Reproduce “UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision” on OpenResearch
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About this paper
Alberto Rota, Mert Kiray, Mert Asim Karaoglu, Patrick Ruhkamp, Elena De Momi, Nassir Navab, Benjamin Busam
UnReflectAnything presents an RGB-only framework for removing specular highlights from single images, leveraging a novel synthetic specular supervision pipeline and token-space inpainting. The model demonstrates high structural consistency (SSIM up to 0.988) and strong highlight suppression (LSR as low as 0.002) across natural and surgical imagery, enhancing downstream tasks like camera pose estimation.
Key insights
- Overcoming the data scarcity challenge for highlight removal is feasible by synthetically generating physically plausible specular highlights on arbitrary images, enabling training without real paired diffuse-only data.
- Reconstructing diffuse appearance in the patch-token space of a powerful pre-trained Vision Transformer allows for more coherent and texture-preserving inpainting compared to direct pixel-level manipulations.
- A carefully designed multi-stage training curriculum, coupled with dedicated losses for highlight prediction, token inpainting, and image reconstruction, is crucial for achieving robust, artifact-free highlight removal that generalizes across diverse domains.
Summary by alphaXiv.
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