Multi-Object Advertisement Creative Generation

2026-03-14

AI Summary

This work addresses the challenge that existing generative AI systems struggle to automatically produce high-quality lifestyle-scene advertisements featuring multiple products in e-commerce, often requiring manual intervention that hinders scalability. To overcome this limitation, we propose CreativeAds, a novel system introducing a three-stage generation pipeline—product pairing, layout generation, and background composition—specifically designed for multi-product advertising. By integrating state-of-the-art image generation models into a modular architecture with an intuitive user interface, CreativeAds enables both batch production and fine-grained control. User studies and image quality evaluations demonstrate that non-expert users can efficiently generate highly realistic and customized multi-product ad images, effectively balancing automation, scalability, and user controllability without requiring generative AI expertise.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Lifestyle images are photographs that capture environments and objects in everyday settings. In furniture product marketing, advertisers often create lifestyle images containing products to resonate with potential buyers, allowing buyers to visualize how the products fit into their daily lives. While recent advances in Generative Artificial Intelligence (GenAI) have given rise to realistic image content creation, their application in e-commerce advertising is challenging because high-quality ads must authentically representing the products in realistic scearios. Therefore, manual intervention is usually required for individual generations, making it difficult to scale to larger product catalogs. To understand the challenges faced by advertisers using GenAI to create lifestyle images at scale, we conducted evaluations on ad images generated using state-of-the-art image generation models and identified the major challenges. Based on our findings, we present CreativeAds, a multi-product ad creation system that supports scalable automated generation with customized parameter adjustment for individual generation. To ensure automated high-quality ad generation, CreativeAds innovates a pipeline that consists of three modules to address challenges in product pairing, layout generation, and background generation separately. Furthermore, CreativeAds contains an intuitive user interface to allow users to oversee generation at scale, and it also supports detailed controls on individual generation for user customized adjustments. We performed a user study on CreativeAds and extensive evaluations of the generated images, demonstrating CreativeAds's ability to create large number of high-quality images at scale for advertisers without requiring expertise in GenAI tools.

Problem

• Multi-Object Advertisement
• Lifestyle Image Generation
• Generative AI
• E-commerce Advertising
• Scalable Ad Creation

Innovation

• multi-object advertisement generation
• generative AI for e-commerce
• automated creative pipeline
• scalable ad creation
• lifestyle image synthesis