Dance carries knowledge about how people live — information about the dancer’s landscapes, communities and histories. However, AI systems that analyze human movement are trained almost entirely on everyday actions like walking, sports and gestures, recorded in Western labs with no information about the geographic or cultural origins of the movement.
An interdisciplinary project at The University of Texas at Austin, co-led by Dance Assistant Professor Sinclair Ogaga Emoghene and Engineering Assistant Professor Lillian Chin, will create an AI-driven large movement language model for dance. The team has been awarded a grant of $643,719 from the Humanities and AI Virtual Institute (HAVI) at the nonprofit Schmidt Sciences to support this research endeavor.
