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Accurate Story visualization requires several necessary elements, such asidentity consistency across frames, the alignment between plain text and visualcontent, and a reasonable layout of objects in images. Most previous worksendeavor to meet these requirements by fitting a text-to-image (T2I) model on aset of videos in the same style and with the same characters, e.g., theFlintstonesSV dataset. However, the learned T2I models typically struggle toadapt to new characters, scenes, and styles, and often lack the flexibility torevise the layout of the synthesized images. This paper proposes a system forgeneric interactive story visualization, capable of handling multiple novelcharacters and supporting the editing of layout and local structure. It isdeveloped by leveraging the prior knowledge of large language and T2I models,trained on massive corpora. The system comprises four interconnectedcomponents: story-to-prompt generation (S2P), text-to-layout generation (T2L),controllable text-to-image generation (C-T2I), and image-to-video animation(I2V). First, the S2P module converts concise story information into detailedprompts required for subsequent stages. Next, T2L generates diverse andreasonable layouts based on the prompts, offering users the ability to adjustand refine the layout to their preference. The core component, C-T2I, enablesthe creation of images guided by layouts, sketches, and actor-specificidentifiers to maintain consistency and detail across visualizations. Finally,I2V enriches the visualization process by animating the generated images.Extensive experiments and a user study are conducted to validate theeffectiveness and flexibility of interactive editing of the proposed system.

TaleCrafter: Interactive Story Visualization with Multiple Characters