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