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
- Fuzhang Wu (ORCID: https://orcid.org/0000-0002-3843-7406)
- Oliver Deußen (ORCID: https://orcid.org/0000-0001-5803-2185)
- Weiming Dong (ORCID: https://orcid.org/0000-0001-6502-145X)
- Minyan Luo
- Xincan Wang
- Tong-Yee Lee
- Fan Tang
- Yuxin Zhang
- Meng Zhao
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