SceneDecorator: Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency
Quanjian Song, Donghao Zhou, Jingyu Lin, Fei Shen, Jiaze Wang, Xiaowei Hu, Cunjian Chen, Pheng-Ann Heng
Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining narrative consistency across images. While existing solutions focus on character consistency, they overlook the crucial role of scenes in storytelling, restricting their creativity in practice. This paper introduces scene-oriented story generation, addressing two key challenges: (i) scene planning, where current methods fail to ensure narrative coherence across scenes due to independent scene generation, and(ii) scene consistency, which remains largely unexplored in terms of maintaining coherence across multiple storylines. We propose SceneDecorator, a training-free framework that employs VLM-Guided Scene Planning to ensure narrative coherence between different scenes in a ``global-to-local" manner, and Long-Term Scene-Sharing Attention to maintain scene consistency and subject style diversity across different stories. Comprehensive experiments demonstrate the superior performance of SceneDecorator, highlighting its potential to unleash creativity in the fields of arts, films, and games. Code will be released.