论文标题
fonttransformer：通过堆叠变压器构成的很少的高分辨率中国字形图像合成
FontTransformer: Few-shot High-resolution Chinese Glyph Image Synthesis via Stacked Transformers
论文作者
论文摘要
来自一些在线培训样本的自动生成高质量的中国字体是一项艰巨的任务，尤其是当样本数量很小时。现有的少数字体生成方法只能合成通常具有不正确的拓扑结构或/和不完整的低分辨率字形图像。为了解决这个问题，本文提出了通过使用堆叠变压器的高分辨率中国字形图像综合的新颖的几弹性学习模型Fonttransformer。关键思想是应用并行变压器以避免预测错误的积累，并利用串行变压器来增强合成中风的质量。同时，我们还设计了一种新颖的编码方案，以将更多的字形信息和先验知识提供给我们的模型，这进一步使高分辨率和视觉上令人愉悦的字形图像产生。定性和定量实验结果都证明了我们方法的优越性与中国字体合成任务中的其他现有方法相比。
Automatic generation of high-quality Chinese fonts from a few online training samples is a challenging task, especially when the amount of samples is very small. Existing few-shot font generation methods can only synthesize low-resolution glyph images that often possess incorrect topological structures or/and incomplete strokes. To address the problem, this paper proposes FontTransformer, a novel few-shot learning model, for high-resolution Chinese glyph image synthesis by using stacked Transformers. The key idea is to apply the parallel Transformer to avoid the accumulation of prediction errors and utilize the serial Transformer to enhance the quality of synthesized strokes. Meanwhile, we also design a novel encoding scheme to feed more glyph information and prior knowledge to our model, which further enables the generation of high-resolution and visually-pleasing glyph images. Both qualitative and quantitative experimental results demonstrate the superiority of our method compared to other existing approaches in the few-shot Chinese font synthesis task.
