FontNet: Closing the gap to font designer performance in font synthesis
This addresses the problem of automating font design for designers and developers, reducing labor and expertise requirements, though it is incremental as it builds on existing synthesis methods.
The paper tackles font synthesis by learning font styles in an embedding space, enabling generation of high-resolution fonts for any language system without requiring fine-tuning for unobserved styles, and it outperforms state-of-the-art methods in experiments.
Font synthesis has been a very active topic in recent years because manual font design requires domain expertise and is a labor-intensive and time-consuming job. While remarkably successful, existing methods for font synthesis have major shortcomings; they require finetuning for unobserved font style with large reference images, the recent few-shot font synthesis methods are either designed for specific language systems or they operate on low-resolution images which limits their use. In this paper, we tackle this font synthesis problem by learning the font style in the embedding space. To this end, we propose a model, called FontNet, that simultaneously learns to separate font styles in the embedding space where distances directly correspond to a measure of font similarity, and translates input images into the given observed or unobserved font style. Additionally, we design the network architecture and training procedure that can be adopted for any language system and can produce high-resolution font images. Thanks to this approach, our proposed method outperforms the existing state-of-the-art font generation methods on both qualitative and quantitative experiments.