提出可控制编辑精度的连续场,解决文本编辑时误改邻近文字的问题。
Edit Fidelity Field: Semantics-Aware Region Isolation for Training-Free Scene Text Editing

- 用OCR识别文本区域构建四区连续场,精细控制每像素编辑范围。
- 在真实场景上将误改率从94%降至25%,非目标区域保真度提升91.4 dB PSNR。
- 无需训练、兼容任意扩散模型,适合需要高精度文本编辑的应用。
基于扩散模型的场景文本编辑(STE)虽已取得显著进展,但存在关键却被忽视的问题:编辑溢出——修改目标文本区域时,无意中影响了非目标区域,尤其是邻近文本。我们在四个类别共50个真实场景上系统评估发现,当前最先进的扩散编辑模型的溢出率达94%,几乎所有非目标文本区域都会被改变。为此,我们提出编辑保真度场(Edit Fidelity Field, EFF),一种语义感知的连续场,用于控制每像素的编辑保真度。与二值掩码不同,EFF利用OCR检测的文本区域构建四区结构:编辑核心区(完全可编辑)、过渡区(平滑衰减)、保护区(非目标文本,显式锁定)和背景区(严格保留)。EFF作为无训练、模型无关的后处理模块,可应用于任意扩散型STE方法。我们还提出了逐区域溢出量化方案,首次在个体非目标文本区域上测量编辑泄漏。实验表明,EFF将溢出率从94%降至25%,同时使非目标区域保真度提升91.4 dB PSNR。
原文摘要 · Abstract (English)
Scene text editing (STE) has achieved remarkable progress in accurately rendering target text through diffusion-based methods. However, we identify a critical yet overlooked problem: edit spillover -- when editing a target text region, existing methods inadvertently modify non-target regions, particularly neighboring text. Through systematic evaluation on 50 real-world scenes across four categories, we reveal that state-of-the-art diffusion editing models exhibit a spillover rate of 94%, meaning nearly all non-target text regions are altered during editing. To address this, we propose the Edit Fidelity Field (EFF), a semantics-aware continuous field that controls per-pixel editing fidelity. Unlike binary masks, EFF leverages OCR-detected text regions to construct a four-zone field: Edit Core (fully editable), Transition Zone (smooth decay), Protected Zone (non-target text, explicitly locked), and Background (strictly preserved). EFF operates as a training-free, model-agnostic post-processing module applicable to any diffusion-based STE method. We further propose per-region spillover quantification, a novel evaluation protocol that measures edit leakage at each non-target text region individually. Experiments demonstrate that EFF reduces spillover rate from 94% to 25% while improving non-target region preservation by +91.4 dB PSNR.
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