UnReflectAnything

UnReflectAnything inputs any RGB image and removes specular highlights, returning a clean diffuse-only outputs. We trained UnReflectAnything by synthetizing specularities and supervising in DINOv3 feature space.

UnReflectAnything works on both natural indoor and surgical/endoscopic domain data.

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Architecture

• Encoder (E): Processes the input image I to extract a rich latent representation, F_l. This is the off-the-shelf pretrained DINOv3-large

• Reflection Predictor (H): Predicts a soft highlight mask (H), identifying areas of specular highlights.

• Masking Operation (P): A binary mask P is derived from the prediction and applied to the feature map: (1 - P) * F_l. This removes features contaminated by reflections, leaving "holes" in the data.

• Token Inpainter (T): Acts as a neural in-painter. It processes the masked features and uses the surrounding clean context prior and a learned mask token to synthesize the missing information in embedding space, producing the completed feature map F_comp.

• Decoder (D): Project the completed features back into the pixel space to generate the final, reflection-free image I_diff.

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Training Strategy

We train UnReflectAnything with Synthetic Specular Supervision by inferring 3D geometry from MoGe-2 and rendering highlights with a Blinn-Phong reflection model. We randomly sample the light source position in 3D space at every training iteration enhance etherogeneity.

We train the model in two stages

1. DPT Decoder Pre-Training: The Decoder is first pre-trained in an autoencoder configuration (minimize L(M_θ(I), I)) to ensure it can reconstruct realistic RGB textures from the DINOV3 latent space.
2. End-to-End Refinement: The full pipeline is then trained to predict reflection masks from H, and fill them using the Token Inpainter, ensuring the final output is both visually consistent and physically accurate. The decoder is also fine-tuned at this stage

Weights

Install the API and CLI on a Python>=3.11 environment with

pip install unreflectanything

then run

unreflectanything download --weights

to download the .pth weights in the package cache dir. The cache dir is usually at .cache/unreflectanything

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Basic Python Usage

import unreflectanything
import torch

# Load the pretrained model (uses cached weights)
unreflect_model = unreflectanything.model() 

# Run inference on a tensor [B, 3, H, W] in range [0, 1]
images = torch.rand(2, 3, 448, 448).cuda()
diffuse_output = unreflect_model(images) 

# Simple file-based inference
unreflectanything.inference("input_with_highlights.png", output="diffuse_result.png")

Refer to the Wiki for all details on the API endpoints

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CLI Overview

The package provides a comprehensive command-line interface via ura, unreflect, or unreflectanything.

• Inference: ura inference --input /path/to/images --output /path/to/output
• Evaluation: ura evaluate --output /path/to/results --gt /path/to/groundtruth
• Verification: ura verify --dataset /path/to/dataset

Refer to the Wiki for all details on the CLI endpoints

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Citation

If you use UnReflectAnything in your research or pipeline, please cite our paper:

@InProceedings{Rota_2026_CVPR,
    author    = {Rota, Alberto and Kiray, Mert and Karaoglu, Mert Asim and Ruhkamp, Patrick and De Momi, Elena and Navab, Nassir and Busam, Benjamin},
    title     = {UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
    pages     = {241-250}
}

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