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Previous works on font generation mainly focus on the standard print fontswhere character's shape is stable and strokes are clearly separated. There israre research on brush handwriting font generation, which involves holisticstructure changes and complex strokes transfer. To address this issue, wepropose a novel GAN-based image translation model by integrating the skeletoninformation. We first extract the skeleton from training images, then design animage encoder and a skeleton encoder to extract corresponding features. Aself-attentive refined attention module is devised to guide the model to learndistinctive features between different domains. A skeleton discriminator isinvolved to first synthesize the skeleton image from the generated image with apre-trained generator, then to judge its realness to the target one. We alsocontribute a large-scale brush handwriting font image dataset with six stylesand 15,000 high-resolution images. Both quantitative and qualitativeexperimental results demonstrate the competitiveness of our proposed model.

SE-GAN: Skeleton Enhanced GAN-based Model for Brush Handwriting Font Generation