AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration
- URL: http://arxiv.org/abs/2511.17906v1
- Date: Sat, 22 Nov 2025 04:03:32 GMT
- Title: AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration
- Abstract summary: Animation pre-production lays the foundation of an animated film by transforming initial concepts into a coherent blueprint across interdependent stages such as ideation, scripting, design, and storyboarding.<n>While generative AI tools are increasingly adopted in this process, they remain isolated, requiring creators to juggle multiple systems without integrated workflow support.<n>Our study with 12 professional creative directors and independent animators revealed key challenges in their current practice: Creators must manually coordinate fragmented outputs, manage large volumes of information, and struggle to maintain continuity and creative control between stages.<n>Based on the insights, we present AnimAgents, a human-multi-agent collaborative system
- Score: 16.753585127545445
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Animation pre-production lays the foundation of an animated film by transforming initial concepts into a coherent blueprint across interdependent stages such as ideation, scripting, design, and storyboarding. While generative AI tools are increasingly adopted in this process, they remain isolated, requiring creators to juggle multiple systems without integrated workflow support. Our formative study with 12 professional creative directors and independent animators revealed key challenges in their current practice: Creators must manually coordinate fragmented outputs, manage large volumes of information, and struggle to maintain continuity and creative control between stages. Based on the insights, we present AnimAgents, a human-multi-agent collaborative system that coordinates complex, multi-stage workflows through a core agent and specialized agents, supported by dedicated boards for the four major stages of pre-production. AnimAgents enables stage-aware orchestration, stage-specific output management, and element-level refinement, providing an end-to-end workflow tailored to professional practice. In a within-subjects summative study with 16 professional creators, AnimAgents significantly outperformed a strong single-agent baseline that equipped with advanced parallel image generation in coordination, consistency, information management, and overall satisfaction (p < .01). A field deployment with 4 creators further demonstrated AnimAgents' effectiveness in real-world projects.
Related papers
- FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production [55.71860113185132]
We introduce FRAMEWORKERS, a task-centric and workspace-grounded multi-agent framework for open-ended video production.<n>A central Director formulates video creation as dynamic task management, continuously editing a Task Stack to determine which subtask to execute next and which sub-agent to invoke.<n>An Assistant serves as the execution layer, grounding each selected task in a shared Workspace, retrieving the required assets and context, invoking the assigned sub-agent, and persisting the resulting artifacts.<n>Experiments show that FRAMEWORKERS outperforms strong planners in routing accuracy, recovers reliably from runtime failures, generalizes to unseen sub
arXiv Detail & Related papers (2026-08-30T14:28:50Z) - Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
arXiv Detail & Related papers (2026-05-14T14:36:13Z) - Effective Strategies for Asynchronous Software Engineering Agents [52.76455094324273]
We introduce a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous execution, and isolated workspaces.<n>We find that CAID improves accuracy over single-agent baselines by 26.7% absolute on paper reproduction tasks (PaperBench) and 14.3% on Python library development tasks (Commit0).
arXiv Detail & Related papers (2026-03-23T02:26:35Z) - Mind-of-Director: Multi-modal Agent-Driven Film Previsualization via Collaborative Decision-Making [35.41772376528704]
We present Mind-of-Director, a multi-modal agent-driven framework for film previz.<n>Given a creative idea, Mind-of-Director orchestrates multiple specialized agents to produce previz sequences within the game engine.<n>A real-time visual editing system built in the game engine further enables interactive inspection and synchronized timeline adjustment.
arXiv Detail & Related papers (2026-03-16T03:40:22Z) - MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
arXiv Detail & Related papers (2026-02-03T09:47:49Z) - 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) - A Flexible Multi-Agent LLM-Human Framework for Fast Human Validated Tool Building [0.8373057326694192]
We introduce CollabToolBuilder, a flexible multiagent LLM framework with expert-in-the-loop (HITL) guidance.<n>It iteratively learns to create tools for a target goal, aligning with human intent and process.<n>The architecture generates and validates tools via four specialized agents.
arXiv Detail & Related papers (2025-12-01T09:19:18Z) - Hollywood Town: Long-Video Generation via Cross-Modal Multi-Agent Orchestration [73.65102758687289]
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.
arXiv Detail & Related papers (2025-10-25T20:34:18Z) - Emergent Coordination in Multi-Agent Language Models [2.504366738288215]
We introduce an information-theoretic framework to test whether multi-agent systems show signs of higher-order structure.<n>This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems.<n>We apply our framework to experiments using a simple guessing game without direct agent communication.
arXiv Detail & Related papers (2025-10-05T11:26:41Z) - Orchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challenge [9.36518257854918]
This paper presents a research vision for autonomous agentic systems that orchestrate collaboration within dynamic human-AI teams.<n>We propose the Autonomous Manager Agent as a core challenge: an agent that decomposes complex goals into task graphs, allocates tasks to human and AI workers, monitors progress, and maintains transparent stakeholder communication.<n>We release MA-Gym, an open-source simulation and evaluation framework for multi-agent workflow orchestration.
arXiv Detail & Related papers (2025-10-02T20:51:39Z) - Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
arXiv Detail & Related papers (2025-05-29T07:24:37Z) - Multi-Agent Collaboration via Evolving Orchestration [55.574417128944226]
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
arXiv Detail & Related papers (2025-05-26T07:02:17Z) - 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) - MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration [11.01813164951313]
This paper introduces MorphAgent, a novel Autonomous, Self-Organizing, and Self-Adaptive Multi-Agent System.<n>Our approach employs self-evolving agent profiles, optimized through three key metrics, guiding agents in refining their individual expertise.<n>Our experimental results show that MorphAgent outperforms existing frameworks in terms of task performance and adaptability to changing requirements.
arXiv Detail & Related papers (2024-10-19T09:10:49Z) - ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems [80.69865295743149]
This work attempts to study using LLM-based agents to design collaborative AI systems autonomously.<n>Based on ComfyBench, we develop ComfyAgent, a framework that empowers agents to autonomously design collaborative AI systems by generating.<n>While ComfyAgent achieves a comparable resolve rate to o1-preview and significantly surpasses other agents on ComfyBench, ComfyAgent has resolved only 15% of creative tasks.
arXiv Detail & Related papers (2024-09-02T17:44:10Z) - AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition [149.89952404881174]
AutoDirector is an interactive multi-sensory composition framework that supports long shots, special effects, music scoring, dubbing, and lip-syncing.
It improves the efficiency of multi-sensory film production through automatic scheduling and supports the modification and improvement of interactive tasks to meet user needs.
arXiv Detail & Related papers (2024-08-21T12:18:22Z)
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.