FontTransformer: Few-shot High-resolution Chinese Glyph Image Synthesis via Stacked Transformers

TL;DR

FontTransformer introduces a stacked Transformer-based model for high-resolution Chinese font synthesis from few samples, effectively improving quality and structural accuracy over existing low-resolution methods.

Contribution

The paper presents a novel stacked Transformer architecture and encoding scheme for high-resolution, few-shot Chinese font generation, surpassing prior low-resolution approaches.

Findings

Outperforms existing methods in quality and accuracy

Generates high-resolution, visually-pleasing glyphs

Effective with very limited training samples

Abstract

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.