  We present practical approaches of using deep learning to create and enhance
level maps and textures for video games -- desktop, mobile, and web. We aim to
present new possibilities for game developers and level artists. The task of
designing levels and filling them with details is challenging. It is both
time-consuming and takes effort to make levels rich, complex, and with a
feeling of being natural. Fortunately, recent progress in deep learning
provides new tools to accompany level designers and visual artists. Moreover,
they offer a way to generate infinite worlds for game replayability and adjust
educational games to players' needs. We present seven approaches to create
level maps, each using statistical methods, machine learning, or deep learning.
In particular, we include:
  - Generative Adversarial Networks for creating new images from existing
examples (e.g. ProGAN).
  - Super-resolution techniques for upscaling images while preserving crisp
detail (e.g. ESRGAN).
  - Neural style transfer for changing visual themes.
  - Image translation - turning semantic maps into images (e.g. GauGAN).
  - Semantic segmentation for turning images into semantic masks (e.g. U-Net).
  - Unsupervised semantic segmentation for extracting semantic features (e.g.
Tile2Vec).
  - Texture synthesis - creating large patterns based on a smaller sample (e.g.
InGAN).
