Artistic text generation aims to amplify the aesthetic qualities of text while maintaining
readability. It can make the text more attractive and better convey its expression, thus …

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 …

In machine learning, generative modeling aims to learn to generate new data statistically
similar to the training data distribution. In this paper, we survey learning generative models …

Few-shot font generation (FFG) produces stylized font images with a limited number of
reference samples which can significantly reduce labor costs in manual font designs. Most …

In this paper, we propose a new cross-lingual font style transfer model, FCAGAN, which
enables font style transfer between different languages by observing a small number of …

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 …

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 …

Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle
bones-has become a pivotal field intersecting archaeology, paleography, and historical …

While large language models (LLMs) have revolutionized natural language processing with
their task-agnostic capabilities, visual generation tasks such as image translation, style …

Fonts have huge variations in their styles and give readers different impressions. Therefore,
generating new fonts is worthy of giving new impressions to readers. In this paper, we …