Character Image Synthesis Based on Selected Content and Referenced Style Embedding
Anna Zhu, Qiyang Zhang, Xiongbo Lu, Shengwu Xiong · 2019
Arbitrary characters synthesis based on a few referenced examples poses a great challenge due to the diversity of characters category and style. We regard this problem as image translation problem and propose a character style transfer network consisting of content selector, style encoder, content encoder, feature embedding and embedded feature decoder to solve it. The content selector is used to select and match the most similar content (i.e., font) from our collected glyph dataset as content references. Then, we apply the style encoder and content encoder to extract the style and content representation separately and mix them for feature embedding. Finally, the embedded features are decoded to generate the target characters. We train them in an end-to-end manner and evaluate the proposed method on MC-GAN dataset and our collected dataset. The experimental results have demonstrated the effectiveness of the proposed model for character synthesis.