Level generation and style enhancement -- deep learning for game development overview

• URL: http://arxiv.org/abs/2107.07397v1
• Date: Thu, 15 Jul 2021 15:24:43 GMT
• Title: Level generation and style enhancement -- deep learning for game development overview
• Authors: Piotr Migda{\l}, Bart{\l}omiej Olechno, B{\l}a\.zej Podg\'orski
• Abstract summary: We present seven approaches to create level maps, each using statistical methods, machine learning, or deep learning. We aim to present new possibilities for game developers and level artists.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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