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title

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VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning

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site name

author

updated

2026-02-19 20:35:12

raw text

VideoDirectorGPT (COLM 2024) VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning Han Lin ,   Abhay Zala ,   Jaemin Cho ,   Mohit Bansal UNC Chapel Hill COLM 2024 Paper Code Abstract Although recent text-to-video (T2V) generation methods have seen significant advancements, the majority of these works focus on producing short video clips of a single event with a single background (i.e., single-scene videos). Meanwhile, recent large language models (LLMs) have demonstrated their capability in generating layouts and programs to control downstream visual modules such as image generation models. This raises an important question: can we leverage the knowledge embedded in these LLMs for temporally consistent long video generation? In this paper, we propose VideoDirectorGPT , a novel framework for consistent multi-scene video generation that uses the knowled...

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