提出三重特征解耦框架，实现文本内容、风格与背景的精准编辑。
TripleFDS: Triple Feature Disentanglement and Synthesis for Scene Text Editing
- 通过三元组构造法分离文本内容、风格和背景特征
- 在12.5万组数据上训练，图像保真度达SSIM 44.54，文本准确率93.58%
- 支持风格替换、背景迁移等新编辑操作，控制更灵活
场景文本编辑（STE）旨在自然修改图像中的文字并保持视觉一致性，其关键因素可分为文本样式、文本内容和背景三部分。以往方法对可编辑属性的解耦不完整，通常仅处理单一维度（如内容编辑），限制了可控性与视觉一致性。为此，本文提出TripleFDS框架，实现三重特征解耦与合成，并构建配套数据集SCB Synthesis。该数据集采用创新的“SCB Group”结构，每张图像组合三种属性以生成多样且解耦的训练样本。TripleFDS基于此构建单元，先通过组间对比正则化确保语义准确性，再通过样本内多特征正交性降低冗余。合成阶段引入特征重映射，避免重建过程中的“捷径”现象与潜在特征泄露。在12.5万组SCB Groups上训练后，TripleFDS在主流STE基准上达到当前最优表现：图像保真度SSIM为44.54，文本准确率ACC为93.58%。除性能优越外，其更灵活的编辑能力支持风格替换、背景迁移等新操作。
原文摘要 · Abstract (English)
Scene Text Editing (STE) aims to naturally modify text in images while preserving visual consistency, the decisive factors of which can be divided into three parts, i.e., text style, text content, and background. Previous methods have struggled with incomplete disentanglement of editable attributes, typically addressing only one aspect - such as editing text content - thus limiting controllability and visual consistency. To overcome these limitations, we propose TripleFDS, a novel framework for STE with disentangled modular attributes, and an accompanying dataset called SCB Synthesis. SCB Synthesis provides robust training data for triple feature disentanglement by utilizing the "SCB Group", a novel construct that combines three attributes per image to generate diverse, disentangled training groups. Leveraging this construct as a basic training unit, TripleFDS first disentangles triple features, ensuring semantic accuracy through inter-group contrastive regularization and reducing redundancy through intra-sample multi-feature orthogonality. In the synthesis phase, TripleFDS performs feature remapping to prevent "shortcut" phenomena during reconstruction and mitigate potential feature leakage. Trained on 125,000 SCB Groups, TripleFDS achieves state-of-the-art image fidelity (SSIM of 44.54) and text accuracy (ACC of 93.58%) on the mainstream STE benchmarks. Besides superior performance, the more flexible editing of TripleFDS supports new operations such as style replacement and background transfer. Code: https://github.com/yusenbao01/TripleFDS
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