DEEP LEARNING FOR PHOTOREALISTIC RENDERING IN ART EDUCATION

Abstract [English]

It has altered the environment of art education, as deep learning techniques provide a three-dimensional visual experience and creative opportunities of a scale never seen before. It is a study that dwells on the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs) and diffusion models in generating artistic scenes, high fidelity and photorealistic images and simulations to teach. The system that employs neural style transfer, the perceptual losses and the Volumetric rendering equations are used to reproduce the dynamism in the light transportation and texture with precision. The rendering process is mathematically formulated as a perceptual quality maximization by adversarial loss to make the results realistic by progressive refinement of the discriminator networks. It is the framework of art education that enable students imagine the conceptual compositions in different situations with different lighting conditions, material characteristics, which lead to the development of the awareness of spatial aesthetic, composition and the realism. Deep learning models make it more democratic for less expensive access to professional quality rendering tools without having to rely on more expensive ray tracing techniques. The introduction of these technologies into the teaching of art facilitates the learning by experience and creative experimentation but it is in line with sustainable and open digital art practices. Also, neural rendering allows for real-time feedback, which makes it possible to set up adaptive learning environments, where pupils can improve their work using AI-provided feedback. This paper focuses on the pedagogical, technologies, and aesthetic consequences of deep learning-based photorealism in art education and outlines a system by which intelligent rendering systems can be developed in the future that combine artistic creativity and computational accuracy.