Zheng Wei, Mingchen Li, Zeqian Zhang, Ruibin Yuan, Pan Hui, Huamin Qu, James A Evans, Maneesh Agrawala, Anyi Rao

1.The Hong Kong University of Science and Technology 2.University of Chicago 3.Stanford University

Demo：https://olpleo.github.io/

arXiv：https://arxiv.org/abs/2510.22431

Recent advancements in multi-agent systems have demonstrated significant potential for enhancing creative task performance, such as long video generation. This study introduces three innovations to improve multi-agent collaboration. First, we propose Hollywood Town, a hierarchical, graph-based multi-agent framework for long video generation that leverages a film-production-inspired architecture to enable modular specialization and scalable inter-agent collaboration. Second, inspired by context engineering, we propose hypergraph nodes that enable temporary group discussions among agents lacking sufficient context, reducing individual memory requirements while ensuring adequate contextual information. Third, we transition from directed acyclic graphs (DAGs) to directed cyclic graphs with limited retries, allowing agents to reflect and refine outputs iteratively, thereby improving earlier stages through feedback from subsequent nodes. These contributions lay the groundwork for developing more robust multi-agent systems in creative tasks.

Zheng Wei, Hongtao Wu, lvmin Zhang, Xian Xu, Yefeng Zheng, Pan Hui, Maneesh Agrawala, Huamin Qu, Anyi Rao

1.The Hong Kong University of Science and Technology 2. West Lake University 3.Stanford University

Demo：http://221.4.186.36:29757

Paper：https://dl.acm.org/doi/10.1145/3746059.3747793

Effective communication between directors and cinematographers is fundamental in film production, yet traditional approaches relying on visual references and hand-drawn storyboards often lack the efficiency and precision necessary during pre-production. We present CineVision, an AI-driven platform that integrates scriptwriting with real-time visual pre-visualization to bridge this communication gap. By offering dynamic lighting control, style emulation based on renowned filmmakers, and customizable character design, CineVision enables directors to convey their creative vision with heightened clarity and rapidly iterate on scene composition. In a 24-participant lab study, CineVision yielded shorter task times and higher usability ratings than two baseline methods, suggesting a potential to ease early-stage communication and accelerate storyboard drafts under controlled conditions. These findings underscore CineVision’s potential to streamline pre-production processes and foster deeper creative synergy among filmmaking teams, particularly for new collaborators.