Learning Component-Level and Inter-Class Glyph Representation for few-shot Font Generation

Yongliang Su, Xu Chen, Lei Wu, Xiangxu Meng

Abstract: Few-shot font generation (FFG) has received increasing attention recently. However, due to the complex structure of Chinese characters, most existing methods suffer from missing component-level content details and inaccurate capture of content-independent style representations. In this paper, we proposed a novel generative adversarial network for FFG by learning component information and inter-class glyph style representation. Specifically, we proposed a Content-Component Aware Module (CCAM) to help the model learn the accurate content representation by using component images, which is a brand new perspective. In addition, we employed a Glyph Style Contrastive Enhance (GSCE) strategy to help the encoder learn the differences of inter-class glyph style while ignoring the influence of reference character content. Both qualitative and quantitative experiments show our method achieves state-of-the-art performance in terms of the content structure integrity and style accuracy of the glyph.