Community Generated VR Painting using Eye Gaze

Mu, Mu; Dohan, Murtada

doi:10.1145/3474085.3478552

Cited by 5 publications

(3 citation statements)

References 3 publications

(1 reference statement)

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“…The artist aims to investigate how participants split their attention among these brushstrokes. In previous work, we studied user attention modelling and eye gaze-based community generative art [8,33]. In this paper, we focus on user interactions related to walking and navigation.…”

Section: Virtual Environmentmentioning

confidence: 99%

Real-walk modelling: deep learning model for user mobility in virtual reality

Dohan,

Mu,

Ajit

et al. 2024

Multimedia Systems

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This paper presents a study on modelling user free walk mobility in virtual reality (VR) art exhibition. The main objective is to investigate and model users’ mobility sequences during interactions with artwork in VR. We employ a range of machine learning (ML) techniques to define scenes of interest in VR, capturing user mobility patterns. Our approach utilises a long short-term memory (LSTM) model to effectively model and predict users’ future movements in VR environments, particularly in scenarios where clear walking paths and directions are not provided to participants. The DL model demonstrates high accuracy in predicting user movements, enabling a better understanding of audience interactions with the artwork. It opens avenues for developing new VR applications, such as community-based navigation, virtual art guides, and enhanced virtual audience engagement. The results highlight the potential for improved user engagement and effective navigation within virtual environments.

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Section: Virtual Environmentmentioning

confidence: 99%

Real-walk modelling: deep learning model for user mobility in virtual reality

Dohan,

Mu,

Ajit

et al. 2024

Multimedia Systems

Self Cite

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“…Similarly, we have not found any immersive VR applications for museums that actively use the gaze to interact with virtual objects. The gaze is mainly exploited to simply "align" the simulation, or, more rarely, for implicit interaction (e.g., for the creation of virtual artwork using the visitors' gaze paths [68]).…”

Section: General Considerationsmentioning

confidence: 99%

Gaze-Based Human–Computer Interaction for Museums and Exhibitions: Technologies, Applications and Future Perspectives

Dondi

Porta

2023

Electronics

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Eye tracking technology is now mature enough to be exploited in various areas of human–computer interaction. In this paper, we consider the use of gaze-based communication in museums and exhibitions, to make the visitor experience more engaging and attractive. While immersive and interactive technologies are now relatively widespread in museums, the use of gaze interaction is still in its infancy—despite the benefits it could provide, for example, to visitors with motor disabilities. Apart from some pioneering early works, only the last few years have seen an increase in gaze-based museum applications. This literature review aims to discuss the state of the art on this topic, highlighting advantages, limitations and current and future trends.

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“…The artist aims to investigate how participants split their attention among these brushstrokes. In previous work, we studied user attention modelling and eye-gaze based community generative art [7,32]. In this paper, we are focusing on user interactions related to walking and navigation.…”

Section: Virtual Environmentmentioning

confidence: 99%

Real-Walk Modelling: Deep Learning Model for User Mobility in Virtual Reality

Dohan

Ajit

et al. 2022

Preprint

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Understanding human interactions in virtual reality (VR) can help developing intelligent applications that adapt to users' needs and enhance the user experience. The significant development of VR content has expanded the impact on VR complexity, making understanding VR spatial characteristics more difficult. While user mobility is a crucial part of their interactions with the VR environment, the current literature still does not provide a suitable framework to interpret and model VR user mobility data. We conducted a user experiment in the context of an abstract VR painting exhibition where users are prompted to walk naturally in a physical area to explore the VR painting. Deep Learning models are used to model user mobility sequences and predict their future movements while engaging with the art exhibition. Our user mobility model can support the development of new VR applications for the improved user navigation and social experience in VR.

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Community Generated VR Painting using Eye Gaze

Cited by 5 publications

References 3 publications

Real-walk modelling: deep learning model for user mobility in virtual reality

Real-walk modelling: deep learning model for user mobility in virtual reality

Gaze-Based Human–Computer Interaction for Museums and Exhibitions: Technologies, Applications and Future Perspectives

Real-Walk Modelling: Deep Learning Model for User Mobility in Virtual Reality

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