Abstract
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, which is laborious and not practical for normal users. In recent years, some efforts have been made for automatic text effects transfer, however, the lack of data limits the capability of transfer models. To address this problem, we introduce a new text effects dataset, TE141K, with 141,081 text effects/glyph pairs in total. Our dataset consists of 152 professionally designed text effects, rendered on glyphs including English letters, Chinese characters, Arabic numerals, etc. To the best of our knowledge, this is the largest dataset for text effects transfer as far. Based on this dataset, we propose a baseline approach named Text Effects 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 where 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.
Abstract (translated)
文本效果是文本的轮廓、颜色和纹理等视觉元素的组合，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，但由于其极其复杂，通常是由人类专家创建的，这对于普通用户来说是很费劲且不实用的。近年来，在文本效果自动传递方面做了一些努力，但由于数据的缺乏，限制了文本效果自动传递模型的能力。为了解决这个问题，我们引入了一个新的文本效果数据集TE141K，总共有141081个文本效果/字形对。我们的数据集由152个专业设计的文本效果组成，呈现在包括英文字母、中文字符、阿拉伯数字等的字形上。据我们所知，这是迄今为止最大的文本效果传输数据集。基于此数据集，我们提出了一种基线方法，即文本效果传递gan（tet-gan），它支持一个模型中所有152种样式的传递，并能有效地扩展到新的样式。最后，我们进行了一个全面的比较，其中14个样式转换模型是基准。实验结果表明，tet-gan在定性和定量两方面都具有优势，说明我们的数据集是有效和具有挑战性的。
URL
https://arxiv.org/abs/1905.03646
