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LLM-powered Text Entry in Virtual Reality

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Large language models (LLMs) have demonstrated exceptional performance across various language-related tasks, offering significant potential for enhancing text entry in Virtual Reality (VR). We introduce an LLM-powered text entry system for VR, which integrates multiple input modalities and utilizes a fine-tuned LLM as a keyboard decoder. The LLM-based decoder achieved 93.1% top-1 decoding accuracy on a word gesture typing dataset and 95.4% on tap typing, highlighting its potential for VR text entry applications. Our demonstration shows how LLMs can support tap typing and word-gesture typing through raycasting and joystick-based inputs, potentially accommodating various user preferences and enhancing the adaptability of VR text input methods.

Original languageEnglish
Title of host publicationProceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1628-1629
Number of pages2
ISBN (Electronic)9798331514846
DOIs
StatePublished - 2025
Event2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025 - Saint-Malo, France
Duration: Mar 8 2025Mar 12 2025

Publication series

NameProceedings - 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

Conference

Conference2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025
Country/TerritoryFrance
CitySaint-Malo
Period03/8/2503/12/25

Keywords

  • Human computer interaction (HCI)
  • Human-centered computing
  • Text entry
  • Virtual reality

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