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

Source: [Level generation and style enhancement -- deep learning for game development overview](<https://www.sota2.com/research/paper/68fa6a601e209131cea27d99>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:23:37.152Z

Authors: Piotr Migda{\\l}, Bart{\\l}omiej Olechno, B{\\l}a\\.zej Podg\\'orski

Published: 2021-07-15T15:24:43.000Z

## 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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.07397>)

[PDF](<https://arxiv.org/pdf/2107.07397.pdf>)