# Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation

arXiv: https://arxiv.org/abs/2408.09787 · HTML: https://mlsanity.com/paper/2408.09787/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 35)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://mlsanity.com/paper/2305.07759/) (2023)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception](https://mlsanity.com/paper/2401.16158/) (2024)
- [LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding](https://mlsanity.com/paper/2306.17107/) (2023)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [Controllable Text-to-Image Generation](https://mlsanity.com/paper/1909.07083/) (2019)
- [RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths](https://mlsanity.com/paper/2305.18295/) (2023)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://mlsanity.com/paper/1812.02784/) (2018)
- [Expressive Text-to-Image Generation with Rich Text](https://mlsanity.com/paper/2304.06720/) (2023)
- [Make-A-Story: Visual Memory Conditioned Consistent Story Generation](https://mlsanity.com/paper/2211.13319/) (2022)
- [TaleCrafter: Interactive Story Visualization with Multiple Characters](https://mlsanity.com/paper/2305.18247/) (2023)
- [Vlogger: Make Your Dream A Vlog](https://mlsanity.com/paper/2401.09414/) (2024)
- [MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning](https://mlsanity.com/paper/2401.10727/) (2024)
- [Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation](https://mlsanity.com/paper/2305.09662/) (2023)
- [Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization](https://mlsanity.com/paper/2110.10834/) (2021)
- [Breathing Life Into Sketches Using Text-to-Video Priors](https://mlsanity.com/paper/2311.13608/) (2023)
- [Create Your World: Lifelong Text-to-Image Diffusion](https://mlsanity.com/paper/2309.04430/) (2023)

## Built on by (most cited first, 30 of 37)

- [Mind the Time: Temporally-Controlled Multi-Event Video Generation](https://mlsanity.com/paper/2412.05263/) (2024)
- [OmniHuman-1.5: Instilling an Active Mind in Avatars via Cognitive Simulation](https://mlsanity.com/paper/2508.19209/) (2025)
- [FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces](https://mlsanity.com/paper/2501.12909/) (2025)
- [FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation](https://mlsanity.com/paper/2506.18899/) (2025)
- [AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation](https://mlsanity.com/paper/2506.10540/) (2025)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing](https://mlsanity.com/paper/2605.30117/) (2026)
- [MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation](https://mlsanity.com/paper/2602.23969/) (2026)
- [MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent](https://mlsanity.com/paper/2502.03207/) (2025)
- [AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video Generation](https://mlsanity.com/paper/2506.03126/) (2025)
- [The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation](https://mlsanity.com/paper/2601.17737/) (2026)
- [AniME: Adaptive Multi-Agent Planning for Long Animation Generation](https://mlsanity.com/paper/2508.18781/) (2025)
- [TalkCuts: A Large-Scale Dataset for Multi-Shot Human Speech Video Generation](https://mlsanity.com/paper/2510.07249/) (2025)
- [ViStoryBench: Comprehensive Benchmark Suite for Story Visualization](https://mlsanity.com/paper/2505.24862/) (2025)
- [MUSE: A Multi-agent Framework for Unconstrained Story Envisioning via Closed-Loop Cognitive Orchestration](https://mlsanity.com/paper/2602.03028/) (2026)
- [AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?](https://mlsanity.com/paper/2602.20664/) (2026)
- [Camera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation](https://mlsanity.com/paper/2604.09195/) (2026)
- [Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling](https://mlsanity.com/paper/2412.20725/) (2024)
- [MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications](https://mlsanity.com/paper/2502.18540/) (2025)
- [Cutscene Agent: An LLM Agent Framework for Automated 3D Cutscene Generation](https://mlsanity.com/paper/2604.25318/) (2026)
- [Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation](https://mlsanity.com/paper/2609.07720/) (2026)
- [LinkTo-Anime: A 2D Animation Optical Flow Dataset from 3D Model Rendering](https://mlsanity.com/paper/2506.02733/) (2025)
- [Bridging Your Imagination with Audio-Video Generation via a Unified Director](https://mlsanity.com/paper/2512.23222/) (2025)
- [COMIC: Agentic Sketch Comedy Generation](https://mlsanity.com/paper/2603.11048/) (2026)
- [Mind-of-Director: Multi-modal Agent-Driven Film Previsualization via Collaborative Decision-Making](https://mlsanity.com/paper/2603.14790/) (2026)
- [OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation](https://mlsanity.com/paper/2606.17536/) (2026)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation](https://mlsanity.com/paper/2604.16958/) (2026)
- [CutVerse: A Compositional GUI Agents Benchmark for Media Post-Production Editing](https://mlsanity.com/paper/2605.19484/) (2026)
- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)

## Abstract (arXiv)

Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial human effort and incurs high training costs. Due to limited prompting plans, these methods typically produce brief, information-poor, and context-incoherent animations. To overcome these limitations and automate the animation process, we pioneer the introduction of large multimodal models (LMMs) as the core processor to build an autonomous animation-making agent, named Anim-Director. This agent mainly harnesses the advanced understanding and reasoning capabilities of LMMs and generative AI tools to create animated videos from concise narratives or simple instructions. Specifically, it operates in three main stages: Firstly, the Anim-Director generates a coherent storyline from user inputs, followed by a detailed director's script that encompasses settings of character profiles and interior/exterior descriptions, and context-coherent scene descriptions that include appearing characters, interiors or exteriors, and scene events. Secondly, we employ LMMs with the image generation tool to produce visual images of settings and scenes. These images are designed to maintain visual consistency across different scenes using a visual-language prompting method that combines scene descriptions and images of the appearing character and setting. Thirdly, scene images serve as the foundation for producing animated videos, with LMMs generating prompts to guide this process. The whole process is notably autonomous without manual intervention, as the LMMs interact seamlessly with generative tools to generate prompts, evaluate visual quality, and select the best one to optimize the final output.