IEEE Trans Pattern Anal Mach Intell. 2021 Oct;43(10):3709-3723. doi: 10.1109/TPAMI.2020.2983697. Epub 2021 Sep 2.
Text effects are combinations of visual elements such as outlines, colors and textures of text, which can dramatically improve its artistry. Although text effects are extensively utilized in the design industry, they are usually created by human experts due to their extreme complexity; this is laborious and not practical for normal users. In recent years, some efforts have been made toward automatic text effect transfer; however, the lack of data limits the capabilities of transfer models. To address this problem, we introduce a new text effects dataset, TE141K 1.Project page: https://daooshee.github.io/TE141K/. with 141,081 text effect/glyph pairs in total. Our dataset consists of 152 professionally designed text effects rendered on glyphs, including English letters, Chinese characters, and Arabic numerals. To the best of our knowledge, this is the largest dataset for text effect transfer to date. Based on this dataset, we propose a baseline approach called text effect transfer GAN (TET-GAN), which supports the transfer of all 152 styles in one model and can efficiently extend to new styles. Finally, we conduct a comprehensive comparison in which 14 style transfer models are benchmarked. Experimental results demonstrate the superiority of TET-GAN both qualitatively and quantitatively and indicate that our dataset is effective and challenging.
文本效果是视觉元素的组合，例如文本的轮廓、颜色和纹理，可以显著提高其艺术性。尽管文本效果在设计行业中得到了广泛的应用，但由于其极端的复杂性，通常由人类专家来创建；这既费力又不适合普通用户。近年来，已经有一些关于自动文本效果转换的努力，但数据的缺乏限制了转换模型的能力。为了解决这个问题，我们引入了一个新的文本效果数据集，TE141K1.项目页面：https://daooshee.github.io/TE141K/。总共包含 141081 对文本效果/字形对。我们的数据集由 152 种专业设计的文本效果组成，这些效果呈现在字形上，包括英文字母、中文字符和阿拉伯数字。据我们所知，这是迄今为止最大的文本效果转换数据集。基于这个数据集，我们提出了一种称为文本效果转移 GAN（TET-GAN）的基线方法，它支持在一个模型中转移所有 152 种风格，并且可以有效地扩展到新的风格。最后，我们进行了全面的比较，基准测试了 14 种风格转换模型。实验结果从定性和定量两个方面证明了 TET-GAN 的优越性，并表明我们的数据集是有效和具有挑战性的。
