UnReflectAnything：通过渲染合成镜面高光监督实现仅RGB的高光去除
UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision
提出用单目几何与Fresnel着色渲染物理合理的合成高光作为监督信号，使仅用RGB图像即可训练高光去除模型；其token级特征修复模块可复用于其他需要恢复被遮挡或损坏特征的底层视觉任务。
原文依据 a token-level inpainting module that restores corrupted feature patches before producing the final diffuse image
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论镜面高光会扭曲外观、遮挡纹理，并妨碍自然图像和手术图像中的几何推理。我们提出UnReflectAnything，一个仅使用RGB的框架，通过预测高光图并重建无反射的漫反射图像，从单张图像中去除高光。该模型使用冻结的视觉Transformer编码器提取多尺度特征，一个轻量级检测头定位镜面区域，以及一个token级图像修复模块在生成最终漫反射图像之前恢复损坏的特征块。为克服配对监督的缺乏，我们引入虚拟高光合成流程，利用单目几何、Fresnel感知着色和随机光照渲染物理合理的镜面高光，从而能够在具有正确几何结构的任意RGB图像上进行训练。UnReflectAnything在自然和手术领域均能泛化，这些领域中非朗伯表面和非均匀光照会产生严重高光，并在多个基准上取得与最优结果相竞争的性能。项目页面：alberto-rota.github.io/UnReflectAnything
英文原文摘要 1150 字符
Specular highlights distort appearance, obscure texture, and hinder geometric reasoning in both natural and surgical imagery. We present UnReflectAnything, an RGB-only framework that removes highlights from a single image by predicting a highlight map together with a reflection-free diffuse reconstruction. The model uses a frozen vision transformer encoder to extract multi-scale features, a lightweight head to localize specular regions, and a token-level inpainting module that restores corrupted feature patches before producing the final diffuse image. To overcome the lack of paired supervision, we introduce a Virtual Highlight Synthesis pipeline that renders physically plausible specularities using monocular geometry, Fresnel-aware shading, and randomized lighting which enables training on arbitrary RGB images with correct geometric structure. UnReflectAnything generalizes across natural and surgical domains where non-Lambertian surfaces and non-uniform lighting create severe highlights and it achieves competitive performance with state-of-the-art results on several benchmarks. Project Page: alberto-rota.github.io/UnReflectAnything