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<h1 class="title is-1 publication-title">Reflection Removal through Efficient Adaptation of Diffusion Transformers</h1> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"> |
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<a href="https://github.com/daniyarzt" target="_blank">Daniyar Zakarin</a><sup>*,1,2</sup>,</span> |
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<span class="author-block"> |
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<a href="" target="_blank">Thiemo Wandel</a><sup>*,2</sup>,</span> |
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<span class="author-block"> |
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<a href="https://www.obukhov.ai/" target="_blank">Anton Obukhov</a><sup>†,2</sup>, |
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</span> |
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<span class="author-block"> |
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<a href="https://scholar.google.ch/citations?user=T51W57YAAAAJ&hl=en" target="_blank">Dengxin Dai</a><sup>2</sup> |
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</span> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"><sup>1</sup>ETH Zurich</span> |
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<span class="author-block"><sup>2</sup>HUAWEI Bayer Lab</span> |
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<span class="author-block">*Equal contributors, internship work</span> |
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<span class="author-block">†Project lead</span> |
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<a href="https://arxiv.org/abs/2512.05000" target="_blank" rel="noopener noreferrer" |
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class="external-link button is-normal is-rounded is-dark"> |
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<span>Paper</span> |
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<span class="link-block"> |
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<a href="https://github.com/huawei-bayerlab/windowseat-reflection-removal" target="_blank" rel="noopener noreferrer" class="external-link button is-normal is-rounded is-dark"> |
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Code |
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<span class="link-block"> |
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<a href="https://hf.co/huawei-bayerlab/windowseat-reflection-removal-v1-0" target="_blank" rel="noopener noreferrer" class="external-link button is-normal is-rounded is-dark"> |
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🤗 |
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Model |
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</a> |
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<img id="teaser" width="100%" src="./images/windowseat_teaser.jpg" alt="Teaser image demonstrating Marigold depth estimation."/> |
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<h2 class="subtitle has-text-centered mt-4"> |
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We present <span class="methodname">WindowSeat</span>, a model and fine-tuning protocol for one-step single-image reflection removal. |
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<h2 class="title is-3">Abstract</h2> |
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<p> |
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We introduce a diffusion-transformer (DiT) framework for single-image reflection removal that leverages the generalization strengths of foundation diffusion models |
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in the restoration setting. |
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Rather than relying on task-specific architectures, we repurpose a pre-trained DiT-based foundation model by conditioning it on reflection-contaminated inputs and guiding it toward clean transmission layers. |
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We systematically analyze existing reflection removal data sources for diversity, scalability, and photorealism. |
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To address the shortage of suitable data, we construct a physically based rendering (PBR) pipeline in Blender, built around the Principled BSDF, to synthesize realistic glass materials and reflection effects. |
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Efficient LoRA-based adaptation of the foundation model, combined with the proposed synthetic data, achieves state-of-the-art performance on in-domain and zero-shot benchmarks. |
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These results demonstrate that pretrained diffusion transformers, when paired with physically grounded data synthesis and efficient adaptation, offer a scalable and high-fidelity solution for reflection removal. |
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</p> |
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<h2 class="title is-3">Qualitative Comparison with Other Recent Methods</h2> |
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</section> |
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<button class="ws-model-pill" data-model="rdnet">RDNet</button> |
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</section> |
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<div class="columns is-centered has-text-centered"> |
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<div class="column is-four-fifths"> |
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<h2 class="title is-3">Our Approach</h2> |
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<div class="content has-text-justified"> |
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<h3 class="title has-text-centered"> |
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Physically Based Rendering Pipeline for Data Generation |
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</h3> |
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<p> |
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Our method’s PBR pipeline generates realistic reflection-contaminated training data by simulating true light–glass interaction |
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inside a lightweight Blender setup. It uses the Principled BSDF to control a surface’s physical properties—such as index of refraction, |
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thickness, and roughness—allowing the system to reproduce ghosting, blur, scattering, and high-intensity highlights that simple alpha blending cannot capture. |
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</p> |
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</div></div></div></div> |
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<div class="is-max-desktop"> |
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<div class="columns is-centered has-text-centered"> |
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<div class="content has-text-justified"> |
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<img id="method_train" width="100%" src="./images/pbr_figure.jpg" alt="Marigold training scheme"/> |
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</div></div></div></div> |
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<div class="container is-max-desktop"> |
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<div class="columns is-centered has-text-centered"> |
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<h3 class="title has-text-centered mt-4"> |
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Fine-tuning Protocol for Modern Diffusion Models |
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</h3> |
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<p> |
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Our model repurposes a large diffusion transformer as a feed-forward reflection-removal network by operating directly |
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in the VAE's latent space and training only lightweight LoRA adapters. During training, the network receives the encoded |
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latent of a reflection-contaminated image and predicts a latent-space update that produces a clean transmission result |
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in a single step, avoiding multi-stage diffusion or auxiliary modules. High-quality PBR data is a key ingredient that |
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ensures that this fine-tuning protocol can be applied to future LDMs without major modifications. |
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</p> |
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<img id="method_inference" width="100%" src="./images/windowseat_training.png" alt="Marigold inference scheme"/> |
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<h3 class="title has-text-centered mt-0"> |
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Quantitative Comparison with Other Recent Methods |
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</h3> |
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<p> |
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Our model consistently outperforms prior reflection-removal methods across both in-domain datasets and challenging zero-shot benchmarks. |
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It delivers higher PSNR and SSIM scores than existing approaches, |
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including diffusion-based, transformer-based, and dual-stream architectures, |
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and shows especially large gains on the SIR2 benchmarks, where it improves zero-shot PSNR by more than 1.5 dB and achieves the highest perceptual quality metrics (MS-SSIM and LPIPS). |
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Qualitative comparisons further show that it handles strong, complex, and high-frequency reflections with fewer artifacts, |
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while other methods often leave reflections partially intact or introduce distortions. |
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Overall, the model sets a new performance level while requiring a simpler architecture and more efficient training. |
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</p> |
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</div></div></div></div> |
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<div class="is-max-desktop"> |
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<div class="columns is-centered has-text-centered"> |
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<div class="column is-four-fifths"> |
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<div class="content has-text-justified"> |
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<img id="comparison" width="100%" src="./images/both_result_tables.png" alt="Quantitative Comparison with Other Recent Methods"/> |
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</div></div></div></div> |
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<div class="container is-max-desktop"> |
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<div class="columns is-centered has-text-centered"> |
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<div class="column is-four-fifths"> |
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<div class="content has-text-justified"> |
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<p class="mt-4 mb-0 pb-0"> |
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Refer to the pdf paper linked above for more details on qualitative, quantitative, and ablation studies. |
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</p> |
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</div> |
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</div> |
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</div> |
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</div> |
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</section> |
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<script> |
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$(window).on('load', function() { |
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bulmaCarousel.attach('#results-carousel-testimonials', { |
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$(".twentytwenty-container-top").twentytwenty({ |
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before_label: 'Input', |
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default_offset_pct: 0.4, |
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}); |
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$("#ws-comparison-slider").twentytwenty({ |
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<section class="section mt-0 pt-0" id="BibTeX"> |
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<div class="container is-max-desktop content"> |
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<h2 class="title">Citation</h2> |
|
|
<pre class="selectable"> |
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|
@misc{zakarin2025reflectionremovalefficientadaptation, |
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title = {Reflection Removal through Efficient Adaptation of Diffusion Transformers}, |
|
|
author = {Daniyar Zakarin and Thiemo Wandel and Anton Obukhov and Dengxin Dai}, |
|
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year = {2025}, |
|
|
eprint = {2512.05000}, |
|
|
archivePrefix= {arXiv}, |
|
|
primaryClass = {cs.CV}, |
|
|
url = {https://arxiv.org/abs/2512.05000}, |
|
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} |
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</pre> |
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</div> |
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