InteractML: Making machine learning accessible for creative practitioners working with movement interaction in immersive media
Clarice Hilton, Nicola Plant, Carlos González Díaz, Phoenix Perry, Ruth Gibson, Bruno Rodrigo Martelli, Michael Zbyszyński, Rebecca Fiebrink, Marco Fyfe Pietro Gillies · 2021
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.