Playing with Soma : Speculating on the Physical Body and Somatic Practice of AI
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Art Machines 2 |
| Subtitle of host publication | International Symposium on Machine Learning and Art 2021 Proceedings |
| Editors | Richard William Allen |
| Place of Publication | Hong Kong |
| Publisher | School of Creative Media, City University of Hong Kong |
| Pages | 31-38 |
| Number of pages | 8 |
| ISBN (Print) | 978-962-442-448-5, 9789624424485 |
| Publication status | Published - Jun 2021 |
Conference
| Title | Art Machines 2: International Symposium on Machine Learning and Art 2021 (AM2) |
|---|---|
| Location | City University of Hong Kong |
| Place | China |
| City | Hong Kong |
| Period | 10 - 14 June 2021 |
Link(s)
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(dac0ce28-d415-4dbf-a709-e68fe6c44393).html |
|---|
Abstract
Current trends in new media art and dance technology have given rise to artworks driven by motion capture (mocap) data and machinelearning algorithms that take the form of immersive media, live performance and projection-based installations. In these 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 these trends, the authors 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.
Citation Format(s)
Playing with Soma : Speculating on the Physical Body and Somatic Practice of AI. / Kim, Eugenia S.; Sandor, Christian; Haebich, Jayson et al.
Art Machines 2: International Symposium on Machine Learning and Art 2021 Proceedings. ed. / Richard William Allen. Hong Kong : School of Creative Media, City University of Hong Kong, 2021. p. 31-38.Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
