This project is a collaboration with artist Eugenia Kim to explore creating a “body” for dance and movement data that has been collected via methods such as mocap systems and processed with machine learning algorithms. In existing movement visualisation works, the human form is still emphasized even when heavily abstracted, and the data remains in a digital and/or virtual realm. In response to this we explore the application of laser projections of motion trails to bring data into physical reality, thus metaphorically giving a “body” to generated movement. Somatic movement improvisations (i.e. Contact Improvisation and the Skinner Releasing Technique) will be used for training to teach the attributes of human movement rather than the vocabulary of a set dance technique.


  • Kim, E.S., Sandor, C., Haebich, J. and Cassinelli, A., 2021, June. Playing with Soma: Speculating on the Physical Body and Somatic Practice of AI. In Art Machines 2: International Symposium on Machine Learning and Art 2021 (AM2) (pp. 31-38). School of Creative Media, City University of Hong Kong. PDF


Jayson Haebich Eugenia Kim

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