  The automatic generation of Chinese fonts is an important problem involved in
many applications. The predominated methods for the Chinese font generation are
based on the deep generative models, especially the generative adversarial
networks (GANs). However, existing GAN-based methods (say, CycleGAN) for the
Chinese font generation usually suffer from the mode collapse issue, mainly due
to the lack of effective guidance information. This paper proposes a novel
information guidance module called the skeleton guided channel expansion (SGCE)
module for the Chinese font generation through integrating the skeleton
information into the generator with the channel expansion way, motivated by the
observation that the skeleton embodies both local and global structure
information of Chinese characters. We conduct extensive experiments to show the
effectiveness of the proposed module. Numerical results show that the mode
collapse issue suffered by the known CycleGAN can be effectively alleviated by
equipping with the proposed SGCE module, and the CycleGAN equipped with SGCE
outperforms the state-of-the-art models in terms of four important evaluation
metrics and visualization quality. Besides CycleGAN, we also show that the
suggested SGCE module can be adapted to other models for Chinese font
generation as a plug-and-play module to further improve their performance.
