DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework
This work addresses a challenge in video generation for applications requiring extended narratives, though it appears incremental in its approach.
The paper tackles the problem of generating long, multi-scene videos, which current models struggle with, by introducing DreamFactory, an LLM-based multi-agent framework that produces stylistically coherent and complex videos, supported by new metrics and a dataset of over 150 human-rated videos.
Current video generation models excel at creating short, realistic clips, but struggle with longer, multi-scene videos. We introduce \texttt{DreamFactory}, an LLM-based framework that tackles this challenge. \texttt{DreamFactory} leverages multi-agent collaboration principles and a Key Frames Iteration Design Method to ensure consistency and style across long videos. It utilizes Chain of Thought (COT) to address uncertainties inherent in large language models. \texttt{DreamFactory} generates long, stylistically coherent, and complex videos. Evaluating these long-form videos presents a challenge. We propose novel metrics such as Cross-Scene Face Distance Score and Cross-Scene Style Consistency Score. To further research in this area, we contribute the Multi-Scene Videos Dataset containing over 150 human-rated videos.