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

• URL: http://arxiv.org/abs/2512.23519v1
• Date: Mon, 29 Dec 2025 14:54:44 GMT
• Title: IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation
• Authors: Donghao Zhou, Jingyu Lin, Guibao Shen, Quande Liu, Jialin Gao, Lihao Liu, Lan Du, Cunjian Chen, Chi-Wing Fu, Xiaowei Hu, Pheng-Ann Heng,
• Abstract summary: IdentityStory is a framework for human-centric story generation that ensures consistent character identity across sequential images.<n>By taming identity-preserving generators, the framework features two key components: Iterative Identity Discovery and Re-denoising Identity Injection.
• Score: 75.09818147405898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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