Locomotion vault: the extra mile in analyzing VR locomotion techniques

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

Standard

Locomotion vault : the extra mile in analyzing VR locomotion techniques. / Di Luca, Max; Seifi, Hasti; Egan, Simon; Gonzalez-Franco, Mar.

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), 2021.

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

Harvard

Di Luca, M, Seifi, H, Egan, S & Gonzalez-Franco, M 2021, Locomotion vault: the extra mile in analyzing VR locomotion techniques. in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM).

APA

Di Luca, M., Seifi, H., Egan, S., & Gonzalez-Franco, M. (Accepted/In press). Locomotion vault: the extra mile in analyzing VR locomotion techniques. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems Association for Computing Machinery (ACM).

Vancouver

Di Luca M, Seifi H, Egan S, Gonzalez-Franco M. Locomotion vault: the extra mile in analyzing VR locomotion techniques. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM). 2021

Author

Di Luca, Max ; Seifi, Hasti ; Egan, Simon ; Gonzalez-Franco, Mar. / Locomotion vault : the extra mile in analyzing VR locomotion techniques. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), 2021.

Bibtex

@inproceedings{2944dc2adb044b7f84a17ce97f0c3175,
title = "Locomotion vault: the extra mile in analyzing VR locomotion techniques",
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.",
author = "{Di Luca}, Max and Hasti Seifi and Simon Egan and Mar Gonzalez-Franco",
year = "2021",
month = jan,
day = "20",
language = "English",
booktitle = "Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Locomotion vault

T2 - the extra mile in analyzing VR locomotion techniques

AU - Di Luca, Max

AU - Seifi, Hasti

AU - Egan, Simon

AU - Gonzalez-Franco, Mar

PY - 2021/1/20

Y1 - 2021/1/20

N2 - 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.

AB - 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.

UR - https://locomotionvault.github.io/

UR - https://www.microsoft.com/en-us/research/publication/locomotion-vault-the-extra-mile-in-analyzing-vr-locomotion-techniques/

UR - https://youtu.be/AHZoFpZ_eRQ

UR - https://sigchi.org/conferences/conference-proceedings/

M3 - Conference contribution

BT - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery (ACM)

ER -