Andrii Bidochko

• Updated: August 19, 2026
• 7 min read

From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection

Direct Answer

The paper introduces S2R – a closed‑loop framework that synthesizes realistic reflected videos, trains a diffusion‑based model to strip reflections from video streams, and provides the first dedicated benchmark for video reflection removal. This matters because reflections captured through glass degrade visual quality, confuse downstream computer‑vision models, and have long lacked scalable, temporally coherent solutions.

Background: Why This Problem Is Hard

When a camera looks through a pane of glass, two optical phenomena coexist: the transmission (the scene behind the glass) and the reflection (light bouncing off the glass surface). In static images, researchers have built datasets and handcrafted priors to separate these layers, but extending those ideas to video introduces three intertwined challenges:

• Temporal coherence. Reflections shift, blur, and split across frames due to glass thickness and surface roughness. A naïve frame‑by‑frame approach creates flickering artifacts that break the visual flow.
• Data scarcity. Paired reflected‑and‑reflection‑free video pairs are virtually nonexistent in the wild, making supervised learning impractical.
• Physical realism. Real‑world glass exhibits ghosting (multiple displaced copies), blur from surface micro‑structures, and spatially varying reflectance. Simulators that ignore these physics generate synthetic data that fails to generalize.

Existing video‑enhancement pipelines either treat reflections as generic noise (leading to over‑smoothing) or rely on handcrafted optical flow tricks that cannot capture the nuanced physics of glass. Consequently, commercial video‑editing tools still require manual masking, and autonomous systems (e.g., surveillance drones) struggle with glass‑induced visual noise.

What the Researchers Propose

The authors present a three‑stage framework called S2R (Synthesis‑to‑Removal):

1. S2R‑Synthesis. A physics‑grounded augmentation engine that operates in a “structure space” to model glass‑specific effects—roughness‑induced blur, thickness‑induced ghosting, and spatial reflectance variation. It then renders reflected videos using a pretrained video diffusion renderer.
2. S2R‑Removal. The first diffusion‑based video dereflection model. It adapts a generic video diffusion prior through reflection‑aware latent adaptation and a one‑step pixel‑geometric refinement, delivering clean transmission in a single denoising pass.
3. S2R‑Bench. A benchmark suite that supplies paired synthetic videos, full‑reference metrics, and a human perceptual study for real‑world evaluation.

Each component plays a distinct role: synthesis creates the training signal, removal learns to invert the physics, and the benchmark validates both realism and temporal consistency.

How It Works in Practice

Conceptual Workflow

The end‑to‑end pipeline can be visualized as a loop:

1. Structure‑Space Augmentation. Starting from a clean video, the system extracts a multi‑scale structural representation (edges, textures, motion fields). It then perturbs this representation with glass‑specific kernels that emulate blur, ghosting offsets, and reflectance maps.
2. Video Diffusion Rendering. The perturbed structure feeds a conditional video diffusion model. This model, trained on large‑scale video corpora, generates a photorealistic reflected video that respects the underlying motion and lighting.
3. Reflection‑Aware Latent Adaptation. For removal, the same diffusion backbone is frozen. A lightweight adapter learns to steer the latent space away from reflection‑laden regions, guided by a loss that penalizes residual ghosting and blur.
4. One‑Step Pixel‑Geometric Refinement. After the diffusion pass, a geometry‑aware filter refines pixel values based on estimated depth and surface normals, ensuring that edges of the transmission layer remain sharp.
5. Benchmark Evaluation. Synthesized pairs are fed to the trained remover, and results are scored against ground truth (PSNR, SSIM) and human judgments collected via crowdsourcing.

Key Differentiators

• Physics‑Grounded Synthesis. Unlike generic data augmentation, the S2R‑Synthesis module explicitly models optical phenomena, producing reflections that are indistinguishable from real glass captures.
• Diffusion Prior Reuse. By leveraging a pretrained video diffusion model, the approach inherits high‑fidelity texture generation without training a massive video generator from scratch.
• Single‑Step Denoising. Traditional diffusion‑based restoration requires dozens of iterative steps. S2R‑Removal collapses this to one step, delivering inference speeds faster than many CNN baselines.
• Temporal Consistency by Design. The diffusion renderer operates on video clips (e.g., 8‑frame windows), preserving motion cues and preventing frame‑wise flicker.

Evaluation & Results

Test Scenarios

The authors evaluated S2R‑Removal on three fronts:

• S2R‑Bench Synthetic Set. 1,200 paired videos covering diverse scenes (indoor, outdoor, low‑light) and glass parameters.
• Public Image Benchmarks. Adapted image‑level reflection removal datasets (e.g., Real‑World Reflection Removal) to assess cross‑domain generalization.
• Real‑World Human Study. 200 participants compared outputs from S2R‑Removal, a leading CNN method, and a non‑diffusion transformer on unseen real videos.

Key Findings

• Quantitative Superiority. S2R‑Removal achieved a 2.3 dB PSNR gain and a 0.07 SSIM improvement over the previous state‑of‑the‑art video reflection remover, while also reducing runtime by 35 %.
• Temporal Coherence. The average temporal warping error dropped by 28 % compared to frame‑wise baselines, confirming smoother motion.
• Human Preference. In the perceptual study, 68 % of participants rated S2R‑Removal outputs as “cleaner” and “more natural” than competing methods, with a statistically significant margin (p < 0.01).
• Cross‑Domain Robustness. When applied to image benchmarks without fine‑tuning, the model retained a top‑3 ranking, demonstrating that the diffusion prior generalizes beyond the synthetic video domain.

These results collectively validate the hypothesis that physics‑aware synthesis plus diffusion‑based restoration can close the gap that has long separated research prototypes from production‑ready video dereflection.

Why This Matters for AI Systems and Agents

For AI practitioners building perception pipelines—whether for autonomous navigation, retail analytics, or remote collaboration—clean visual input is a prerequisite. Reflections introduce spurious edges, false motion vectors, and misleading color cues that degrade object detection, pose estimation, and scene understanding.

Integrating S2R‑Removal into an AI stack yields several concrete benefits:

• Improved Downstream Accuracy. Object detectors trained on dereflected video see a 4–6 % boost in mean average precision on glass‑rich environments.
• Reduced Pre‑Processing Overhead. Because the model runs in a single diffusion step, it can be deployed on edge GPUs (e.g., NVIDIA Jetson) without sacrificing real‑time frame rates.
• Modular Compatibility. The diffusion backbone can be swapped for other generative priors, allowing system architects to align dereflection with existing video‑generation or compression modules.
• Data‑Efficient Training. The physics‑grounded synthesis eliminates the need for costly manual annotation, enabling rapid iteration for domain‑specific agents (e.g., warehouse robots that navigate through glass doors).

Enterprises looking to embed visual AI into their workflows can therefore treat S2R as a plug‑and‑play preprocessing layer, similar to how UBOS platform overview offers modular AI components for end‑to‑end pipelines.

What Comes Next

While S2R marks a significant leap, several avenues remain open:

• Higher‑Resolution Scaling. Current experiments cap at 720p. Extending the diffusion renderer to 4K will demand memory‑efficient attention mechanisms.
• Adaptive Glass Modeling. Real‑world glass can have dynamic properties (e.g., electrochromic tint). Future work could incorporate sensor feedback to adjust the synthesis parameters on the fly.
• Joint Multi‑Task Learning. Combining dereflection with simultaneous de‑hazing or low‑light enhancement could produce a universal video‑cleaning agent.
• Integration with Conversational Agents. Embedding S2R‑Removal into chat‑driven automation (e.g., ChatGPT and Telegram integration) would let users submit raw video clips and receive cleaned outputs via a conversational UI.

Addressing these challenges will broaden the applicability of video dereflection from research labs to production ecosystems, including Enterprise AI platform by UBOS customers who demand reliable visual data streams.

For a deeper dive into the methodology and to explore the open‑source code, see the original arXiv paper.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.