# GAS-NeXt: Few-Shot Cross-Lingual Font Generator

arXiv: https://arxiv.org/abs/2212.02886 · HTML: https://mlsanity.com/paper/2212.02886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 22 of 22)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [FIGR: Few-shot Image Generation with Reptile](https://mlsanity.com/paper/1901.02199/) (2019)
- [Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning](https://mlsanity.com/paper/1910.04987/) (2019)
- [Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts](https://mlsanity.com/paper/2104.00887/) (2021)
- [Few-shot Compositional Font Generation with Dual Memory](https://mlsanity.com/paper/2005.10510/) (2020)
- [Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator](https://mlsanity.com/paper/2205.00146/) (2022)
- [CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator](https://mlsanity.com/paper/2005.12500/) (2020)
- [TET-GAN: Text Effects Transfer via Stylization and Destylization](https://mlsanity.com/paper/1812.06384/) (2018)
- [Neural Style Transfer for Audio Spectograms](https://mlsanity.com/paper/1801.01589/) (2018)
- [StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding](https://mlsanity.com/paper/2012.08687/) (2020)
- [GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks](https://mlsanity.com/paper/1905.12502/) (2019)
- [MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis](https://mlsanity.com/paper/1805.01123/) (2018)
- [Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders](https://mlsanity.com/paper/2004.01864/) (2020)

## Abstract (arXiv)

Generating new fonts is a time-consuming and labor-intensive task, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style, but few models support cross-lingual font generation. This paper presents GAS-NeXt, a novel few-shot cross-lingual font generator based on AGIS-Net and Font Translator GAN, and improve the performance metrics such as Fr'echet Inception Distance (FID), Structural Similarity Index Measure(SSIM), and Pixel-level Accuracy (pix-acc). Our approaches include replacing the original encoder and decoder with the idea of layer attention and context-aware attention from Font Translator GAN, while utilizing the shape, texture, and local discriminators of AGIS-Net. In our experiment on English-to-Chinese font translation, we observed better results in fonts with distinct local features than conventional Chinese fonts compared to results obtained from Font Translator GAN. We also validate our method on multiple languages and datasets.