提出无参考图像的物体移除评估指标，更精准衡量扩散模型的修复效果。
ReMOVE: A Reference-free Metric for Object Erasure
- 基于生成结果直接评估，无需原始参考图
- 能区分物体移除与替换，符合人类直觉
- 适合评估扩散模型的图像编辑质量
我们提出 $ exttt{ReMOVE}$，一种用于评估基于扩散模型的图像编辑中物体移除效果的新型无参考指标。与现有的 LPIPS 和 CLIPScore 等度量不同，$ exttt{ReMOVE}$ 解决了在无参考图像场景下评估修复效果的难题，这类场景在实际应用中很常见。它能有效区分物体移除与替换，这是由于扩散模型生成过程具有随机性所致。传统指标难以契合修补（inpainting）的直观定义，即（1）在掩码区域内实现无缝物体移除，（2）保持背景连续性。$ exttt{ReMOVE}$ 不仅与当前最先进指标高度相关，且与人类感知一致，还能捕捉修补过程中的细微差异，提供更精细的生成结果评估。
原文摘要 · Abstract (English)
We introduce $\texttt{ReMOVE}$, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, $\texttt{ReMOVE}$ addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. $\texttt{ReMOVE}$ not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.
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