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

Yunxin Li, Haoyuan Shi, Baotian Hu, Longyue Wang, Jiashun Zhu, Jinyi Xu, Zhen Zhao, Min Zhang

arXiv:2408.09787v1cs.CLcs.CVcs.MM

TL;DR

Traditional animation pipelines require human effort and labeled-data training, and short prompts can produce brief, incoherent videos. Anim-Director uses an LMM agent to develop stories and coordinate image and video tools; the authors report longer animations with enhanced storylines and superior background consistency than baselines.

  • Problem

    Traditional animation methods rely on human effort and labeled-data training, while brief prompting plans often produce short, context-incoherent videos.

    Source: §1 · p. 2

  • Method

    Anim-Director uses an LMM agent to expand narratives, generate structured scripts, and coordinate image and video tools with visual evaluation and candidate selection.

    Source: §1 · pp. 2–3

  • Results

    Anim-Director generates long animation videos with enhanced storylines and superior background consistency than baselines.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Its automated integration with external tools streamlines animation production and minimizes routine human oversight.

    Source: §1 · p. 3

    The generative tools are not fully controllable, and scene transitions may be choppy, risking inappropriate, visually unappealing, or contextually inconsistent content.

    Source: §5 · p. 8

Abstract

from arXiv · show

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.

1 INTRODUCTION

Anim-Director replaces labor-intensive, training-dependent animation pipelines with an autonomous LMM director that coordinates story, script, image, and video generation. It reports long animations with enhanced storylines and background consistency compared with baselines.

  • Earlier approaches rely on human effort and labeled-data training, while short prompting plans often yield brief, context-incoherent animations.Source: §1 · p. 2
  • It expands brief narratives into detailed stories and scripts describing characters, settings, and coherent scenes.Source: §1 · p. 2
  • Scene images are generated from descriptions and visual references, then checked for scene fit and character consistency, with segmentation and replacement used for refinement.Source: §1 · pp. 2–3
  • Video prompts and generation parameters are tailored to scene actions, while video quality checks and candidate selection prioritize narrative alignment.Source: §1 · p. 3
  • Anim-Director generates long animation videos with enhanced storylines and superior background consistency than baselines.Source: §1 · p. 3
  • The agent autonomously directs the animation process, using LMM reasoning to refine, evaluate, and select generated content while coordinating external tools.Source: §1 · p. 3

2 RELATED WORK

Related work spans text-to-animation generation, multimodal language models, and tool-using agents. The paper situates Anim-Director against dataset-trained story-visualization methods and the expanding capabilities of multimodal models.

2.1 Text-to-Animation Generation

  • Text-to-video diffusion and story-visualization systems advance animation, but dataset-trained approaches remain limited in controllability and generalization to new narratives.Source: §2.1 · p. 3

2.2 Large Multimodal Models

  • Multimodal LLMs combine visual backbones, language models, and cross-modal connectors; Figure 2 situates GPT-4 in a six-step generative-tool workflow.Source: §2.2 · p. 4, Figure 2 · p. 4

2.3 Large Models Powered Agents

  • Multimodal agents combine models with external tools across tasks, illustrating their potential to support human creative processes.Source: §2.3 · p. 4

3 METHODOLOGY

Anim-Director organizes generation into six tool-mediated steps, from refining a narrative and scripting scenes to producing and selecting videos. GPT-4 guides image and video creation through prompts, evaluation, and candidate selection.

3.1 Overview

  • The six-step workflow refines the narrative, creates director-like scripts, generates and improves scene images, then produces videos and selects candidates.Source: §3.1 · p. 5

3.2 Story Refinement and Script Generation

  • GPT-4 expands short stories with plot details, then extracts characters and settings, writes context-coherent scene descriptions, and verifies them against the story.Source: §3.2 · p. 5

3.3 Scene Image Generation and Improvement

  • Midjourney builds prior character and setting images, then scene prompts combine text and images; GPT-4 checks fit and character consistency, with segmentation-supported replacement when needed.Source: §3.3 · pp. 5–6

3.4 Video Production and Quality Enhancement

  • GPT-4 creates action-and-emotion prompts and predicts video parameters; ten candidates are metric-screened to three, then GPT-4 selects one before scene videos are spliced.Source: §3.4 · p. 6

4 EXPERIMENTS

Experiments used 100 concise TinyStories narratives and evaluated image and video generation with similarity, coherence, and video-quality measures. Anim-Director ranked highest in reported image and video quality comparisons, while qualitative cases emphasized visual coherence and consistency.

4.1 Settings

  • The evaluation uses 100 concise TinyStories narratives and compares Anim-Director with Custom Diffusion, DPT-T2I, and advanced image- and video-generation models.Source: §4.1 · p. 6
  • Image evaluation measures text-image alignment and image consistency, while video evaluation covers temporal consistency, frame-wise quality, and text-video alignment.The image measures include CLIP-space text-image similarity and image-image similarity for character consistency.Source: §4.1 · pp. 6–7

4.2 Quantitative Comparisons

  • Anim-Director achieves the highest reported performance in image and video quality generation across Tables 3 and 4.Table 3 covers contextual coherence and image-image and text-image similarity; Table 4 covers video-quality measures.Source: Table 3 · p. 7, Table 4 · p. 7, §4.2 · p. 7
  • Anim-Director generates 3-second videos per prompt versus VideoCrafter2’s 1 second, while maintaining subject and background consistency.The authors also report that their video-enhancement technique elevates overall video quality.Source: Table 4 · p. 7, §4.2 · p. 8
  • The last two rows of the ablation experiments provide additional evidence that the image-quality improvement technique is effective.Source: §4.2 · p. 8

4.3 Qualitative Comparisons

  • Qualitative comparisons report stronger visual coherence and quality, especially for human faces, and greater consistency of humans, objects, and backgrounds than baselines.The examples include all 10 scenes for Anim-Director, including transition scenes 6 and 9, while the displayed DPT-T2I and Custom Diffusion examples omit those scenes.Source: Figure 3 · p. 7, §4.3 · p. 8

5 LIMITATIONS

The system’s generative tools are not fully controllable, and scene transitions can be choppy; outputs may therefore be inappropriate, visually unappealing, or contextually inconsistent.

5 LIMITATIONS

  • Generative processes are not fully controllable, and scene transitions may be choppy.These conditions can result in inappropriate, visually unappealing, or contextually inconsistent content.Source: §5 · p. 8
  • The system uses GPT-4 with generative tools such as Midjourney and Pika.Source: §5 · p. 8
  • The authors aim to improve visual quality and contextual coherence, particularly across extended video sequences.Source: §5 · p. 8

6 CONCLUSION

Anim-Director uses an LMM as an autonomous director to create detailed animated videos from concise narratives or simple instructions. The authors present it as a flexible, training-free alternative to pipelines requiring human-labeled data and substantial human effort.

6 CONCLUSION

  • Anim-Director uses an LMM as an autonomous director to orchestrate the animation-making process.The system deeply interacts with external generative tools to enhance generated images and videos.Source: §6 · p. 8
  • Unlike prior methods that train generative models on human-labeled data, Anim-Director is flexible and training-free.The conclusion describes prior pipelines as requiring substantial human effort and high training costs.Source: §6 · p. 8
  • The authors describe the system as instructive and promising, with potential real-life applications.Source: §6 · p. 8
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