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Automatic few-shot font generation aims to solve a well-defined, real-worldproblem because manual font designs are expensive and sensitive to theexpertise of designers. Existing methods learn to disentangle style and contentelements by developing a universal style representation for each font style.However, this approach limits the model in representing diverse local styles,because it is unsuitable for complicated letter systems, for example, Chinese,whose characters consist of a varying number of components (often called"radical") -- with a highly complex structure. In this paper, we propose anovel font generation method that learns localized styles, namelycomponent-wise style representations, instead of universal styles. The proposedstyle representations enable the synthesis of complex local details in textdesigns. However, learning component-wise styles solely from a few referenceglyphs is infeasible when a target script has a large number of components, forexample, over 200 for Chinese. To reduce the number of required referenceglyphs, we represent component-wise styles by a product of component and stylefactors, inspired by low-rank matrix factorization. Owing to the combination ofstrong representation and a compact factorization strategy, our method showsremarkably better few-shot font generation results (with only eight referenceglyphs) than other state-of-the-art methods. Moreover, strong localitysupervision, for example, location of each component, skeleton, or strokes, wasnot utilized. The source code is available at https://github.com/clovaai/lffontand https://github.com/clovaai/fewshot-font-generation.

Few-shot Font Generation with Weakly Supervised Localized Representations