Bipartite graph reasoning GANs for person pose and facial image synthesis
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. Specific...
| Main Authors: | , , , |
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| 格式: | Conference item |
| 语言: | English |
| 出版: |
Springer
2022
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| 总结: | 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-andaggregation (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. |
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