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A Model for Assessing and Sorting Virtual Locomotion Techniques According to Their Fidelity to Real Walking

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Virtual Reality and Mixed Reality (EuroXR 2023)

Abstract

Travel is a challenging task in virtual reality applications due to space constraints of the setup or limited range of the tracking systems. This prevents the user from moving away from the installation and requires developers to implement smart solutions that overcome these limitations. Some of these solutions, known as virtual locomotion techniques, try to mimic real walking movements, while others introduce some kind of magic. We developed a model that is meant to classify and sort these techniques as regards to their fidelity to real walking. This can be useful both for research, to find opportunities for new techniques, and for development, to find the technique that best fits a target application. Whereas previous works typically divide techniques into real and virtual classes, we propose a 12-point scale where all techniques fit into and therefore makes easier to compare and select techniques. Besides, we perform a retrospective analysis of a previous travel technique evaluation using this tool. Comparing the results of the experiment with the proposed fidelity scale allows us to both evaluate the proposal and find ways of improving it in future revisions.

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Acknowledgements

Grants PID2020-115220RB-C21, funded by MCIN/AEI/https://doi.org/10.13039/501100011033, and 2022-GRIN-34436, funded by UCLM, both also funded by “ERDF: A way to make Europe”.

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Correspondence to José P. Molina .

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Molina, J.P., González, P., García, A.S., González, J.J. (2023). A Model for Assessing and Sorting Virtual Locomotion Techniques According to Their Fidelity to Real Walking. In: Zachmann, G., et al. Virtual Reality and Mixed Reality. EuroXR 2023. Lecture Notes in Computer Science, vol 14410. Springer, Cham. https://doi.org/10.1007/978-3-031-48495-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-48495-7_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-48495-7

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