  Previous works on font generation mainly focus on the standard print fonts
where character's shape is stable and strokes are clearly separated. There is
rare research on brush handwriting font generation, which involves holistic
structure changes and complex strokes transfer. To address this issue, we
propose a novel GAN-based image translation model by integrating the skeleton
information. We first extract the skeleton from training images, then design an
image encoder and a skeleton encoder to extract corresponding features. A
self-attentive refined attention module is devised to guide the model to learn
distinctive features between different domains. A skeleton discriminator is
involved to first synthesize the skeleton image from the generated image with a
pre-trained generator, then to judge its realness to the target one. We also
contribute a large-scale brush handwriting font image dataset with six styles
and 15,000 high-resolution images. Both quantitative and qualitative
experimental results demonstrate the competitiveness of our proposed model.
