Locomotion vault: the extra mile in analyzing VR locomotion techniques

Max Di Luca, Hasti Seifi, Simon Egan, Mar Gonzalez-Franco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Numerous techniques have been proposed for locomotion in virtual reality (VR). Several taxonomies consider a large number of attributes (e.g., hardware, accessibility) to characterize these techniques. However, finding the appropriate locomotion technique (LT) and identifying gaps for future designs in the high-dimensional space of attributes can be quite challenging. To aid analysis and innovation, we devised Locomotion Vault (https://locomotionvault.github.io/), a database and visualization of over 100 LTs from academia and industry. We propose similarity between LTs as a metric to aid navigation and visualization. We show that similarity based on attribute values correlates with expert similarity assessments (a method that does not scale). Our analysis also highlights an inherent trade-off between simulation sickness and accessibility across LTs. As such, Locomotion Vault shows to be a tool that unifies information on LTs and enables their standardization and large-scale comparison to help understand the space of possibilities in VR locomotion.
Original languageEnglish
Title of host publicationCHI '21
Subtitle of host publicationProceedings of the 2021 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Print)9781450380966
DOIs
Publication statusPublished - 6 May 2021
Event2021 ACM CHI Virtual Conference on Human Factors in Computing Systems - Virtual
Duration: 8 May 202113 May 2021
https://chi2021.acm.org/

Publication series

NameCHI: Conference on Human Factors in Computing Systems

Conference

Conference2021 ACM CHI Virtual Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2021
Period8/05/2113/05/21
Internet address

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