Authors: Minyan Luo, Yuxin Zhang, Meng Zhao, Xincan Wang, Fuzhang Wu, Tong-Yee Lee, Oliver Deussen, Fan Tang, Weiming Dong
Organizations: MAIS, Institute of Automation, CAS Beijing, China · School of Artificial Intelligence, UCAS Beijing, China · Shanghai Theatre Academy Shanghai, China · Institute of Software, CAS Beijing, China · National Cheng Kung University Tainan, Taiwan · University of Konstanz Konstanz, Germany · ICT, CAS Beijing, China
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.
Figures & tables
Figure 1. Creative advertising images generated by AdMuse. (a) One representative ad per bridge type from skill library. (b) A single skill applied across diverse products. (c) A single brief routed through different skills yields visually distinct ads.
Figure 2. AdMuse combines a creative skill library with a three-phase generation pipeline.