mlsanityData to 7 Oct 2026

AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production

arXiv 2403.07952 ↗First posted 12 Mar 2024cs.CV cs.AI cs.MMcited by 12

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

The Agent and AIGC (Artificial Intelligence Generated Content) technologies have recently made significant progress. We propose AesopAgent, an Agent-driven Evolutionary System on Story-to-Video Production. AesopAgent is a practical application of agent technology for multimodal content generation. The system integrates multiple generative capabilities within a unified framework, so that individual users can leverage these modules easily. This innovative system would convert user story proposals into scripts, images, and audio, and then integrate these multimodal contents into videos. Additionally, the animating units (e.g., Gen-2 and Sora) could make the videos more infectious. The AesopAgent system could orchestrate task workflow for video generation, ensuring that the generated video is both rich in content and coherent. This system mainly contains two layers, i.e., the Horizontal Layer and the Utility Layer. In the Horizontal Layer, we introduce a novel RAG-based evolutionary system that optimizes the whole video generation workflow and the steps within the workflow. It continuously evolves and iteratively optimizes workflow by accumulating expert experience and professional knowledge, including optimizing the LLM prompts and utilities usage. The Utility Layer provides multiple utilities, leading to consistent image generation that is visually coherent in terms of composition, characters, and style. Meanwhile, it provides audio and special effects, integrating them into expressive and logically arranged videos. Overall, our AesopAgent achieves state-of-the-art performance compared with many previous works in visual storytelling. Our AesopAgent is designed for convenient service for individual users, which is available on the following page: https://aesopai.github.io/.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 57 of its 67 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

57 of 67 references.

References · 57

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2023GPT-4 Technical Reportcited by 18,132
  8. 2023Visual Instruction Tuningcited by 7,792
  9. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  10. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  11. 2017Neural Discrete Representation Learningcited by 4,650
  12. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
38 more
  1. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  2. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  4. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  5. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  6. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  7. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
  8. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  9. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  10. 2016Generative Adversarial Text to Image Synthesiscited by 735
  11. 2023ModelScope Text-to-Video Technical Reportcited by 651
  12. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  13. 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
  14. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
  15. 2023MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Actioncited by 511
  16. 2023Simple and Controllable Music Generationcited by 480
  17. 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 475
  18. 2023MusicLM: Generating Music From Textcited by 442
  19. 2016Generating Videos with Scene Dynamicscited by 376
  20. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  21. 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
  22. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  23. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  24. 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 250
  25. 2018Music Transformercited by 207
  26. 2023Recommender Systems with Generative Retrievalcited by 191
  27. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  28. 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generationcited by 170
  29. 2017Video Generation From Textcited by 153
  30. 2018StoryGAN: A Sequential Conditional GAN for Story Visualizationcited by 137
  31. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 136
  32. 2023NExT-Chat: An LMM for Chat, Detection and Segmentationcited by 79
  33. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  34. 2024Vlogger: Make Your Dream A Vlogcited by 70
  35. 2023RET-LLM: Towards a General Read-Write Memory for Large Language Modelscited by 64
  36. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  37. 2024VideoStudio: Generating Consistent-Content and Multi-Scene Videoscited by 62
  38. 2020XGPT: Cross-modal Generative Pre-Training for Image Captioningcited by 51

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 4.

12 papers.

Similar, not cited

Close in wording, no citation either way

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  2. 2026VideoAgent: All-in-One Framework for Video Understanding and Editing
  3. 2026VideoWeaver: Evaluating and Evolving Skills for Agentic Long Video Generation
  4. 2026VideoGen-Agent: Reinforcing Video Generation Agents
  5. 2025Automated Movie Generation via Multi-Agent CoT Planning
  6. 2024From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systems
  7. 2026Camera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation
  8. 2025Communicative Agents for Slideshow Storytelling Video Generation based on LLMs