FontGAN: A Unified Generative Framework for Chinese Character Stylization and De-stylization
This addresses the problem of precise style control in Chinese character synthesis for applications like font design, though it appears incremental by building on existing generative methods.
The paper tackles the challenge of synthesizing Chinese characters with specified styles while maintaining content consistency, proposing FontGAN to unify stylization and de-stylization, which improves generation quality as shown in experiments.
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.