Abstract
With the rapid development of diffusion models, style transfer has made remarkable progress. However, flexible and localized style editing for scene text remains an unsolved challenge. Although existing scene text editing methods have achieved text region editing, they are typically limited to content replacement and simple styles, which lack the ability of free-style transfer. In this paper, we introduce SceneTextStylizer, a novel training-free diffusion-based framework for flexible and high-fidelity style transfer of text in scene images. Unlike prior approaches that either perform global style transfer or focus solely on textual content modification, our method enables prompt-guided style transformation specifically for text regions, while preserving both text readability and stylistic consistency. To achieve this, we design a feature injection module that leverages diffusion model inversion and self-attention to transfer style features effectively. Additionally, a region control mechanism is introduced by applying a distance-based changing mask at each denoising step, enabling precise spatial control. To further enhance visual quality, we incorporate a style enhancement module based on the Fourier transform to reinforce stylistic richness. Extensive experiments demonstrate that our method achieves superior performance in scene text style transformation, outperforming existing state-of-the-art methods in both visual fidelity and text preservation.
Abstract (translated)
随着扩散模型的快速发展，风格迁移取得了显著进展。然而，场景文本中的灵活且局部化的风格编辑仍然是一个未解决的挑战。尽管现有的场景文本编辑方法已经实现了文字区域编辑，但它们通常局限于内容替换和简单的样式更改，并缺乏自由式风格转移的能力。在本文中，我们介绍了SceneTextStylizer，这是一种新颖的、无需训练的基于扩散模型的框架，用于对场景图像中的文本进行灵活且高保真的风格转换。 与先前的方法要么执行全局风格迁移，要么仅专注于文本内容修改不同，我们的方法通过使用提示引导的方式实现了针对文字区域的特定风格变换，并同时保持了文本可读性和风格一致性。为了实现这一点，我们设计了一个特征注入模块，该模块利用扩散模型逆向工程和自注意力机制有效地转移风格特征。此外，我们引入了一种基于距离变化掩码的区域控制机制，在每次去噪步骤中应用以实现精确的空间控制。为了进一步提高视觉质量，我们还集成了一个基于傅立叶变换的风格增强模块，用于加强风格丰富性。 大量的实验表明，我们的方法在场景文本风格转换方面表现出了优越性能，在视觉保真度和文本保留方面均优于现有的最先进的方法。
URL
https://arxiv.org/abs/2510.10910
