IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation

Recent visual generative models enable story generation with consistent characters from text, but human-centric story generation faces additional challenges, such as maintaining detailed and diverse human face consistency and coordinating multiple characters across different images. This paper presents IdentityStory, a framework for human-centric story generation that ensures consistent character identity across multiple sequential images. By taming identity-preserving generators, the framework features two key components: Iterative Identity Discovery, which extracts cohesive character identities, and Re-denoising Identity Injection, which re-denoises images to inject identities while preserving desired context. Experiments on the ConsiStory-Human benchmark demonstrate that IdentityStory outperforms existing methods, particularly in face consistency, and supports multi-character combinations. The framework also shows strong potential for applications such as infinite-length story generation and dynamic character composition.

Authors

• Donghao Zhou The Chinese University of Hong Kong
• Jingyu Lin
• Guibao Shen The Hong Kong University of Science and Technology (Guangzhou)
• Quande Liu
• Jialin Gao
• Lihao Liu Shanghai Artificial Intelligence Laboratory
• Lan Du Monash University
• Cunjian Chen
• Chi-Wing Fu The Chinese University of Hong Kong
• Xiaowei Hu Shanghai Artificial Intelligence Laboratory
• Pheng-Ann Heng The Chinese University of Hong Kong

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