  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.
