Dancers in an Encypher cypher during a public live performance. Each dancer's zone is lit from above, with light intensity driven by that dancer's movement energy; the visualization screen has been replaced by light on the dancers themselves.
Anonymous submission · CHI 2027
Dancers in an Encypher cypher during a public live performance. Each dancer's zone is lit from above, with light intensity driven by that dancer's movement energy; the visualization screen has been replaced by light on the dancers themselves.
Music and dance are social practices of expression and connection, yet most HCI work in human-AI co-creation centers the solo performer. As generative music matures, we ask not only what AI can compose but what social encounters it can organize around sound. We present Encypher, a collaborative generative music system that translates collective movement qualities into text prompts conditioning real-time music generation for dance cyphers. Through five weeks of co-design with local dancers, a user study with unacquainted participants, a public museum event, and a live performance, we found that users developed shared agency, perceiving the music as a response to the room's energy. While newcomers felt uncertain, the system fostered social presence by prompting them to look to each other for cues. By treating sociality as a design concern rather than a downstream effect, we offer a framework and design implications for AI systems for collaborative, embodied expression.
Six dancers performed with Encypher in front of an audience of approximately 300, with the screen removed and each dancer's movement energy driving the stage light on their zone. Select a moment to jump to it; section numbers refer to the paper.
Select a moment to jump to that point in the performance. The description here updates as the video plays.
Edited excerpt: segments where the stage lighting or audio dropped out have been removed. Blurred throughout for anonymity.