Deep-learning based recommenders for the improved user navigation in VR

Murtada Dohan; Mu Mu; Suraj Ajit; Tawfiq A. Al-Assadi; Gary Hill; Andreas Mauthe

Deep-learning based recommenders for the improved user navigation in VR

Murtada Dohan^*, Mu Mu^*, Suraj Ajit, Tawfiq A. Al-Assadi, Gary Hill, Andreas Mauthe

^*Corresponding author for this work

Research output: Contribution to Book/Report › Conference Contribution › peer-review

Abstract

Virtual reality (VR) has become a popular choice for education, industrial simulation, entertainment and healthcare applications. User navigation is an essential propriety of virtual applications. However, novice audiences often face the difficulty of engaging with the virtual surrounding environment. This work presents a novel design of a deep learning-based navigation solution to improve the quality of user experience and the engagement with virtual content. We compare two navigation methods avatar-based and arrows-based guidance, both of which are driven by a recurrent neural network (RNN) model. We capture participants’ mobility and eye-gaze to compare the impact of different navigation affects on users’ engagement in VR applications.

Original language	English
Title of host publication	ACM International Conference on Interactive Media Experiences (ACM IMX 2022)
Publisher	Association for Computing Machinery (ACM)
Publication status	Accepted/In press - 21 Apr 2022
Event	ACM IMX 2022:: ACM International Conference on Interactive Media Experiences - Universidade de Aveiro, Aveiro, Portugal Duration: 22 Jun 2022 → 24 Jun 2022

Conference

Conference	ACM IMX 2022:
Country/Territory	Portugal
City	Aveiro
Period	22/06/22 → 24/06/22

Access to Document

Dohan_etal_ACM_2022_Deep-learning_based_recommenders_for_the_improved_user_navigation_in_VRAccepted author manuscript, 4.17 MBLicence: CC BY

Keynote: Powering up VR applications with eye gaze and mobility tracking
Mu Mu (Author)
6 Sept 2022
Activity: Academic Talks or Presentations › Keynote › Research

Cite this

@inproceedings{7618f55d28e44bbc962168db908bdbdc,

title = "Deep-learning based recommenders for the improved user navigation in VR",

abstract = "Virtual reality (VR) has become a popular choice for education, industrial simulation, entertainment and healthcare applications. User navigation is an essential propriety of virtual applications. However, novice audiences often face the difficulty of engaging with the virtual surrounding environment. This work presents a novel design of a deep learning-based navigation solution to improve the quality of user experience and the engagement with virtual content. We compare two navigation methods avatar-based and arrows-based guidance, both of which are driven by a recurrent neural network (RNN) model. We capture participants{\textquoteright} mobility and eye-gaze to compare the impact of different navigation affects on users{\textquoteright} engagement in VR applications.",

author = "Murtada Dohan and Mu Mu and Suraj Ajit and {A. Al-Assadi}, Tawfiq and Gary Hill and Andreas Mauthe",

year = "2022",

month = apr,

day = "21",

language = "English",

booktitle = "ACM International Conference on Interactive Media Experiences (ACM IMX 2022)",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = " ACM IMX 2022: : ACM International Conference on Interactive Media Experiences ; Conference date: 22-06-2022 Through 24-06-2022",

}

TY - GEN

T1 - Deep-learning based recommenders for the improved user navigation in VR

AU - Dohan, Murtada

AU - Mu, Mu

AU - Ajit, Suraj

AU - A. Al-Assadi, Tawfiq

AU - Hill, Gary

AU - Mauthe, Andreas

PY - 2022/4/21

Y1 - 2022/4/21

N2 - Virtual reality (VR) has become a popular choice for education, industrial simulation, entertainment and healthcare applications. User navigation is an essential propriety of virtual applications. However, novice audiences often face the difficulty of engaging with the virtual surrounding environment. This work presents a novel design of a deep learning-based navigation solution to improve the quality of user experience and the engagement with virtual content. We compare two navigation methods avatar-based and arrows-based guidance, both of which are driven by a recurrent neural network (RNN) model. We capture participants’ mobility and eye-gaze to compare the impact of different navigation affects on users’ engagement in VR applications.

AB - Virtual reality (VR) has become a popular choice for education, industrial simulation, entertainment and healthcare applications. User navigation is an essential propriety of virtual applications. However, novice audiences often face the difficulty of engaging with the virtual surrounding environment. This work presents a novel design of a deep learning-based navigation solution to improve the quality of user experience and the engagement with virtual content. We compare two navigation methods avatar-based and arrows-based guidance, both of which are driven by a recurrent neural network (RNN) model. We capture participants’ mobility and eye-gaze to compare the impact of different navigation affects on users’ engagement in VR applications.

M3 - Conference Contribution

BT - ACM International Conference on Interactive Media Experiences (ACM IMX 2022)

PB - Association for Computing Machinery (ACM)

T2 - ACM IMX 2022:

Y2 - 22 June 2022 through 24 June 2022

ER -

Deep-learning based recommenders for the improved user navigation in VR

Abstract

Conference

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Activities

Keynote: Powering up VR applications with eye gaze and mobility tracking

Cite this