  Chinese character style transfer is a very challenging problem because of the
complexity of the glyph shapes or underlying structures and large numbers of
existed characters, when comparing with English letters. Moreover, the
handwriting of calligraphy masters has a more irregular stroke and is difficult
to obtain in real-world scenarios. Recently, several GAN-based methods have
been proposed for font synthesis, but some of them require numerous reference
data and the other part of them have cumbersome preprocessing steps to divide
the character into different parts to be learned and transferred separately. In
this paper, we propose a simple but powerful end-to-end Chinese calligraphy
font generation framework ZiGAN, which does not require any manual operation or
redundant preprocessing to generate fine-grained target-style characters with
few-shot references. To be specific, a few paired samples from different
character styles are leveraged to attain a fine-grained correlation between
structures underlying different glyphs. To capture valuable style knowledge in
target and strengthen the coarse-grained understanding of character content, we
utilize multiple unpaired samples to align the feature distributions belonging
to different character styles. By doing so, only a few target Chinese
calligraphy characters are needed to generated expected style transferred
characters. Experiments demonstrate that our method has a state-of-the-art
generalization ability in few-shot Chinese character style transfer.
