Pipeline generates personalized photorealistic advertising images using AI
I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
Artificial IntelligenceHuman-Computer Interaction
Summary
Personalized ads usually match products to people but often look generic. The authors created a system called AdMan that uses customer info and AI to make custom ad images that look real. These ads change based on who the customer is, the product, and can be made automatically at large scale. The system's quality varies depending on the product and the AI model used, showing both promise and current challenges. This work helps understand how AI might fully automate personalized advertising images someday.
What this means in practice
- •For advertising technology teams: Automatically create personalized ad images tailored to individual customer profiles at large scale using AI.$Commercial implications: Enables selling personalized ad generation services for marketing firms that want to automate visually customized campaigns.
- •For e-commerce marketing teams: Generate customized advertisement visuals for different products and customer types without manual image design.
Authors
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke, Niklas Kühl
Abstract
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.