We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To these ends, we make a number of technical contributions: (1) We disentangle the style and content of a textual image into a non-parametric, fixed-dimensional vector. (2) We propose a novel approach inspired by StyleGAN but conditioned over the example style at different resolution and content. (3) We present novel self-supervised training criteria which preserve both source style and target content using a pre-trained font classifier and text recognizer. Finally, (4) we also introduce Imgur5K, a new challenging dataset for handwritten word images. We offer numerous qualitative photo-realistic results of our method. We further show that our method surpasses previous work in quantitative tests on scene text and handwriting datasets, as well as in a user study.

我们提出了一种新颖的方法来将文本图像的内容与其外观的所有方面分离开来，然后将所得到的外观表示应用于新的内容，以将源样式一次性转移给新的内容。我们通过自监督学习的方式学习了这种分离过程。我们的方法可以处理整个字块，无需将文本从背景分割开，也不需要按字符处理或对字符串长度进行假设。我们在不同的文本领域展示了结果，这些结果以前需要使用专门的方法，例如场景文本、手写文本。

TextStyleBrush：从单一示例中传输文本美学