AD-Relight: Training-Free Banner Relighting via Illumination Translation with Diffusion Priors

arXiv:2604.24407 · cs.CV · Submitted 2026-04-27 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "AD-Relight: Training-Free Banner Relighting via Illumination Translation with Diffusion Priors".

Tom: The AD-Relight framework is a novel, multi-stage, training-free approach designed to relight custom Photoshop-generated ad banners seamlessly into existing scenes by adapting a diffusion-based relighting model at test time.

Jane: First, who's behind it and why it matters.

Title and authors: Tom: Moving on to the title and authors of AD-Relight, it clearly lays out what they're doing: training-free banner relighting using illumination translation with diffusion priors. It tells us right away that the core trick is using light information to bridge the gap between a custom banner and its new environment.

Jane: And I think the authors are very clear about their aim, which is to solve that inconsistency issue where inserted banners lack scene illumination details, especially on horizontal surfaces like floors. They’re essentially proposing a conditional image-to-image translation problem to handle this lighting transfer seamlessly.

Lu: The fact that they're using IC-Light as the relighting backbone, trained on over ten million images with diverse lighting conditions, gives them a solid foundation for this process without having to deal with the millions of ad banner images that current diffusion-based methods require. That existing training set is a significant asset here.

Meng: I see how that pre-training helps keep the initial model robust, but my concern is whether that pre-trained model can generalize well when we move from portrait lighting scenarios to, say, very harsh directional light on a flat surface where the shading cues are completely different.

Lalam: That's a fair point, Meng; if the illumination translation process isn't robust across all spatial lighting variations, the transfer won't be reliable in diverse real-world settings. This method seems to focus heavily on aligning shading first before applying the light features from the backbone model.

The paper's summary: Tom: So, summarizing AD-Relight, they propose a three-stage process: Shade Alignment, Light Feature Estimation, and then finally Relighting the Custom Logo. Stage one focuses on matching the shading of the input region to the banner using SAM and Retinex theory approximations.

Jane: That sounds complex for a summary, but if I break it down simply, they are first figuring out how much light is hitting that specific area in the scene and then applying that pattern onto your custom logo structure. It’s like painting the shadow onto the banner first before trying to color it correctly.

Lu: And Stage two is where they use IC-Light to extract illumination cues by comparing full frame inputs with masked inputs, creating a differential feature that isolates the light contribution of just the target region against everything else. That differential illumination feature is key for getting that scene consistency right.

Meng: Is that differential feature extraction actually feasible without needing an enormous amount of training data specifically for ad banners? I’m thinking about the computational overhead involved in running this model at test time compared to what we are currently using.

Lalam: The paper suggests they approximate the illumination decomposition using Retinex theory, which is a standard way to separate light and surface properties, making the shading transfer stage computationally tractable within their framework. This makes it practical for deployment.

The paper's improvements: Tom: Now let's talk about what makes this approach better than what we have now. The main improvement is that it achieves relighting without requiring additional training on ad banner data, which saves us tons of labor and time compared to existing diffusion-based object insertion methods.

Jane: And another big win they point out is that it addresses a major flaw in previous work: the struggle with banners placed on floors or horizontal surfaces, which is something state-of-the-art relighting models often fail at because those scenarios aren't in their training distribution.

Lu: They also incorporate a texture map generated by another pre-trained diffusion model to simulate the material of the custom logo, blending it with the logo to match the scene’s surface properties, which adds a layer of realism that simple geometric warping completely misses.

Meng: The authors mention they use an adaptive threshold T hr to define a continuous shadow attenuation factor for smooth transitions instead of harsh edges, which is a nice engineering touch for visual quality improvement.

Lalam: When I look at the results, they show superior performance on metrics like SSIM and LPIPS compared to simple geometric warping and existing diffusion baselines, suggesting the human perception of realism is actually much better with this method.

Conclusion: Tom: So, wrapping up AD-Relight, it seems the main implication is that we can integrate custom ad banners into scenes with a level of lighting consistency that was previously thought to require massive amounts of specialized training data. It moves the goalposts for how we handle object insertion in visual content creation.

Jane: I think what this means for us is that creating personalized experiences using ads will become much smoother and more realistic across all kinds of settings, not just controlled studio shots. It opens up a lot more creative possibilities for where and how we place these banners.

Lu: For the future, I see this as a foundation. We could potentially adapt the light feature estimation part to handle even more complex scene lighting dynamics or different material interactions in future iterations of this work.

Meng: From an engineering standpoint, it’s promising because it’s training-free for us right now, meaning we can integrate this into existing pipelines quickly without waiting months for a new model to train. It's practical application over theoretical complexity at this stage.

Lalam: Ultimately, the advance in AD-Relight shows that leveraging diffusion priors and structured illumination translation can significantly improve the quality of visual content generation across many domains, impacting how we design immersive experiences.

Rameshwar Mishra, A. V. Subramanyam

Indraprastha Institute of Information Technology

cs.CV

Submitted: 2026-04-27

Updated: 2026-09-30

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 81/100

The gist: The AD-Relight framework is a novel, multi-stage, training-free approach designed to relight custom Photoshop-generated ad banners seamlessly into existing scenes by adapting a diffusion-based

Key concepts

Shade Alignment
This initial stage matches the shading of the input region (the area where the banner will go) to that of a custom logo. It uses Retinex theory approximations to decompose lighting into shading and reflectance components for both the scene and the banner, allowing for structural alignment.
Light Feature Estimation
This stage isolates how the target region contributes to the overall scene illumination using a pre-trained relighting backbone. By comparing outputs from a full frame and a masked background, it calculates a differential feature ($\epsilon$) that captures the specific lighting influence of the banner area.
Relighting
The final stage uses the extracted lighting features to adjust the custom logo's appearance. It combines differential illumination ($\epsilon$) with a global light gradient ($G$) to create a background input, which is then used to compute a smooth shadow map, ensuring the banner respects the scene's shadows and lighting trends.

Terminology

Summary

The AD-Relight framework is a novel, multi-stage, training-free approach designed to relight custom Photoshop-generated ad banners seamlessly into existing scenes by adapting a diffusion-based relighting model at test time. This method addresses the critical limitation in current ad placement and object insertion techniques—their inability to maintain lighting consistency for inserted banners, especially those placed on horizontal surfaces where they lack scene illumination details. By leveraging a pre-trained lighting-aware diffusion model without requiring extensive retraining, AD-Relight ensures that the newly added banners exhibit lighting consistent with the original scene, outperforming existing baselines and achieving superior human perception scores.

Motivation and Problem Statement

The surge in personalized content necessitates replacing existing regions with custom ad banners, but current pipelines rely on simple geometric warping that ignores underlying lighting conditions. State-of-the-art diffusion models struggle to relight these new banners because they are not trained on ad banner data, and training such a model would be prohibitively expensive. Existing methods for object insertion often suffer from identity loss and distortion, while existing object relighting models frequently fail when applied to floor-level placements outside their training distribution. The core challenge identified is that inserted banners exhibit lighting inconsistent with the scene, particularly where the original region has complex spatial lighting variations that are not reproduced by prior methods.

AD-Relight Framework Overview

AD-Relight is a novel three-stage, training-free framework formulated as a conditional image-to-image illumination translation problem. The method adapts a publicly available model, specifically using IC-Light [2] as the relighting backbone, which has been trained on over 10 million images with diverse lighting conditions. The framework consists of three main stages:

  1. Shade Alignment

  2. Light Feature Estimation

  3. Relighting the Custom Logo

Stage 1: Shade Alignment

This initial stage focuses on matching the shading characteristics of the input region to the custom banner's structure. It involves several steps:

** Extracting the input region using the Segment Anything Model (SAM) [16], denoted as I O.**

Pre-processing the Photoshop-generated custom logo I L to match the lighting shade of the input region I O.

The method then estimates shading maps by drawing inspiration from Retinex theory. This decomposition is approximated by factoring the illuminance channel of the original region (I O ill) into shading components:

S O = GaussianF ilterK(I O ill), S O t = I O ill / S O

The structural information of the custom ad banner (I L) is similarly factored to obtain its shading component (S L). The crucial step is then transferring the shading of the original region to the banner by applying S O to the structure of I L:

I L ill = S O s I L t,

Stage 2: Light Feature Estimation

This stage extracts region-specific illumination cues from the pre-trained relighting backbone (IC-Light [2]) using a differential probing strategy. The goal is to isolate the illumination contribution of the target region (I O) from the full scene background (B O).

The model implicitly encodes a light transport operator Tϕ and an illumination representation Lϕ.

The method constructs two inputs: the full frame background (BO) yielding output Out1, and a masked background BM by removing I O to yield Out2. Assuming approximate linearity in illumination space, the illumination contribution of region I O is approximated as:

L Mϕ ≈ Lϕ − L Oϕ

A differential illumination feature, ϵ, is defined as:

(4) ϵ = Out1 − Out2,

which captures the spatially aligned illumination residual in RGB space that captures the relative lighting contribution of I O under the model prior.

Stage 3: Relighting the Custom Logo

The final stage uses the extracted lighting feature to relight the custom logo (I L) while ensuring it respects scene illumination.

A weighted combination of ϵ and an initial light gradient G is passed as background input, denoted as Bϵ.

The initial light gradient G is obtained by applying a Gaussian filter to the illuminance channel of I O (I O ill), capturing low-frequency illumination structure. The combined input Bϵ is defined as:

(5) Bϵ = αϵG + (1 − αϵ)G,

where G captures global lighting trends and ϵ captures differential lighting. A shadow map is computed using an adaptive threshold T hr to define a continuous shadow attenuation factor df(x, y), which provides a smooth attenuation that preserves relative intensity differences while avoiding hard transitions.

Improvements for AI systems

Here are the specific improvements that an AI system, leveraging the AD-Relight framework, can achieve:

  1. Enhanced Ad Integration Realism in Dynamic Scenes: The system will be able to seamlessly insert custom Photoshop-generated ad banners into complex scenes (especially those with challenging lighting) while ensuring the banner's shading and illumination perfectly match the original scene's lighting conditions.

  2. Robustness to Floor/Horizontal Placement: Unlike existing diffusion-based relighting models that fail when objects are placed on floors, this system will accurately relight banners placed on horizontal surfaces, overcoming a major limitation in current state-of-the-art object insertion methods.

  3. Training-Free Illumination Transfer: The system can perform high-fidelity ad relighting without requiring millions of images of ad banners to be collected and trained upon, making the process practical for real-time or on-demand application in production pipelines.

  4. Improved Visual Quality Metrics: The resulting banner integration will demonstrate superior performance across established perceptual metrics like SSIM (Structural Similarity Index) and LPIPS (Learned Perceptual Image Patch Similarity), outperforming simple geometric warping and existing diffusion baselines, as shown in the quantitative results.

  5. User Preference Optimization: The system's outputs are statistically preferred by human evaluators (as demonstrated in the GPT-4o evaluation and user studies) across criteria such as Light Consistency and Scene Realism, indicating a high degree of perceptual quality suitable for consumer applications.

Abstract

The recent surge in content consumption through streaming services has driven a growing demand for personalized content. Personalized advertisements (ads) play a crucial role in enhancing both user engagement and ad effectiveness. A key aspect of ad personalization involves replacing existing regions in a frame with custom, Photoshop-generated banners. However, existing ad-placement pipelines typically rely on simple geometric warping, ignoring the scene's underlying lighting conditions. Similarly, state-of-the-art diffusion-based object insertion and relighting models struggle to accurately relight these newly inserted banners, as they are not trained on ad-banner data, and training such a model for ad banners would require millions of images. This highlights the need for an effective relighting framework that enables seamless integration of custom banners into the original scene. Motivated by this, we present AD-Relight, a novel multi-stage training-free framework that adapts a diffusion-based relighting model at test time to relight newly added Photoshop-generated ad banners. Through extensive evaluation, we demonstrate that AD-Relight outperforms both relighting baselines and existing ad-placement methods based on simple warping. User studies further show that participants consistently prefer the outputs of AD-Relight over those of prior approaches.

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