Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration
This work addresses the challenge of generating coherent long videos for creative applications, representing an incremental advancement in multi-agent systems for video generation.
The paper tackles the problem of long video generation by introducing a hierarchical multi-agent framework with innovations in agent collaboration, context sharing, and iterative refinement, achieving improved performance in generating coherent long videos.
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 OmniAgent, 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.