Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

TL;DR

This paper reviews the current state of procedural graphical asset generation in games and beyond, proposing a comprehensive framework to guide future research and application in this diverse field.

Contribution

It provides a systematic review of 200 papers and introduces a conceptual framework to unify and guide graphical asset generation methods.

Findings

Comprehensive review of 200 papers on graphical asset generation

Development of a conceptual framework for procedural asset creation

Identification of diverse approaches across gaming and non-gaming domains

Abstract

Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 200 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps.