Content and style disentanglement is an effective way to achieve few-shot font generation. It
allows to transfer the style of the font image in a source domain to the style defined with a …

Vector font synthesis is a challenging and ongoing problem in the fields of Computer Vision
and Computer Graphics. The recently-proposed DeepVecFont achieved state-of-the-art …

Font generation presents a significant challenge due to the intricate details needed,
especially for languages with complex ideograms and numerous characters, such as …

Text-to-image generation (TTI) refers to the usage of models that could process text input
and generate high fidelity images based on text descriptions. Text-to-image generation …

Automatic few-shot font generation (AFFG), aiming at generating new fonts with only a few
glyph references, reduces the labor cost of manually designing fonts. However, the …

Automatic font generation is an imitation task, which aims to create a font library that mimics
the style of reference images while preserving the content from source images. Although …

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 …

Text design is one of the most critical procedures in poster design, as it relies heavily on the
creativity and expertise of humans to design text images considering the visual harmony and …

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an
emerging topic in recent years due to the academic and commercial values. Typically, the …

The process of designing fonts traditionally requires a great deal of manpower, taking up to
one year to complete one style set. In computer vision (CV) and computer graphics (CG), the …