无需训练，用扩散先验实现广告牌自然光照融合
AD-Relight: Training-Free Banner Relighting via Illumination Translation with Diffusion Priors
- 测试时动态适配扩散模型，实现无训练光照迁移
- 在真实视频场景中使广告牌光照与环境一致，显著提升真实感
- 适合需快速生成逼真广告的影视内容生产者
流媒体内容消费激增推动个性化内容需求。个性化广告对提升用户参与度和广告效果至关重要，关键在于将自定义的Photoshop生成广告牌无缝插入画面。现有方法多依赖简单几何变形，忽略场景光照条件。当前基于扩散模型的物体插入与光照重绘方法因未在广告牌数据上训练，难以准确还原新插入广告牌的光照，而针对广告牌训练模型需数百万图像，成本过高。为此，我们提出AD-Relight，一种新型多阶段免训练框架，在测试时通过扩散先验动态调整光照模型，实现对新插入广告牌的精准光照重绘。大量评估表明，AD-Relight优于现有光照重绘基线及基于简单变形的广告插入方法。用户研究进一步显示，参与者更偏好AD-Relight的输出结果。
原文摘要 · Abstract (English)
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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