In this work, we propose a task called "Scene Style Text Editing (SSTE)", changing the text content as well as the text style of the source image while keeping the original text scene. Existing methods neglect to fine-grained adjust the style of the foreground text, such as its rotation angle, color, and font type. To tackle this task, we propose a quadruple framework named "QuadNet" to embed and adjust foreground text styles in the latent feature space. Specifically, QuadNet consists of four parts, namely background inpainting, style encoder, content encoder, and fusion generator. The background inpainting erases the source text content and recovers the appropriate background with a highly authentic texture. The style encoder extracts the style embedding of the foreground text. The content encoder provides target text representations in the latent feature space to implement the content edits. The fusion generator combines the information yielded from the mentioned parts and generates the rendered text images. Practically, our method is capable of performing promisingly on real-world datasets with merely string-level annotation. To the best of our knowledge, our work is the first to finely manipulate the foreground text content and style by deeply semantic editing in the latent feature space. Extensive experiments demonstrate that QuadNet has the ability to generate photo-realistic foreground text and avoid source text shadows in real-world scenes when editing text content.

翻译：在本文中，我们提出了一项名为“场景风格文本编辑”（SSTE）的任务，旨在改变源图像的文本内容及文本风格，同时保留原始文本场景。现有方法忽略了前景文本风格的细粒度调整，例如其旋转角度、颜色和字体类型。为应对这一任务，我们提出了一种名为“QuadNet”的四元框架，用于在潜在特征空间中嵌入和调整前景文本风格。具体而言，QuadNet由四部分组成，即背景修复、风格编码器、内容编码器和融合生成器。背景修复模块擦除源文本内容，并以高度真实的纹理恢复适当的背景。风格编码器提取前景文本的风格嵌入。内容编码器在潜在特征空间中提供目标文本表示，以实现内容编辑。融合生成器将上述各部分产生的信息组合起来，生成渲染后的文本图像。在实践中，我们的方法仅需字符串级标注即可在真实世界数据集上取得令人满意的表现。据我们所知，本文首次通过潜在特征空间中的深度语义编辑，对前景文本内容和风格进行精细操控。大量实验表明，QuadNet在编辑文本内容时，能够生成逼真的前景文本，并避免在真实场景中留下源文本阴影。