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The recent innovations and breakthroughs in diffusion models havesignificantly expanded the possibilities of generating high-quality videos forthe given prompts. Most existing works tackle the single-scene scenario withonly one video event occurring in a single background. Extending to generatemulti-scene videos nevertheless is not trivial and necessitates to nicelymanage the logic in between while preserving the consistent visual appearanceof key content across video scenes. In this paper, we propose a novelframework, namely VideoDrafter, for content-consistent multi-scene videogeneration. Technically, VideoDrafter leverages Large Language Models (LLM) toconvert the input prompt into comprehensive multi-scene script that benefitsfrom the logical knowledge learnt by LLM. The script for each scene includes aprompt describing the event, the foreground/background entities, as well ascamera movement. VideoDrafter identifies the common entities throughout thescript and asks LLM to detail each entity. The resultant entity description isthen fed into a text-to-image model to generate a reference image for eachentity. Finally, VideoDrafter outputs a multi-scene video by generating eachscene video via a diffusion process that takes the reference images, thedescriptive prompt of the event and camera movement into account. The diffusionmodel incorporates the reference images as the condition and alignment tostrengthen the content consistency of multi-scene videos. Extensive experimentsdemonstrate that VideoDrafter outperforms the SOTA video generation models interms of visual quality, content consistency, and user preference.

VideoDrafter: Content-Consistent Multi-Scene Video Generation with LLM