ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment

The paper proposes ICG, a novel framework for personalized cover image generation that integrates MLLM-based prompting with preference alignment. It utilizes semantic features and user embeddings to contextualize the diffusion model and adopts a multi-reward learning strategy to address the lack of labeled supervision.