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Automatic font generation remains a challenging research issue due to thelarge amounts of characters with complicated structures. Typically, only a fewsamples can serve as the style/content reference (termed few-shot learning),which further increases the difficulty to preserve local style patterns ordetailed glyph structures. We investigate the drawbacks of previous studies andfind that a coarse-grained discriminator is insufficient for supervising a fontgenerator. To this end, we propose a novel Component-Aware Module (CAM), whichsupervises the generator to decouple content and style at a more fine-grainedlevel, i.e., the component level. Different from previous studies struggling toincrease the complexity of generators, we aim to perform more effectivesupervision for a relatively simple generator to achieve its full potential,which is a brand new perspective for font generation. The whole frameworkachieves remarkable results by coupling component-level supervision withadversarial learning, hence we call it Component-Guided GAN, shortly CG-GAN.Extensive experiments show that our approach outperforms state-of-the-artone-shot font generation methods. Furthermore, it can be applied to handwrittenword synthesis and scene text image editing, suggesting the generalization ofour approach.

Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator