AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance
This study addresses the limitations of traditional stage lighting design, which is typically time-consuming, labor-intensive, and difficult to transfer across contexts. To overcome these challenges, this work proposes an automated aesthetic generation framework that translates music into lighting by integrating expert knowledge, representation learning, and preference-adaptive modeling. Methodologically, it introduces LAMP alignment pretraining and a PAMoE mixture-of-experts module, alongside the construction of Musilux, the first paired music-lighting dataset. The framework further incorporates contrastive learning, gating networks, and retrieval-augmented generation to achieve precise adaptation of lighting cues. Evaluations conducted in both virtual simulations and physical laboratory settings demonstrate that the generated lighting sequences are visually coherent, semantically rich, and artistically expressive.