LLMR: Real-time Prompting of Interactive Worlds using Large Language Models
September 21, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores Fernandez, Jaron Lanier
arXiv ID
2309.12276
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CL,
cs.ET
Citations
95
Venue
International Conference on Human Factors in Computing Systems
Last Checked
3 months ago
Abstract
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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