  Chinese character synthesis involves two related aspects, i.e., style
maintenance and content consistency. Although some methods have achieved
remarkable success in synthesizing a character with specified style from
standard font, how to map characters to a specified style domain without losing
their identifiability remains very challenging. In this paper, we propose a
novel model named FontGAN, which integrates the character stylization and
de-stylization into a unified framework. In our model, we decouple character
images into style representation and content representation, which facilitates
more precise control of these two types of variables, thereby improving the
quality of the generated results. We also introduce two modules, namely, font
consistency module (FCM) and content prior module (CPM). FCM exploits a
category guided Kullback-Leibler loss to embedding the style representation
into different Gaussian distributions. It constrains the characters of the same
font in the training set globally. On the other hand, it enables our model to
obtain style variables through sampling in testing phase. CPM provides content
prior for the model to guide the content encoding process and alleviates the
problem of stroke deficiency during de-stylization. Extensive experimental
results on character stylization and de-stylization have demonstrated the
effectiveness of our method.
