Generative adversarial network-driven dynamic motion generation and optimization for labor roles

Addressing the challenges of spatio-temporal continuity and ergonomic optimization in dynamic action generation for theatrical performance roles, this study introduces a novel generative adversarial network (GAN)-driven framework. We construct a spatio-temporal constraint generator utilizing the Archive of Motion Capture as Surface Shapes (AMASS) motion capture dataset, integrated with a biomechanical feedback mechanism to optimize joint load distribution. Experimental validation demonstrates that our method significantly enhances action naturalness in choreographed fight and prop-handling scenarios, achieving an Fréchet Inception Distance (FID) of 29.7 (16.6% lower than the optimal baseline PhysGAN). Furthermore, it reduces peak lumbar spine loads by 24.2% and optimizes rapid entire body assessment (REBA) scores by 21.5% compared to PhysGAN (from 6.5 to 5.1). The proposed framework delivers a high-fidelity, low-risk action generation solution for automated performer-technology collaboration systems, enhancing performance realism and actor safety with demonstrable theatrical/cinematic application value.