Few-shot Font Generation with Localized Style Representations and Factorization


  • Song Park Yonsei University
  • Sanghyuk Chun NAVER AI LAB
  • Junbum Cha NAVER CLOVA
  • Bado Lee NAVER CLOVA
  • Hyunjung Shim Yonsei University




Computational Photography, Image & Video Synthesis


Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot font generation methods aim to learn to disentangle the style and content element from a few reference glyphs, and mainly focus on a universal style representation for each font style. However, such approach limits the model in representing diverse local styles, and thus makes it unsuitable to the most complicated letter system, e.g., Chinese, whose characters consist of a varying number of components (often called ``radical'') with a highly complex structure. In this paper, we propose a novel font generation method by learning localized styles, namely component-wise style representations, instead of universal styles. The proposed style representations enable us to synthesize complex local details in text designs. However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e.g., over 200 for Chinese. To reduce the number of reference glyphs, we simplify component-wise styles by a product of component factor and style factor, inspired by low-rank matrix factorization. Thanks to the combination of strong representation and a compact factorization strategy, our method shows remarkably better few-shot font generation results (with only 8 reference glyph images) than other state-of-the-arts, without utilizing strong locality supervision, e.g., location of each component, skeleton, or strokes. The source code is available at https://github.com/clovaai/lffont.




How to Cite

Park, S., Chun, S., Cha, J., Lee, B., & Shim, H. (2021). Few-shot Font Generation with Localized Style Representations and Factorization. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2393-2402. https://doi.org/10.1609/aaai.v35i3.16340



AAAI Technical Track on Computer Vision II