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.
@article{arxiv.2512.23519,
title = {IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation},
author = {Donghao Zhou and Jingyu Lin and Guibao Shen and Quande Liu and Jialin Gao and Lihao Liu and Lan Du and Cunjian Chen and Chi-Wing Fu and Xiaowei Hu and Pheng-Ann Heng},
journal= {arXiv preprint arXiv:2512.23519},
year = {2025}
}
Comments
Accepted by AAAI2026 (Project page: https://correr-zhou.github.io/IdentityStory)