NEURAL NETWORK–BASED MODELS FOR GESTURE RECOGNITION AND CHOREOGRAPHIC PATTERN SYNTHESIS

The understanding of gestures and the synthesis of choreography can be viewed as two distinct sides of the human-AI interaction problem, which cannot be viewed as complementary and must be addressed through joint modeling of perception, synthesis, and real-time interaction. An interactive multimodal neural architecture consisting of spatial-temporal gesture encoding, latent motion representation learning, and style-conditioned choreography synthesis is proposed to facilitate end-to-end transfer of human movement from sense to expressive synthesized movement. The semantic consistency constraints in joint optimization will be used to ensure consistency between the perceived gesture intent and the synthesized choreography, while an edge cloud deployment approach will be utilized to facilitate interactive latency and energy-efficient execution. The experimental evaluation on benchmark datasets and live co-creative applications demonstrate high recognition accuracy, smooth and diverse motion synthesis, and successful semantic agreement and consistency in co-creating real-time settings. The formal user study also reveals high levels of perceptual realism, sense of expression, usability, and creative satisfaction, which verifies the framework as an excellent collaborative partner and not a passive generative tool. Managerial analysis Networks have lower production costs, scalable deployment opportunities, and therapeutic engagement of benefits in the areas of creative media, rehabilitation, and social robotics. The findings place gesture-based creative AI as a promising foundation of embodied intelligent interaction, and future research directions include the integration of emotion in creative choreography synthesis, adaptive reinforcement learning co-creation, and extreme low-latency edge synthesis