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.

It should be clear by now that automation and artificial intelligence are about to hit the enterprise in a big way. But while this transformation will be rapid, it will not happen across the entire IT stack all at once. So where will this new computing paradigm first make its presence known in the enterprise? According to leading researchers, the most obvious candidates are the productivity applications like Enterprise Resource Planning (ERP) and Customer Relations Management (CRM) that have already subsumed much of the IT operational model, and this will effectively create a data environment that will, for the most part, manage itself in response to changing workload requirements. Gartner's David Cearley, for example, notes that artificial intelligence and machine learning have already shown a marked propensity to understand, learn, predict and adapt to a wide range of events, to the point that they can function autonomously even in complex environments.