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Real-time Animation Generation and Control on Rigged Models via Large Language Models

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arxiv 2310.17838 v2 pith:72NIPEGZ submitted 2023-10-27 cs.GR cs.AI

Han Huang , Fernanda De La Torre , Cathy Mengying Fang , Andrzej Banburski-Fahey , Judith Amores , Jaron Lanier This is my paper · ORCID
classification cs.GRcs.AI
keywords modelslanguageriggedanimationanimationscontrolgenerationlarge
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We introduce a novel method for real-time animation control and generation on rigged models using natural language input. First, we embed a large language model (LLM) in Unity to output structured texts that can be parsed into diverse and realistic animations. Second, we illustrate LLM's potential to enable flexible state transition between existing animations. We showcase the robustness of our approach through qualitative results on various rigged models and motions.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models

    cs.MM 2025-02 conditional novelty 5.0 of 10

    LLMER uses LLM-generated JSON data instead of code to create interactive XR worlds, cutting token use and task completion time in a small user study.

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