Designing AI-Driven Dance Choreography: Motion Capture and Generation Protocols

Abstract

In the era of rapid technological advancement, the emergence of artificial intelligence (AI) technology has reshaped the landscape of dance creation, proposing a new direction for artistic practice through human-machine collaboration. By integrating motion capture technology with generative algorithms and protocols, this study breaks through the limitations imposed by human physiological boundaries, creating a dynamic choreography system that enables virtual-reality interaction. This system extends the possibilities of movement language into other spatial dimensions, After reviewing the current state of AI-driven dance choreography research, this paper delves into two algorithms: a dance notation generation algorithm based on spatial feature fusion and a dance notation generation algorithm based on multi-temporal modeling. Based on the experimental results of these two algorithms, a platform architecture for the automatic generation and display of Latin dance notation is constructed, providing technical support for the documentation and preservation of dance art and culture in the new era.