DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage CJK Character Generation
<|reference_start|>DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage CJK Character Generation: Chinese, Japanese, and Korean (CJK), with a vast number of native speakers, have profound influence on society and culture. The typesetting of CJK languages carries a wide range of requirements due to the complexity of their scripts and unique literary traditions. A critical aspect of this typesetting process is that CJK fonts need to provide a set of consistent-looking glyphs for approximately one hundred thousand characters. However, creating such a font is inherently labor-intensive and expensive, which significantly hampers the development of new CJK fonts for typesetting, historical, aesthetic, or artistic purposes. To bridge this gap, we are motivated by recent advancements in diffusion-based generative models and propose a novel diffusion method for generating glyphs in a targeted style from a single conditioned, standard glyph form. Our experiments show that our method is capable of generating fonts of both printed and hand-written styles, the latter of which presents a greater challenge. Moreover, our approach shows remarkable zero-shot generalization capabilities for non-CJK but Chinese-inspired scripts. We also show our method facilitates smooth style interpolation and generates bitmap images suitable for vectorization, which is crucial in the font creation process. In summary, our proposed method opens the door to high-quality, generative model-assisted font creation for CJK characters, for both typesetting and artistic endeavors.<|reference_end|>
@article{tian2024diffcjk:,
    title={DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage
  CJK Character Generation},
    author={Yingtao Tian},
    journal={arXiv preprint arXiv:2404.05212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05212},
    primaryClass={cs.CV}
}