https://www.sciencedaily.com/releases/2018/07/180716114557.htm

To address this challenge, a global team of computer scientists has developed a unique artificial intelligence-based technique that trains a network to learn to expand small textures into larger ones. The researchers' data-driven method leverages an AI technique called generative adversarial networks (GANs) to train computers to expand textures from a sample patch into larger instances that best resemble the original sample.

"Our approach successfully deals with non-stationary textures without any high level or semantic description of the large-scale structure," says Yang Zhou, lead author of the work and an assistant professor at Shenzhen University and Huazhong University of Science & Technology. "It can cope with very challenging textures, which, to our knowledge, no other existing method can handle. The results are realistic designs produced in high-resolution, efficiently, and at a much larger scale."