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Neural Style Difference Transfer and Its Application to Font Generation
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2020-01-21 , DOI: arxiv-2001.07321
Gantugs Atarsaikhan, Brian Kenji Iwana and Seiichi Uchida

Designing fonts requires a great deal of time and effort. It requires professional skills, such as sketching, vectorizing, and image editing. Additionally, each letter has to be designed individually. In this paper, we will introduce a method to create fonts automatically. In our proposed method, the difference of font styles between two different fonts is found and transferred to another font using neural style transfer. Neural style transfer is a method of stylizing the contents of an image with the styles of another image. We proposed a novel neural style difference and content difference loss for the neural style transfer. With these losses, new fonts can be generated by adding or removing font styles from a font. We provided experimental results with various combinations of input fonts and discussed limitations and future development for the proposed method.

中文翻译:

神经风格差异迁移及其在字体生成中的应用

设计字体需要大量的时间和精力。它需要专业技能,例如素描、矢量化和图像编辑。此外,每个字母都必须单独设计。在本文中,我们将介绍一种自动创建字体的方法。在我们提出的方法中,找到了两种不同字体之间字体样式的差异,并使用神经样式转移将其转移到另一种字体。神经风格迁移是一种用另一幅图像的风格对一幅图像的内容进行风格化的方法。我们为神经风格转移提出了一种新的神经风格差异和内容差异损失。有了这些损失,可以通过在字体中添加或删除字体样式来生成新字体。
更新日期:2020-01-22
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