InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media

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Hilton, Clarice; Plant, Nicola; Fiebrink, Rebecca; Perry, Phoenix; González Díaz, Carlos; Gibson, Ruth; Martelli, Bruno; Zbyszynski, Michael and Gillies, Marco. 2021. 'InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media'. In: VRST ’21: ACM Symposium on Virtual Reality Software and Technology. Osaka, Japan 8-10 December 2021. [Conference or Workshop Item] (In Press)

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Official URL: https://dl.acm.org/conference/vrst

Abstract or Description

Interactive Machine Learning offers a method for designing movement interaction that supports creators in implementing even complex movement designs in their immersive applications by simply performing them with their bodies. We introduce a new tool, InteractML, and an accompanying ideation method, which makes movement interaction design faster, adaptable and accessible to creators of varying experience and backgrounds, such as artists, dancers and independent game developers. The tool is specifically tailored to non-experts as creators configure and train machine learning models via a node-based graph and VR interface, requiring minimal programming. We aim to democratise machine learning for movement interaction to be used in the development of a range of creative and immersive applications.

Item Type:

Conference or Workshop Item (Paper)

Keywords:

machine learning, movement interaction, creative virtual reality, artists, dancers

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