IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation
This work addresses challenges in generating consistent human characters across sequential images for story applications, representing an incremental improvement over existing methods.
The paper tackles the problem of maintaining consistent character identities in human-centric story generation by introducing IdentityStory, a framework that uses iterative identity discovery and re-denoising injection to improve face consistency and multi-character coordination, achieving superior performance on the ConsiStory-Human benchmark.
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.