Few-shot Compositional Font Generation with Dual Memory
This addresses the labor-intensive task of font generation for glyph-rich scripts, offering a practical solution for designers and developers, though it is incremental as it builds on existing font generation methods.
The paper tackles the problem of generating high-quality font libraries for compositional scripts with only a few reference samples, achieving significantly better quality and faithful stylization compared to state-of-the-art methods in experiments on Korean-handwriting and Thai-printing fonts.
Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with only a few samples. In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components. By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples. We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization compared to the state-of-the-art generation methods quantitatively and qualitatively. Source code is available at https://github.com/clovaai/dmfont.