VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and
  Quantization
Mingshuai Yao Yabo Zhang Xianhui Lin Xiaoming Li Wangmeng Zuo
Few-shot font generation is challenging, as it needs to capture the
fine-grained stroke styles from a limited set of reference glyphs, and then
transfer to other characters, which are expected to have similar styles.
However, due to the diversity and complexity of Chinese font styles, the
synthesized glyphs of existing methods usually exhibit visible artifacts, such
as missing details and distorted strokes. In this paper, we propose a
VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token
prior refinement and structure-aware enhancement. Specifically, we pre-train a
VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font
refines the synthesized glyphs with the codebook to eliminate the domain gap
between synthesized and real-world strokes. Furthermore, our VQ-Font leverages
the inherent design of Chinese characters, where structure components such as
radicals and character components are combined in specific arrangements, to
recalibrate fine-grained styles based on references. This process improves the
matching and fusion of styles at the structure level. Both modules collaborate
to enhance the fidelity of the generated fonts. Experiments on a collected font
dataset show that our VQ-Font outperforms the competing methods both
quantitatively and qualitatively, especially in generating challenging styles.