L-DGC: LLM-Based Dance Generative Control

The global expansion of K-pop has increased demand for AI-driven choreography learning. However, existing motion recognition models often struggle to capture fine-grained rhythm patterns and dynamic motion transitions across consecutive frames, limiting their ability to provide accurate and objective feedback. To address these challenges, this paper proposes a Large Language Model-based Dance Generative Control (L-DGC), an integrated framework for controllable dance generation and evaluation. The framework comprises four stages: a Visual Analysis Phase (VAP) for skeletal extraction; an Audio Analysis Phase (AAP) for rhythmic synchronization; a Multimodal Data Phase (MDP), which employs Long Short-Term Memory (LSTM) and Transformer architectures to evaluate movement accuracy; and a three-dimensional (3D) Transformation Phase (3TP), which converts two-dimensional (2D) skeletal data into 3D character animations within the Unity engine. Guided by an LLM, the framework performs real-time inference and iterative refinement to optimize choreographic data without requiring subjective expert assessment. By quantifying choreographic components and transforming 2D motion data into 3D representations, L-DGC provides an objective evaluation framework for dance learning. The proposed system has significant potential for artificial intelligence (AI)-based dance education, real-time feedback applications, and automated audition platforms in the entertainment industry.