AdMuse: Skill-Guided Creative Advertising Image Generation

An outstanding creative advertising image should both attract attention and communicate a product's positioning. How to choose a persuasive conceptual bridge before rendering begins is challenging for advertising generation. We present AdMuse, a skill-guided multi-agent framework that distills high-quality advertisements into reusable creative skills for cross-domain advertising strategy. Rather than retrieving isolated exemplars, AdMuse abstracts recurring bridge patterns into a skill library that can guide new product briefs. At generation time, AdMuse coordinates skill routing, multi-candidate ideation and selection, and VLM-guided visual refinement: diverse skills propose alternative concepts, relative judgment selects the most promising direction, and vision-language feedback helps realize the selected intent in the final image. This design treats advertising creativity as a structured decision process rather than a single prompt-writing step. Experiments across diverse product and brand positionings show that AdMuse produces visually compelling advertisements that faithfully communicate product positioning.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-29
DOI
https://doi.org/10.48550/arxiv.2610.40190
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
preprint
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AdMuse: Skill-Guided Creative Advertising Image Generation

Fuzhang Wu, Oliver Deußen, Weiming Dong, Minyan Luo et al.
arXiv (Cornell University)
Generative Adversarial Networks and Image Synthesis
preprint

AdMuse: Skill-Guided Creative Advertising Image Generation

Fuzhang Wu, Oliver Deußen, Weiming Dong, Minyan Luo, Xincan Wang, Tong-Yee Lee, Fan Tang, Yuxin Zhang, Meng Zhao
preprint en

Abstract

An outstanding creative advertising image should both attract attention and communicate a product's positioning. How to choose a persuasive conceptual bridge before rendering begins is challenging for advertising generation. We present AdMuse, a skill-guided multi-agent framework that distills high-quality advertisements into reusable creative skills for cross-domain advertising strategy. Rather than retrieving isolated exemplars, AdMuse abstracts recurring bridge patterns into a skill library that can guide new product briefs. At generation time, AdMuse coordinates skill routing, multi-candidate ideation and selection, and VLM-guided visual refinement: diverse skills propose alternative concepts, relative judgment selects the most promising direction, and vision-language feedback helps realize the selected intent in the final image. This design treats advertising creativity as a structured decision process rather than a single prompt-writing step. Experiments across diverse product and brand positionings show that AdMuse produces visually compelling advertisements that faithfully communicate product positioning.

arXiv (Cornell University)
Generative Adversarial Networks and Image Synthesis
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AdMuse: Skill-Guided Creative Advertising Image Generation — Fuzhang Wu, Oliver Deußen, et al. · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS