Immersive Multimedia Art Design Based on Deep Learning Intelligent Vr Technology

Abstract

Virtual Reality (VR) based art design offers a revolutionary platform for artists to explore new dimensions of creativity and expression. By immersing users in virtual environments, VR art transcends traditional limitations, enabling the creation of immersive and interactive experiences that defy conventional boundaries. Artists harness VR technology to sculpt three-dimensional forms, paint in volumetric space, and manipulate light and sound in ways previously unimaginable. Moreover, VR facilitates collaboration and audience engagement, allowing viewers to interact with and even become part of the artwork. From virtual exhibitions to immersive installations, VR art opens up endless possibilities for artistic innovation and audience participation. This paper introduces an innovative approach to immersive multimedia art design, leveraging deep learning intelligent Virtual Reality (VR) technology alongside Centralized Data Transmission Classification (CDTC) with the integration of IoT multimedia sensor data. By combining these advanced technologies, artists can create captivating and dynamic multimedia experiences that engage multiple senses and transcend traditional artistic boundaries. Deep learning algorithms analyze and interpret vast amounts of sensory data collected from IoT multimedia sensors, enabling the generation of immersive VR environments that respond intelligently to user interactions. CDTC facilitates efficient data transmission and classification, optimizing the integration of real-time sensor data into the VR experience. Through a series of experiments and simulations, the efficacy of the proposed framework is demonstrated, showcasing its ability to create immersive art installations that dynamically adapt to user input and environmental stimuli. in a simulated VR environment, deep learning algorithms processed IoT sensor data in real-time, resulting in an average classification accuracy of 92% for environmental stimuli recognition. Additionally, CDTC facilitated efficient data transmission, reducing latency by 30% compared to traditional methods.