A Real-Time Gesture-Based Control Framework
This provides an interactive tool for live performances and installations, though it appears incremental as it integrates existing computer vision and machine learning techniques.
The paper tackles real-time gesture-based control of audio and music by analyzing live video input, enabling users like dancers to influence music through movements, achieving user-independent functionality with as few as 50 to 80 samples for gesture labeling.
We introduce a real-time, human-in-the-loop gesture control framework that can dynamically adapt audio and music based on human movement by analyzing live video input. By creating a responsive connection between visual and auditory stimuli, this system enables dancers and performers to not only respond to music but also influence it through their movements. Designed for live performances, interactive installations, and personal use, it offers an immersive experience where users can shape the music in real time. The framework integrates computer vision and machine learning techniques to track and interpret motion, allowing users to manipulate audio elements such as tempo, pitch, effects, and playback sequence. With ongoing training, it achieves user-independent functionality, requiring as few as 50 to 80 samples to label simple gestures. This framework combines gesture training, cue mapping, and audio manipulation to create a dynamic, interactive experience. Gestures are interpreted as input signals, mapped to sound control commands, and used to naturally adjust music elements, showcasing the seamless interplay between human interaction and machine response.