  Automatically writing stylized Chinese characters is an attractive yet
challenging task due to its wide applicabilities. In this paper, we propose a
novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
generate Chinese characters. Specifically, we propose to capture the different
characteristics of a Chinese character by disentangling the latent features
into content-related and style-related components. Considering of the complex
shapes and structures, we incorporate the structure information as prior
knowledge into our framework to guide the generation. Our framework shows a
powerful one-shot/low-shot generalization ability by inferring the style
component given a character with unseen style. To the best of our knowledge,
this is the first attempt to learn to write new-style Chinese characters by
observing only one or a few examples. Extensive experiments demonstrate its
effectiveness in generating different stylized Chinese characters by fusing the
feature vectors corresponding to different contents and styles, which is of
significant importance in real-world applications.
