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

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 composed 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 239 accepted papers. This article 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.