Gesture Studies

Hand tracking and gesture recognition for musical performance. As the performer shapes gestures with their hands, each shape is visually highlighted and stored in the background along a timeline, while simultaneously triggering a sample via MIDI. This project explores the intersection of computer vision and musical performance. The samples used in the performance were generated using the genPC app, creating a full-circle integration between generative audio and gesture-based control.

Highlights

1. Hand Tracking: Computer vision algorithms track hand positions and movements in real-time
2. Gesture Recognition: Machine learning models identify specific hand shapes and gestures
3. MIDI Triggering: Each recognized gesture triggers a corresponding sample via MIDI
4. Score Generation: All gestures and timing are recorded to create a reproducible musical score