Interactive Machine Learning for Movement Interaction in VR

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Lawrence, Tom; Zhang, Tianyuan; Hilton, Clarice and Gillies, Marco. 2025. 'Interactive Machine Learning for Movement Interaction in VR'. In: 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Saint-Malo, France 8 - 12 March 2025. [Conference or Workshop Item]

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Interactive_Machine_Learning_for_Movement_Interaction_in_VR__NIDIT_-4.pdf - Accepted Version
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Official URL: https://ieeexplore.ieee.org/document/10973007

Abstract or Description

Full body movement is a powerful way of interacting with virtual reality experiences. Not only does it reproduce real world interactions, it can also have positive effects on emotions. However, designing effective movement interaction can be hard, as our knowledge about how we move is tacit and embodied, meaning that we can move without knowing exactly how we make those movements. This makes it hard to explicitly program movement interaction. Interactive Machine Learning (IML) is an alternative approach in which movement interaction is designed and implementing by providing examples of movement. This paper presents IntearctML, a movement interaction design platform based on IML, as well as a case study of using it to create a VR experience called Dolittle VR.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.1109/VRW66409.2025.00081

Additional Information:

“© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

Funding: This research was partially supported by the UK Engineering and Physical Sciences Research Council grant “4i: Immersive Interaction Design for Indie Developers with Interactive Machine Learning” (EP/S02753X/1).

Keywords:

Virtual Reality, Movement Interaction, Interactive Machine Learning.

Departments, Centres and Research Units:

Computing

Dates: