CAPGen: An MLLM-Based Framework Integrated with Iterative Optimization Mechanism for Cultural Artifacts Poster Generation

Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform, the demands of visual communication keep increasing for promoting traditional cultural artifacts online. As an effective medium, posters serve to attract public attention and facilitate broader engagement with cultural artifacts. However, existing poster generation methods mainly rely on fixed templates and manual design, which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts. Therefore, we propose CAPGen, an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model (MLLM) with integrated iterative optimization. During our research, we collaborated with designers to define principles of graphic design for cultural artifact posters, to guide the MLLM in generating layout parameters. Later, we generated these parameters into posters. Finally, we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism. Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance. Furthermore, ablation studies indicate that the prompt, iterative optimization mechanism, and design principles significantly enhance the effectiveness of poster generation.