我是广告人：个性化广告图像自动生成流水线
I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
- University of Bayreuth（拜罗伊特大学）
- Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对广告视觉呈现个性化不足的问题，提出AdMan多智能体流水线，将客户数据转为画像并生成个性化广告图像，经评估验证其可行性与局限性。
AI中文摘要：
个性化营销能够提升客户参与度、满意度和转化率。虽然现有的个性化方法在将合适的产品匹配给合适的客户方面已变得有效，但广告的视觉呈现仍然是一般化的，且对个体的针对性较弱。先前的研究表明，生成式人工智能可以改进个性化广告的创作，尤其是在文本方面，并且图像生成模型能够支持可扩展的广告生产。然而，很少有研究探讨如何在技术层面上，将详细的客户信息系统地转化为大规模、完全由AI生成的个性化广告图像。为弥补这一空白，我们提出了AdMan，一个多智能体流水线，它将客户数据转化为用户画像，基于产品参考图像生成个性化广告图像，并应用基于LLM的评判智能体进行自动化质量控制。我们使用两种不同的模型配置实现了该流水线，并针对四种产品进行了评估，使用六个名人用户画像进行定性检查，以及100个真实客户画像，共生成1745个广告。评估结合了定性专家焦点小组和定量伪影率评估。结果表明，该流水线能够生成照片级逼真的个性化广告。同时，性能因产品复杂度和模型配置的不同而有显著差异。我们的发现通过展示完全自动化图像生成用于广告的可行性和当前局限性，扩展了基于AI的个性化广告的文献。
英文摘要：
Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product to the right customer, the visual representation of advertisements remains generic and only weakly tailored to the individual. Prior research shows that generative artificial intelligence can improve the creation of personalized advertisements, particularly for text, and that image generation models can support scalable advertisement production. However, little research has examined how detailed customer information can be systematically translated into fully AI-generated, personalized advertising imagery at scale on a technical level. To address this gap, we propose AdMan, a multi-agent pipeline that transforms customer data into personas, generates personalized advertisement images conditioned on product reference images, and applies an LLM-based judge agent for automated quality control. We implement the pipeline with two different model configurations and evaluate it across four products, using six celebrity personas for qualitative inspection, and 100 real customer profiles, producing 1745 advertisements. The evaluation combines a qualitative expert focus group and a quantitative artifact-rate assessment. The results show that the pipeline can generate photorealistic and personalized advertisements. At the same time, performance varies substantially by product complexity and model configuration. Our findings extend the literature on AI-based personalized advertising by demonstrating the feasibility and current limitations of fully automated image generation for advertising.
