Abstract
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: this https URL
Abstract (translated)
镜面高光会扭曲外观，遮蔽纹理，并阻碍几何推理，在自然和外科图像中均会出现这种情况。我们提出了 UnReflectAnything，这是一个仅基于 RGB 的框架，通过预测高光图并生成无反射的漫反射重建来从单张图像中移除高光。该模型使用冻结的视觉变换器编码器提取多尺度特征、一个轻量级头部用于定位镜面区域，并且在输出最终漫反射图像之前，采用令牌级别的修复模块恢复被破坏的特征块。 为了克服配对监督不足的问题，我们引入了一个虚拟高光合成流水线，该流水线使用单目几何、符合菲涅尔定律的着色以及随机光线来生成物理上合理的镜面效果。这使得模型可以利用具有正确几何结构的任意 RGB 图像进行训练。 UnReflectAnything 能够在自然和外科领域中跨域泛化，在非朗伯表面和不均匀照明条件下产生的严重高光情况下表现良好，并且在多个基准测试中取得了与现有最先进的方法相当甚至更好的性能。项目页面：[请在此处插入正确的网址链接]
URL
https://arxiv.org/abs/2512.09583
