From Pixels to Context: Adapting Generative Models for Advertising at Scale

Hyunhee Chung, Taeyoung Na; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 6130-6137

As marketing shifts toward hyper-personalization, advertisers seek to generate customized advertisement posters at scale--an inherently challenging task for traditional heuristic workflows. Generative AI offers a promising solution, but its adaptation to real-world advertising presents two key challenges: (1) generalized models fail to precisely capture target tasks, requiring personalized models. However, selecting optimal training samples and defining their inclusion criteria remains an inefficient trial-and-error process, and (2) fine-tuning models without sacrificing generative diversity and controllability, where controllability in advertisement poster generation specifically requires preserving the input product image without distortion. Existing methods rely on ad-hoc dataset selection and often constrain latent spaces, leading to suboptimal personalization. To address this, we introduce DCD-Pipeline (Directional Context Derivative Pipeline) for systematic in-context data selection and DBA-Attention (Dual-Branch Adaptive Attention) for preserving both generalization and personalization through separate attention branches. Applied to advertising poster generation, our approach significantly improves context-aware, high-fidelity content creation, demonstrating the potential of Generative AI in scalable, industry-driven applications.