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Font synthesis has been a very active topic in recent years because manualfont design requires domain expertise and is a labor-intensive andtime-consuming job. While remarkably successful, existing methods for fontsynthesis have major shortcomings; they require finetuning for unobserved fontstyle with large reference images, the recent few-shot font synthesis methodsare either designed for specific language systems or they operate onlow-resolution images which limits their use. In this paper, we tackle thisfont synthesis problem by learning the font style in the embedding space. Tothis end, we propose a model, called FontNet, that simultaneously learns toseparate font styles in the embedding space where distances directly correspondto a measure of font similarity, and translates input images into the givenobserved or unobserved font style. Additionally, we design the networkarchitecture and training procedure that can be adopted for any language systemand can produce high-resolution font images. Thanks to this approach, ourproposed method outperforms the existing state-of-the-art font generationmethods on both qualitative and quantitative experiments.

FontNet: Closing the gap to font designer performance in font synthesis