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LLMR: Real-time Prompting of Interactive Worlds using Large Language Models

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arxiv 2309.12276 v3 pith:RMRJDOKE submitted 2023-09-21 cs.HC cs.AIcs.CLcs.ET

classification cs.HCcs.AIcs.CLcs.ET
keywords llmrcreationdiverseexperiencesframeworkinteractivelanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is scarce, or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. Our framework relies on text interaction and the Unity game engine. By incorporating techniques for scene understanding, task planning, self-debugging, and memory management, LLMR outperforms the standard GPT-4 by 4x in average error rate. We demonstrate LLMR's cross-platform interoperability with several example worlds, and evaluate it on a variety of creation and modification tasks to show that it can produce and edit diverse objects, tools, and scenes. Finally, we conducted a usability study (N=11) with a diverse set that revealed participants had positive experiences with the system and would use it again.

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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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