图像游戏资产的智能生成:概念框架和技术现状的系统回顾

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius
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引用次数: 0

摘要

程序内容生成(PCG)可以应用于游戏中的各种任务,从叙述、关卡、声音到树木和武器。大量的游戏内容是由图像资产组成的,如云、建筑或植被,这些都不需要考虑游戏功能。在游戏之外,也有大量的文献研究这些元素的程序生成。研究的主体集中在产生特定资产的特定方法上,提供了对可用可能性的狭隘看法。因此,很难对所有的方法和可能性有一个清晰的了解,因为没有指导感兴趣的各方为他们的需要发现可能的方法和途径,也没有设施来指导他们通过每种技术或途径来规划使用它们的过程。因此,我们进行了系统的文献综述,共收到239篇论文。本文探讨了图像资产生成的最新方法,并检查了来自游戏内外广泛应用的研究。根据文献资料,已衍生出一个概念性框架来解决上述差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
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 239 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.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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