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, Chi-Wing Fu, Xiaowei Hu, Pheng-Ann Heng

TL;DR
IdentityStory introduces a novel framework for human-centric story generation that maintains consistent character identities across multiple images, improving face consistency and enabling complex multi-character stories.
Contribution
The paper proposes a new method combining iterative identity discovery and re-denoising identity injection to enhance human face consistency in story generation.
Findings
Outperforms existing methods on ConsiStory-Human benchmark
Achieves superior face consistency across images
Supports multi-character story generation
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications · Face recognition and analysis
