JointFontGAN: Joint Geometry-Content GAN for Font Generation via Few-Shot Learning

Yankun Xi, Guoli Yan, Jing Hua, Zichun Zhong

Abstract: Automatic generation of font and text design in the wild is a challenging task since font and text in real world exhibit various visual effects. In this paper, we propose a novel model, JointFontGAN, to derive fonts, including both geometric structures and shape contents in correctness and consistency with very few font samples available. Specifically, we design an end-to-end deep learning based approach for font generation through the new multi-stream extended conditional generative adversarial network (XcGAN) models, which jointly learn and generate both font skeleton and glyph representations simultaneously. It can adapt to the geometric variability and content scalability at the neural network level. Then, we apply it, along with the developed efficient and effective one-stage model, to text generations in letters and sentences / paragraphs with both standard and artistic / handwriting styles. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on the collected datasets including 20K fonts (letters and punctuations) with different styles.