About the Performing Arts x Tech Lab 2023/24

Deconstructing and mediating different perceptions of movement to generate sonic and visual expressions.

The inception of Theories of Motion can be traced back to the collaboration between multidisciplinary practitioners seeking to understand motion within performing arts. This collaborative effort involves a movement artist, an interactive designer and technologist, a sound designer, and a performance and technical director, utilising technology as a mediator to reconcile varying viewpoints. Considering the body as a central instrument, the project seeks to integrate projection, sound, and moving bodies, where refined physicality merges with digital and sonic realities. The primary objective was to explore how technology could seamlessly translate into or extend expressions in movement, or vice versa.

Archival of movement and choreography by creating a system that can learn, generate movement scores through AI algorithm and motion capture technology.

The inception of Dancing the Algorithm (DatA) can be traced back to a simple question: ‘How do we dance collectively?’ By blending computer-generated elements and individually created movement sequences, this project explores the intersection of computational logic and human expression. Its goal was to expand dance-making and performance tools through a focused exploration of possibilities, rather than fixating on a final product.

A digital experiment that reimagines interactive experience in theatre for young audiences (TYA).

Conceived for young audiences, DOTS 2.0 is a theatrical adaptation of the award-winning children’s book The Dot by Peter H. Reynolds. Covering themes of creative empowerment and resilience, the story follows the artistic journey of a young girl who believed that she could not draw–until her art teacher advised her to “just make a mark and see where it takes you.” (Reynolds, 2003). This non-verbal adaptation seeks to inspire children to find confidence in physical expression while bringing out their inherent creativity through the process of making art.

Gesture-responsive technology for dynamic multi-sensory performance experiences.

Verge 2.0 represents the latest iteration of an experimental project that has been evolving for over a decade, initially starting as a university project by creative technologist and project lead, James Lye. What began as a fascination with interactive sound art has matured through various stages of development, embracing multiple forms of presentation styles including interactive installations, multi-disciplinary performances, research initiatives, and now, in its current iteration, a performance-lecture.

The project's core aim has always been to explore the creative interplay between technology and human gestures. The collaboration with Dr. Amin Farid, a choreographer and dance researcher, since the early stages of this experiment in 2014, has enriched the project with invaluable insights, introducing a new dimension to the work.

Using AI to transform oral storytelling experiences to engage diverse audiences.

In The Sound of Stories, technology is perceived as an artistic ally, enhancing and safeguarding traditional oral storytelling practices to ensure its endurance in an ever-evolving world. Utilising deep learning and neural audio synthesis as creative tools, The Sound of Stories explores ways for storytelling to be delivered in numerous languages whilst maintaining the original voice and cadence of the human storyteller, offering possibilities for engaging multiple, diverse audiences in the same room, or for the storyteller’s unique delivery to be experienced even when they are not physically present.

perfor.ml ↗ (opens in new tab) ↗(opens in new tab) ↗ (opens in new tab)—where ‘ml’ refers to machine learning—is an ongoing project by Feelers, a research lab of artists and designers focused on the intersections of art and technology. perfor.ml ↗ (opens in new tab) ↗(opens in new tab) ↗ (opens in new tab) introduces a new performance methodology and investigates the possibility of integrating machine learning into performance, as if questioning, ‘on whose terms, and for what goals?'