IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation
Donghao Zhou, Jingyu Lin, Guibao Shen, Quande Liu, Jialin Gao, Lihao Liu, Lan Du, Cunjian Chen
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.
BibTeX
@inproceedings{aaai2026_identitystorytam,
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},
booktitle = {AAAI 2026},
year = {2026}
}