mlsanityData to 14 Oct 2026

Story3D-Agent: Exploring 3D Storytelling Visualization with Large Language Models

arXiv 2408.11801 ↗First posted 21 Aug 2024cs.CVcited by 9

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

Abstract (arXiv)

Traditional visual storytelling is complex, requiring specialized knowledge and substantial resources, yet often constrained by human creativity and creation precision. While Large Language Models (LLMs) enhance visual storytelling, current approaches often limit themselves to 2D visuals or oversimplify stories through motion synthesis and behavioral simulation, failing to create comprehensive, multi-dimensional narratives. To this end, we present Story3D-Agent, a pioneering approach that leverages the capabilities of LLMs to transform provided narratives into 3D-rendered visualizations. By integrating procedural modeling, our approach enables precise control over multi-character actions and motions, as well as diverse decorative elements, ensuring the long-range and dynamic 3D representation. Furthermore, our method supports narrative extension through logical reasoning, ensuring that generated content remains consistent with existing conditions. We have thoroughly evaluated our Story3D-Agent to validate its effectiveness, offering a basic framework to advance 3D story representation.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 33 of its 51 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

33 of 51 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects14 Feb

Other references · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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20 more
  1. 2022Visual Programming: Compositional visual reasoning without trainingcited by 516
  2. 2023Human Motion Diffusion as a Generative Priorcited by 357
  3. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 289
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  6. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 227
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  9. 2023VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planningcited by 141
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  11. 2024GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splattingcited by 109
  12. 2023HumanTOMATO: Text-aligned Whole-body Motion Generationcited by 104
  13. 2023Animate-A-Story: Storytelling with Retrieval-Augmented Video Generationcited by 90
  14. 2023Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Followingcited by 77
  15. 2023MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perceptioncited by 67
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Built on by

Cited by year (2026 to 14 Oct)

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

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization
  2. 2025VisAgent: Narrative-Preserving Story Visualization Framework
  3. 2025StoryWriter: A Multi-Agent Framework for Long Story Generation
  4. 2025Audit & Repair: An Agentic Framework for Consistent Story Visualization in Text-to-Image Diffusion Models
  5. 2024DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts
  6. 2025LL3M: Large Language 3D Modelers
  7. 2025ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling
  8. 2026From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives