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: https://alberto-rota.github.io/UnReflectAnything/
@article{arxiv.2512.09583,
title = {UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision},
author = {Alberto Rota and Mert Kiray and Mert Asim Karaoglu and Patrick Ruhkamp and Elena De Momi and Nassir Navab and Benjamin Busam},
journal= {arXiv preprint arXiv:2512.09583},
year = {2025}
}