I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery

• URL: http://arxiv.org/abs/2609.12694v1
• Date: Fri, 11 Sep 2026 10:39:18 GMT
• Title: I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
• Abstract summary: We propose AdMan, a pipeline that transforms customer data into personas, and generates personalized advertisement images conditioned on product reference images.<n>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.<n>Our findings extend the literature on AI-based personalized advertising by demonstrating the feasibility and current limitations of fully automated image generation for advertising.
• Score: 4.662993987530914
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Enabling and Understanding Personalization in AI-Generated Advertising Imagery [4.346377939583986]
  We develop and implement an AI-based framework that generates personalized advertising imagery directly from customer data.<n>We evaluate this framework in a two-stage within-subject study with N=100 participants across four products.
  arXiv  Detail & Related papers  (2026-09-11T10:44:37Z)
• Ad Headline Generation using Self-Critical Masked Language Model [4.556167062902223]
  We propose a programmatic solution to generate product advertising headlines using retail content.<n>Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise.
  arXiv  Detail & Related papers  (2026-07-07T21:25:49Z)
• Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models [75.31791398000492]
  We develop a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts.<n>To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module.<n>Our method outperforms baselines in general and personalized advertisement generation.
  arXiv  Detail & Related papers  (2026-05-12T13:58:06Z)
• Multi-Object Advertisement Creative Generation [10.187584785251087]
  In furniture product marketing, advertisers often create lifestyle images containing products to resonate with potential buyers.<n>Recent advances in Generative Artificial Intelligence (GenAI) have given rise to realistic image content creation.<n>CreativeAds is a multi-product ad creation system that supports scalable automated generation.
  arXiv  Detail & Related papers  (2026-03-14T04:30:27Z)
• NextAds: Towards Next-generation Personalized Video Advertising [46.666292153282875]
  NextAds is a generation-based paradigm for next-generation personalized video advertising.<n>We formulate two representative tasks: personalized creative generation and personalized creative integration.<n>We conduct initial exploratory experiments, demonstrating that GenAI can generate and integrate personalized creatives with encouraging performance.
  arXiv  Detail & Related papers  (2026-03-02T17:58:07Z)
• CTR-Driven Advertising Image Generation with Multimodal Large Language Models [53.40005544344148]
  We explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective.<n>To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL)<n>Our method achieves state-of-the-art performance in both online and offline metrics.
  arXiv  Detail & Related papers  (2025-02-05T09:06:02Z)
• JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation [49.997839600988875]
  Existing personalization methods rely on finetuning a text-to-image foundation model on a user's custom dataset. We propose Joint-Image Diffusion (jedi), an effective technique for learning a finetuning-free personalization model. Our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
  arXiv  Detail & Related papers  (2024-07-08T17:59:02Z)
• PALP: Prompt Aligned Personalization of Text-to-Image Models [68.91005384187348]
  Existing personalization methods compromise personalization ability or the alignment to complex prompts. We propose a new approach focusing on personalization methods for a emphsingle prompt to address this issue. Our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts.
  arXiv  Detail & Related papers  (2024-01-11T18:35:33Z)
• Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
  We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
  arXiv  Detail & Related papers  (2022-07-25T14:37:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.