Current story visualization methods tend to position subjects solely by text and face challenges in maintaining artistic consistency. To address these limitations, we introduce DreamingComics, a layout-aware story visualization framework. We build upon a pretrained video diffusion-transformer (DiT) model, leveraging its spatiotemporal priors to enhance identity and style consistency. For layout-based position control, we propose RegionalRoPE, a region-aware positional encoding scheme that re-indexes embeddings based on the target layout. Additionally, we introduce a masked condition loss to further constrain each subject's visual features to their designated region. To infer layouts from natural language scripts, we integrate an LLM-based layout generator trained to produce comic-style layouts, enabling flexible and controllable layout conditioning. We present a comprehensive evaluation of our approach, showing a 29.2% increase in character consistency and a 36.2% increase in style similarity compared to previous methods, while displaying high spatial accuracy. Our project page is available at this https URL
DreamingComics：一种基于视频模型、通过主体与布局定制生成的故事可视化流程 / DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models
这篇论文提出了一个名为DreamingComics的故事可视化新方法，它通过改进的视频模型和创新的布局控制技术，能根据文字脚本自动生成漫画风格且角色与画风高度一致的连续画面。