. To further capture the change in pose of each
part more precisely, we propose a novel part-aware bipartite graph reasoning
(PBGR) block to decompose the task of reasoning the global structure
transformation with a bipartite graph into learning different local
transformations for different semantic body/face parts. Experiments on two
challenging generation tasks with three public datasets demonstrate the
effectiveness of the proposed methods in terms of objective quantitative scores
and subjective visual realness. The source code and trained models are
available at https://github.com/Ha0Tang/BiGraphGAN.
