Algorithmic art meets computational creativity: Reinventing interactive multimedia experiences through convergent innovation - Peng, Bowen (Edelweiss Applied Science and Technology, 2025)
Traditional multimedia systems often lack real-time emotional responsiveness and visual richness. This research addresses this by proposing the Creative Interaction through Multimedia Algorithmic Convergence (CIMAC) framework, integrating algorithmic art and computational creativity to generate dynamic, user-driven visual art. CIMAC operates in four stages: 1) Kinect sensors and face recognition capture user gestures and expressions; 2) A Convolutional Neural Network (CNN) analyzes facial data to determine emotional state; 3) A Generative Adversarial Network (GAN) generates artistic visuals based on the recognized emotion; 4) The Coyote Optimization Algorithm (COA) dynamically generates and optimizes mathematical patterns (e.g., fractals, grids) for structural foundation and aesthetic harmony. The GAN-generated visuals are then fused with the COA-driven patterns to create cohesive artwork. This integrated output is displayed on a large interactive screen and continuously adapted in real-time to user movements and emotional shifts. The synergistic CNN+GAN+COA integration within CIMAC demonstrably improves output quality scores by up to 0.3 and reduces content creation and engagement time by up to 6 hours compared to traditional methods. This yields a powerful platform for highly personalized and immersive interactive multimedia experiences.