Abstract
Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene text editing framework that integrates the strengths of Attention Boost (AttnBoost) and Flow Manifold Steering (FMS) to enable flexible, high-fidelity text manipulation without additional training. Specifically, FMS preserves the structural and style consistency by modeling the visual flow of characters and background regions, while AttnBoost enhances the rendering of textual content through attention-based guidance. By jointly leveraging these complementary modules, our approach performs end-to-end text editing through semantic alignment and spatial refinement in a plug-and-play manner. Extensive experiments demonstrate that our framework achieves visual quality and text accuracy comparable to or superior to those of training-based counterparts, generalizing well across diverse scenes and languages. This study advances scene text editing toward a more efficient, generalizable, and training-free paradigm. Code is available at this https URL
Abstract (translated)
场景文本编辑旨在修改自然图像中的文本内容，同时保持视觉真实性和语义一致性。现有方法通常需要针对特定任务进行训练或依赖成对数据，限制了其可扩展性和适应性。本文提出TextFlow，一个免训练的场景文本编辑框架，该框架整合了注意力增强（AttnBoost）和流形引导（FMS）的优势，无需额外训练即可实现灵活、高保真的文本操作。具体而言，FMS通过建模字符与背景区域的视觉流来保持结构与风格一致性，而AttnBoost则通过注意力引导增强文本内容的渲染。通过联合利用这些互补模块，我们的方法以即插即用的方式，通过语义对齐和空间细化实现端到端的文本编辑。大量实验表明，我们的框架在视觉质量和文本准确性方面与基于训练的方法相当甚至更优，且能良好泛化至多样场景和语言。本研究推动场景文本编辑向更高效、更具泛化能力且免训练的范式发展。代码可在此链接获取。
URL
https://arxiv.org/abs/2603.24571
