“F O R M S”: Creating Visual Composition Through the Movement of Dance and Artificial Intelligence

Abstract

What relationship exists between dance and visual arts? How can dance visually express lines, shapes, and visual compositions in space? It is true that performing arts and visual arts have common methodologies and connections with each other. However, how can the audience understand their relationship? The present work intersects art with technology, more specifically dance movement, and machine learning techniques, to create a new visual representation of the body's movement in space. The field of artificial intelligence has allowed machine learning techniques, such as human-pose estimation to explore areas of body movement. The integration of machine learning with dance has resulted in different approaches, but how can this relationship contribute to involving the audience? FORMS mirrors this dancer-machine dialogue in an interactive installation performance. Body language is the vehicle that drives the visual outcome of the interactive experience, creating a novel real-time visual expression of the dance movement. The hybrid format of the installation offers the audience a live performance and an open experience where anyone can play with FORMS through their movement. It contributes to cultivating body awareness, understanding in major detail the dance movement, and enriching the art experience.



Author Information
Maria Rita Nogueira, University of Coimbra, Portugal
Paulo Menezes, University of Coimbra, Portugal
José Maçãs de Carvalho, University of Coimbra, Portugal

Paper Information
Conference: PCAH2022
Stream: Arts - Media Arts Practices: Television

This paper is part of the PCAH2022 Conference Proceedings (View)
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To cite this article:
Nogueira M., Menezes P., & Carvalho J. (2022) “F O R M S”: Creating Visual Composition Through the Movement of Dance and Artificial Intelligence ISSN: 2758-0970 The Paris Conference on Arts & Humanities 2022 Official Conference Proceedings https://doi.org/10.22492/issn.2758-0970.2022.8
To link to this article: https://doi.org/10.22492/issn.2758-0970.2022.8


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