DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage
  CJK Character Generation
Yingtao Tian
Chinese, Japanese, and Korean (CJK), with a vast number of native speakers,
has 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 \emph{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.