GAN-Supported Concept Art Workflows

We all love the fact that computers can execute annoying work for us. Work we already know how to do, work that is repeatable and, often, also repetitive. For the past few decades, new processes such as procedural generation have been helping us achieve diverse results with minimal input, leaving us to focus on being creative. Be it in the shape of procedural level generation of early rogue-like games, procedural nature such as Speedtree or, lately, the vast possibilities of procedural texturing with noise procedurality as seen in Substance Designer. Neural networks that generate new data and in the case of so called StyleGAN's it creates images or sequences. These machine learning frameworks are making two AI's play against each other to test and learn what would be considered to be a realistic result. This is based on the library you are feeding the network.