LLM-powered Text Entry in Virtual Reality
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.
Authors
- Tony W Li (ORCID: https://orcid.org/0000-0001-7552-6689)
- Xiaojun Bi (ORCID: https://orcid.org/0000-0002-9716-7709)
- Yan Ma (ORCID: https://orcid.org/0000-0001-8264-3103)
- Zhi Li (ORCID: https://orcid.org/0000-0001-8298-2279)
Institutions
- Stony Brook University (US)
Publication Details
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1109/vrw66409.2025.00459
- Primary Topic
- Natural Language Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00