Artificial Intelligence-generated Textures for Realistic Digital Environments in Concept Art Development
Year: 2026
Type: article
Abstract: The rapid development of artificial intelligence (AI) has had a tremendous impact as well on the digital content creating, particularly in the production of concept art. In the current paper, the authors examine AI-generated textures as the way of creating realistic digital spaces in the context of the impact on the aesthetic quality, the efficiency of the working process, and the artistic flexibility. The ancient methods of making texture take resources and time, but they offer enormous levels of control of the artwork outcome. In comparison to it, AI-based systems, i. e. diffusion models, generative adversarial networks (GANs), etc., can be automated, which enables them to generate high-quality textures that are more realistic and scalable. The methodology of the paper is structured appropriately and is connected to the integration of AI-created textures in concept art pipelines that are also supported by the experimental application and case study. The comparison and evaluation of traditional and AI-assisted methods are conducted in terms of the realism, consistency, and efficiency measures. The results indicate that AI-created textures save the time in the production process and do not or improve the visual appearance of manually built textures. Also, aesthetician remarks direct to an improved usability and exploration of the creative but still there are the control problems, reliance on the datasets, and computational requirements. The research concludes that AI-generated textures may be employed as the addition of the traditional one and enable developing a hybrid workflow that will contribute to the productivity and innovativeness. The outcomes are put together with the growing intersection of the art and technology, providing a glimpse into the future of the digital world design with AI.
Language: English
Topic: Digital Media and Visual Art
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7153772626