  Automatic generation of high-quality Chinese fonts from a few online training
samples is a challenging task, especially when the amount of samples is very
small. Existing few-shot font generation methods can only synthesize
low-resolution glyph images that often possess incorrect topological structures
or/and incomplete strokes. To address the problem, this paper proposes
FontTransformer, a novel few-shot learning model, for high-resolution Chinese
glyph image synthesis by using stacked Transformers. The key idea is to apply
the parallel Transformer to avoid the accumulation of prediction errors and
utilize the serial Transformer to enhance the quality of synthesized strokes.
Meanwhile, we also design a novel encoding scheme to feed more glyph
information and prior knowledge to our model, which further enables the
generation of high-resolution and visually-pleasing glyph images. Both
qualitative and quantitative experimental results demonstrate the superiority
of our method compared to other existing approaches in the few-shot Chinese
font synthesis task.
