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This paper introduces Ad-Relight, an inference-only method that uses diffusion models to transfer scene illumination onto inserted advertising graphics without requiring specific training data. By separating shade from structure and leveraging pre-trained relighters, the approach significantly improves photometric consistency for floor-mounted signs under non-uniform lighting compared to geometric compositing baselines. While currently limited to static images, this work addresses a key challenge in realistic video insertion where temporal stability remains an open problem.
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Scene-Consistent Illumination Transfer for Inserted Advertising Graphics

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.37951v1 Announce Type: new Abstract: 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.

Abstract: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.

Machine Learning (cs.LG)

arXiv:2609.37951 [cs.LG]

(or arXiv:2609.37951v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.37951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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