超越平面文本：面向视觉文本生成的双重自继承引导
Beyond Flat Text: Dual Self-inherited Guidance for Visual Text Generation
AI总结：
针对扩散模型在倾斜、弯曲等复杂文本布局下易生成扭曲文本且与背景不协调的问题，本文提出免训练框架STGen，通过语义校正与结构注入双分支实现高质量协调的复杂布局视觉文本生成。
摘要：
在真实世界图像中，受艺术设计或布局限制，倾斜或弯曲文本（常见于易拉罐、横幅或徽章上）即便不比平面文本更常见，也至少与之出现频率相当。尽管扩散模型的先进生成能力已能实现高质量视觉文本生成，但受训练数据限制，这类模型在处理倾斜或弯曲文本布局时，往往会生成扭曲的文本，且文本与背景不协调。本文提出一种全新的免训练框架STGen，可在倾斜、弯曲文本布局等挑战性场景下准确生成视觉文本，同时实现文本与背景的协调融合。本框架将视觉文本生成过程拆解为两个分支：（1）语义校正分支：利用模型生成平面且准确视觉文本的能力，引导挑战性场景下的文本生成。生成的平面文本隐变量包含丰富的准确语义信息，既涵盖文本本身也涉及背景，通过引入该隐变量，我们校正了复杂布局下文本的语义信息，同时协调了文本与背景的融合。（2）结构注入分支：在推理过程中强化视觉文本结构。我们将富含字形结构的字形图像隐信息作为新条件引入，进一步增强文本结构。为提升图像协调性，我们还采用了一种有效的融合方法来合并先验信息，为生成过程提供坚实基础。在多种视觉文本布局上开展的大量实验表明，本框架实现了更优的生成准确率与出色的生成质量。
英文摘要：
In real-world images, slanted or curved texts, especially those on cans, banners, or badges, appear as frequently, if not more so, than flat texts due to artistic design or layout constraints. While high-quality visual text generation has become available with the advanced generative capabilities of diffusion models, these models often produce distorted text and inharmonious text background when given slanted or curved text layouts due to training data limitation. In this paper, we introduce a new training-free framework, STGen, which accurately generates visual texts in challenging scenarios (\eg, slanted or curved text layouts) while harmonizing them with the text background. Our framework decomposes the visual text generation process into two branches: (i) \textbf{Semantic Rectification Branch}, which leverages the ability in generating flat but accurate visual texts of the model to guide the generation of challenging scenarios. The generated latent of flat text is abundant in accurate semantic information related both to the text itself and its background. By incorporating this, we rectify the semantic information of the texts and harmonize the integration of the text with its background in complex layouts. (ii) \textbf{Structure Injection Branch}, which reinforces the visual text structure during inference. We incorporate the latent information of the glyph image, rich in glyph structure, as a new condition to further strengthen the text structure. To enhance image harmony, we also apply an effective combination method to merge the priors, providing a solid foundation for generation. Extensive experiments across a variety of visual text layouts demonstrate that our framework achieves superior accuracy and outstanding quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
