Replacing a visible advertisement in a broadcast frame is geometrically straightforward but photometrically delicate. A pasted graphic can have the correct perspective and still appear detached when its brightness, shading, or shadow disagrees with the surface beneath it. This paper presents Ad-Relight, an inference-only procedure for transferring scene illumination to a supplied advertising graphic without collecting a banner-specific training set. The procedure first separates slowly varying shade from graphic structure, then probes a pretrained diffusion relighter with two nearly identical backgrounds to isolate the contribution of the target region. A final pass combines this residual with a smoothed luminance field and a soft attenuation mask. Across 560 generated placements, the approach improves structural similarity, perceptual distance, and illumination agreement over geometric compositing and direct relighting baselines. Human judgments and an automated preference study show the clearest gains on floor-mounted graphics with nonuniform lighting. The current study is image based; temporal stabilization remains an open extension.
Figures & tables
Fig. 1: A supplied graphic can be geometrically correct yet visually detached from its host surface. The examples motivate matching spatial brightness changes, rather than treating the replacement as a uniformly lit texture.
Fig. 2: Processing graph for Ad-Relight. A low-frequency shade field is transferred to the supplied graphic, two backbone queries provide a target-region residual, and a mixture of that residual with a smoothed luminance guide controls the final relighting pass.
Method
SSIM ↑
LPIPS ↓
ILL-SIM ↑
Warp-only
0.89
0.12
0.82
Training-free compositor
0.45
0.57
0.62
Direct relighting
0.91
0.07
0.77
Shadow-guided relighting
0.92
0.09
0.79
Ad-Relight
0.95
0.03
0.92
TABLE I: Frame-level comparison on 560 placements. Higher is preferred for SSIM and ILL-SIM; lower is preferred for LPIPS.
Fig. 3: Representative replacements arranged by scene and method. Geometric pasting leaves the graphic uniformly lit, while direct relighting can borrow texture from the floor. The proposed pipeline preserves the logo and follows the host surface’s broad brightness changes.
Variant
SSIM ↑
LPIPS ↓
ILL-SIM ↑
M1
0.92
0.03
0.85
M2
0.91
0.05
0.87
M3 ( −G )
0.89
0.07
0.83
M4 ( − shade)
0.86
0.05
0.80
M5 ( −ϵ )
0.80
0.10
0.85
Full
0.95
0.03
0.92
TABLE II: Ablation on the same benchmark.
Fig. 4: Ablation examples. Removing a stage changes either the banner’s contact with the floor or the strength of the recovered illumination gradient.
Criterion
Warp
Direct
Shadow-guided
Gradient fidelity
100.0
97.6
95.2
Lighting agreement
97.6
95.2
92.8
Scene realism
90.4
92.8
85.7
TABLE III: Percentage of GPT-4o pairwise choices favoring Ad-Relight.
Fig. 5: Human preference rates for two comparisons. Green bars indicate selections of Ad-Relight.
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.
Rameshwar Mishra, Bishshoy Das, A V Subramanyam +1
Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-based methods often suffer from unstable illumination changes or inconsistent content preservation under complex lighting, as they lack an explicit mechanism to learn feature transformations between images. We reformulate relighting as an illumination feature transport problem and introduce Consistent Feature Transport (CFT), a training principle that explicitly enforces illumination-consistent transport between source and target image distributions. Built upon rectified flow, CFT jointly models noise-to-image generation and illumination-consistent source-to-target transport through trajectory-level supervision. This dual-transport formulation encourages isolation of illumination-specific variations while preserving content-aligned features. To support complex lighting scenarios, we construct a large-scale portrait relighting dataset with diverse relighting effects. Experiments show consistent improvements over existing state-of-the-art relighting approaches and demonstrate that CFT can generalize to other editing tasks, including style transfer. Code is available at https://github.com/Dixin-Lab/CFT.
Bohan Zhang, Huanwei Liang, Yuhan He +5
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China · MT Lab, Meitu Inc., Beijing 100083, China · Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China
Indoor scene relighting demands photorealism, precise spatial control, and strict multi-view consistency. While diffusion-based image editing models enable semantic lighting manipulation via text prompts, enforcing exact 3D light placement often disrupts their generative priors. We propose Lume-Palette, a progressive framework that leverages semantic lighting priors for spatially controllable multi-view indoor relighting. The approach decouples relighting into two stages: (1) illumination distillation, which extracts canonical illumination palettes from a pretrained diffusion model to preserve realistic material-light interactions, and (2) illumination casting, which explicitly maps target spatial lighting conditions defined from coarse 3D geometry. To efficiently handle dense multi-view and multi-modal inputs, we introduce an asymmetric multi-view conditioning strategy that selectively injects essential spatial context. Experiments on diverse synthetic scenes and real-world scenes demonstrate that Lume-Palette produces photorealistic, spatially controllable, and multi-view consistent relighting results. Project Page: https://cjeen.github.io/lumepalette
Chenjian Gao, Linning Xu, Tianfan Xue
Multimedia Laboratory, The Chinese University of Hong Kong · Shanghai AI Laboratory · CPII under InnoHK