AniME: Adaptive Multi-Agent Anime Generation

• AniME is a director-oriented multi-agent system designed to generate long-form anime with coherent character and scene consistency.
• It employs a centralized Director Agent that orchestrates specialized agents using adaptive Model Context Protocol for precise control.
• The system integrates asset memory management to ensure persistent character, scene, and audio–visual synchronization throughout production.

Searching arXiv for the AniME paper and closely related anime-generation work to ground the article in current research. AniME, short for “Adaptive Multi-Agent Planning for Long Animation Generation,” denotes a director-oriented multi-agent system for automated long-form anime production that covers “the full workflow from a story to the final video.” Its defining premise is that long-form animation generation is not a single-model problem but an orchestration problem: a Director Agent maintains global memory across the workflow, coordinates specialized downstream agents, and uses customized Model Context Protocol (MCP) to let those agents adaptively select control conditions for diverse sub-tasks. In the formulation given for the system, AniME aims to transform a story script $\mathcal{R}$ into a final video $\mathcal{V}$ while preserving character consistency, scene/style coherence, and audio–visual synchronization (Zhang et al., 26 Aug 2025).

1. Concept and problem setting

AniME is presented as a response to a core difficulty in long animation generation: orchestrating many heterogeneous generative models while preserving cross-stage consistency. The architecture is explicitly framed as director-oriented. Rather than delegating the entire task to a single video model, the system decomposes production into planning, asset generation, animation synthesis, audio production, quality control, and final editing, each handled by a specialized agent under centralized coordination (Zhang et al., 26 Aug 2025).

This positioning matters because the paper treats long-form anime production as a dependency-structured workflow. The governing problem is not merely frame synthesis, but the maintenance of persistent identities, reusable assets, and synchronized modalities over an extended sequence of shots. A plausible implication is that AniME belongs to a line of work that shifts anime generation away from isolated image or video modules and toward production-level systems engineering. Related anime-specific work emphasizes individual subproblems—such as physics-guided animation from a static illustration in PhysAnimator (Xie et al., 27 Jan 2025), non-overlapped-view 3D character reconstruction in NOVA-3D (Wang et al., 2024), or artistic-correctness-oriented video generation in AniMatrix (Team, 5 May 2026)—whereas AniME is defined primarily by cross-module coordination rather than by a single generative backbone.

2. Director-centered architecture

The system is formally organized around a centralized Director Agent and multiple specialized agents. Each agent $A_i$ is described by an input type $\mathcal{I}_i$, an output type $\mathcal{O}_i$, and a local MCP toolbox $\mathcal{T}_i$. Inter-agent communication is carried out through structured JSON messages (Zhang et al., 26 Aug 2025).

The Director Agent acts as the control center. Its responsibilities include hierarchical decomposition of a long-form story into scenes and shots, style planning, task generation with chain-of-thought prompting, workflow graph management, quality assurance and revision control, and global memory maintenance. The workflow graph is written as

$\mathcal{W} = (N, E),$

with $N$ denoting task nodes and $E$ denoting dependency edges. The paper gives the dependency pattern character design $\rightarrow$ storyboard $\mathcal{V}$0 animation as a representative example of the graph structure (Zhang et al., 26 Aug 2025).

The operational workflow is summarized algorithmically. Starting from a story script $\mathcal{V}$1, the Director derives shots $\mathcal{V}$2 and styles $\mathcal{V}$3, generates a task list $\mathcal{V}$4 and dependency graph $\mathcal{V}$5, dispatches each task in topological order, evaluates returned outputs, requests revision if quality is low, stores accepted outputs in asset memory, and finally triggers final editing through the Video Editor Agent to obtain $\mathcal{V}$6 (Zhang et al., 26 Aug 2025). This suggests that AniME is not only modular but also explicitly dependency-aware: execution order is a first-class part of the method.

3. Asset memory and specialized agents

A central mechanism in AniME is the Asset Memory Bank, described as an Asset Memory Management module implemented with queryable database tables for efficient indexing, retrieval, and update operations. The paper enumerates asset tables for shot, scene, character, style, storyboard, video, and music. Their fields include, for example, description for shots, prompt and view_3d for scenes, demo_voice, voice_prompt, and 3d_view for characters, and video_path, shot_id, and music_id for video assets (Zhang et al., 26 Aug 2025).

This memory is the explicit mechanism by which the system attempts to preserve continuity across production stages. The paper states that it supports character identity consistency across shots, the reuse of canonical character views and voice prompts, scene continuity, and retrieval of corresponding music/audio assets during final assembly (Zhang et al., 26 Aug 2025). In long animation generation, these capabilities are structurally important because later agents can condition on archived assets instead of regenerating core design choices from scratch.

The specialized agents are differentiated by production role, input-output contract, and toolset.

Agent	Inputs and outputs	Tools / methods
Character Designer	Visual style, character description $\mathcal{V}$7 multi-view character image	Text-to-image generation, refinement, multi-view synthesis
Scene Designer	Visual style, scene description $\mathcal{V}$8 background images, layered assets	Depth-guided generation, layout-guided generation, relighting
Script / Storyboard Agent	Shot description text $\mathcal{V}$9 timeline, shot prompt, keyframes, camera motion	LLM-based segmentation and tagging, camera planner, layout planner
Animator	Keyframes, camera paths, rigs, poses, audio $A_i$0 frame sequences	Keyframe/audio/pose/camera-conditioned video generation
Audio Production	Dialogue with emotion labels, scene tags $A_i$1 phoneme-aligned audio	Speaker-conditioned TTS, text/video-to-music, audio mixer programs
Video Editor	Frame sequences, audio stems, editorial instructions $A_i$2 final encoded video	Transition effects, color pipeline, FFmpeg multi-pass encoding
Quality Evaluator	Generated frames and assets $A_i$3 verification results	Text-image similarity, identity verification, AV sync checks, VLM narrative evaluator

The paper also notes that human animation creators can intervene at any stage with revision suggestions. That point places AniME closer to an artist-guided production framework than to a strictly autonomous generator (Zhang et al., 26 Aug 2025).

4. Customized MCP and adaptive control selection

AniME’s principal technical novelty is its use of customized MCP integration as a control-selection layer. The MCP toolsets associated with downstream agents are not merely exposed APIs; they are explicitly annotated with each tool’s domain expertise, strengths, limitations, and applicability to sub-tasks (Zhang et al., 26 Aug 2025).

The resulting control logic is described procedurally. The Director sends a structured task specification with input type, output type, and constraints; the assigned specialized agent consults its MCP toolbox $A_i$4; the agent chooses the proper model for the task requirement; the chosen model is executed with task-specific controls; and the output is returned for evaluation and possible revision (Zhang et al., 26 Aug 2025). The Scene Designer may therefore choose among depth-guided generation, layout-guided generation, and relighting, whereas the Animator may select conditioning modes based on keyframes, audio, poses, or camera paths.

The significance of this design is that AniME does not hard-code a single generation model per stage. Instead, the architecture delegates model-choice decisions to specialized agents operating under structured tool metadata. This suggests that the framework treats production heterogeneity as endogenous to the method: diversity of tasks is handled not by a universal model, but by adaptive controller logic over a model library. In that respect, AniME differs conceptually from systems such as AniMatrix, which centers a single video-generation model augmented by a production knowledge system and dual-channel conditioning (Team, 5 May 2026).

5. End-to-end production workflow and consistency mechanisms

The full AniME pipeline proceeds from story input to Director planning, then through staged asset generation by specialized agents, followed by quality verification and revision, and finally final assembly into the output video $A_i$5 (Zhang et al., 26 Aug 2025). The Director first segments the story into scenes and shots, infers visual and audio styles $A_i$6 and $A_i$7, builds the dependency graph, and then executes tasks in graph order. Outputs are stored in the global memory so that later stages can reuse them.

Character consistency is achieved primarily through canonical character assets and memory reuse. The Character Designer generates multi-view portraits, identity embeddings, and canonical references, and these are stored in the character table together with prompt, demo_voice, voice_prompt, and 3d_view. Downstream agents then reuse those assets in storyboard generation, animation, voice production, and final shot assembly (Zhang et al., 26 Aug 2025). This suggests that identity preservation is not handled only by latent continuity or prompt reuse, but by persistent asset retrieval.

Audio–visual synchronization is likewise distributed across several stages. The Audio Production agent produces phoneme-aligned audio from dialogue with emotion labels and scene tags. The Animator receives keyframes, camera paths, rigs, poses, and audio, so animation synthesis is already conditioned on the temporal structure of the audio. The Video Editor then combines frame sequences, audio stems, editorial instructions, transition effects, and color processing, while the Quality Evaluator performs AV sync checks (Zhang et al., 26 Aug 2025). This architecture indicates that synchronization is treated as a pipeline-wide coordination problem rather than a post hoc adjustment.

A useful point of comparison is Anim-400K, which was introduced as a large JP–EN aligned animated-video dataset to support automated end-to-end dubbing and emphasizes the challenges of timing / isochrony, facial movement synchronization, and prosody matching (Cai et al., 2024). AniME addresses a different production target, but the overlap in synchronization concerns underscores that long-form animation systems increasingly depend on explicit cross-modal coordination rather than isolated generation modules.

6. Position in the research landscape, scope, and limitations

Within anime-oriented generative research, AniME occupies the systems-integration end of the spectrum. Other cited work addresses narrower technical layers of the production stack: LinkTo-Anime targets cel anime character motion with optical-flow supervision for anime video generation and line drawing colorization (Feng et al., 3 Jun 2025); NOVA-3D addresses 3D anime character reconstruction from front and back concept views (Wang et al., 2024); PhysAnimator combines image-space deformable body simulation with a sketch-guided video diffusion model to animate a single static illustration (Xie et al., 27 Jan 2025); and AniMatrix redefines anime video generation around artistic rather than physical correctness through a production taxonomy, dual-channel conditioning, and deformation-aware preference optimization (Team, 5 May 2026). AniME can therefore be understood as a framework for coordinating modules of the kind these papers exemplify, rather than replacing them with a single monolithic model.

The limitations described for AniME are chiefly architectural and evidentiary. The provided paper content includes no detailed experimental sections, quantitative benchmarks, ablation studies, or implementation hyperparameters (Zhang et al., 26 Aug 2025). There are no reported runtime figures, dataset comparisons, or user-study results in the supplied excerpt. Consequently, the paper’s claims about coherent long-form video generation, global consistency, adaptive control selection, and cinematic animation with synchronized audio–visual elements remain, in the available text, qualitative and architectural rather than empirically benchmarked (Zhang et al., 26 Aug 2025).

Several additional constraints are implicit in the description. The system’s output quality depends on the availability and performance of the downstream models exposed through MCP toolboxes; the centralized revision loop may introduce orchestration overhead; and the Director’s planning quality may depend on the robustness of segmentation and prompting over long, complex stories (Zhang et al., 26 Aug 2025). A plausible implication is that AniME’s central research contribution lies less in a proved best-in-class generator and more in a formalization of long-form anime production as a memory-backed, dependency-aware, multi-agent planning problem.

In that sense, AniME represents a specific stage in the evolution of anime-generation research. Earlier work often isolated a single task—portrait guidance, scene stylization, optical flow, 3D reconstruction, or dubbing—whereas AniME proposes that long animation generation should be treated as an integrated workflow whose central units are not only frames and tokens, but also tasks, assets, dependencies, and revisions (Zhang et al., 26 Aug 2025).