Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration

• URL: http://arxiv.org/abs/2510.22431v1
• Date: Sat, 25 Oct 2025 20:34:18 GMT
• Title: Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration
• Authors: Zheng Wei, Mingchen Li, Zeqian Zhang, Ruibin Yuan, Pan Hui, Huamin Qu, James Evans, Maneesh Agrawala, Anyi Rao,
• Abstract summary: This study introduces three innovations to improve multi-agent collaboration.<n>First, we propose OmniAgent, a hierarchical, graph-based multi-agent framework for long video generation.<n>Second, inspired by context engineering, we propose hypergraph nodes that enable temporary group discussions.
• Score: 73.65102758687289
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• GenAgent: Scaling Text-to-Image Generation via Agentic Multimodal Reasoning [54.42973725693]
  We introduce GenAgent, unifying visual understanding and generation through an agentic multimodal model.<n>GenAgent significantly boosts base generator(FLUX.1-dev) performance on GenEval++ and WISE.<n>Our framework demonstrates three key properties: 1) cross-tool generalization to generators with varying capabilities, 2) test-time scaling with consistent improvements across interaction rounds, and 3) task-adaptive reasoning that automatically adjusts to different tasks.
  arXiv  Detail & Related papers  (2026-01-26T14:49:04Z)
• A Versatile Multimodal Agent for Multimedia Content Generation [66.86040734610073]
  We propose a MultiMedia-Agent designed to automate complex content creation tasks.<n>Our agent system includes a data generation pipeline, a tool library for content creation, and a set of metrics for evaluating preference alignment.
  arXiv  Detail & Related papers  (2026-01-06T18:49:47Z)
• Diffusion Forcing for Multi-Agent Interaction Sequence Modeling [52.769202433667125]
  MAGNet is a unified autoregressive diffusion framework for multi-agent motion generation.<n>It supports a wide range of interaction tasks through flexible conditioning and sampling.<n>It captures both tightly synchronized activities and loosely structured social interactions.
  arXiv  Detail & Related papers  (2025-12-19T18:59:02Z)
• UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist [107.04196084992907]
  We introduce UniVA, an omni-capable multi-agent framework for next-generation video generalists.<n>UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow.<n>We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation.
  arXiv  Detail & Related papers  (2025-11-11T17:58:13Z)
• GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration [20.988801611785522]
  We propose GenMAC, an iterative, multi-agent framework that enables compositional text-to-video generation.<n>The collaborative workflow includes three stages: Design, Generation, and Redesign.<n>To tackle diverse scenarios of compositional text-to-video generation, we design a self-routing mechanism to adaptively select the proper correction agent from a collection of correction agents each specialized for one scenario.
  arXiv  Detail & Related papers  (2024-12-05T18:56:05Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [76.3175166538482]
  VideoGen-of-Thought (VGoT) is a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT addresses three core challenges: Narrative fragmentation, visual inconsistency, and transition artifacts.<n>Combined in a training-free pipeline, VGoT surpasses strong baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2024-12-03T08:33:50Z)
• StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration [88.94832383850533]
  We propose a multi-agent framework designed for Customized Storytelling Video Generation (CSVG) StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process. Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency. Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.
  arXiv  Detail & Related papers  (2024-11-07T18:00:33Z)
• Scaling Large Language Model-based Multi-Agent Collaboration [72.8998796426346]
  Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning.<n>This study explores whether the continuous addition of collaborative agents can yield similar benefits.
  arXiv  Detail & Related papers  (2024-06-11T11:02:04Z)
• AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production [34.665965986359645]
  AesopAgent is an Agent-driven Evolutionary System on Story-to-Video Production. The system integrates multiple generative capabilities within a unified framework, so that individual users can leverage these modules easily. Our AesopAgent achieves state-of-the-art performance compared with many previous works in visual storytelling.
  arXiv  Detail & Related papers  (2024-03-12T02:30:50Z)
• Collaborative Multi-Agent Video Fast-Forwarding [30.843484383185473]
  We develop two collaborative multi-agent video fast-forwarding frameworks in distributed and centralized settings. In these frameworks, each individual agent can selectively process or skip video frames at adjustable paces based on multiple strategies. We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.
  arXiv  Detail & Related papers  (2023-05-27T20:12:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.