Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR

要旨

Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of visual feedback) impact user performance. To address these gaps, this paper examines the current landscape of XR TETs by creating a database of 176 different techniques. We analyze this database to highlight trends in the design of these techniques, the metrics used to evaluate them, and how various interaction attributes impact these metrics. We discuss implications for future techniques and present TEXT: Text Entry for XR Trove, an interactive online tool to navigate our database.

著者
Arpit Bhatia
University of Copenhagen, Copenhagen, Denmark
Moaaz Hudhud Mughrabi
Arizona State University, Tempe, Arizona, United States
Diar Abdlkarim
The University of Birmingham, University of Birmingham, West Midlands, United Kingdom
Massimiliano Di Luca
University of Birmingham, Birmingham, United Kingdom
Mar Gonzalez-Franco
Google, Seattle, Washington, United States
Karan Ahuja
Google, Seattle, Washington, United States
Hasti Seifi
Arizona State University, Tempe, Arizona, United States
DOI

10.1145/3706598.3713382

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713382

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: XR

G302
7 件の発表
2025-04-29 18:00:00
2025-04-29 19:30:00
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