Info
- Title: GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
- Task: Font Generation
- Author: H. Hayashi, K. Abe, and S. Uchida
- Date: May 2019
- Arxiv: 1905.12502
Highlights & Drawbacks
- Two encode vectors for character and style, respectively
Motivation & Design
The main frame work is a DCGAN, with Wasserstein distance as discriminator Loss. Notice that char-ID is encoded with a saperate one-hot vector as priors applied to random vector for generator.
Performance & Ablation Study
legibility evaluation
Style Consistency
The metric of style consistency is defined as: where is the number of generated images (we used in this experiment), is the number of character classes, i.e.,, is the distance between the generated font and the nearest real font, and is the average of over . The metric C is the averaged coefficient of variation of , and represents an intra-style variation of the generated font images. The lower is, the higher style consistency is.