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DC Field | Value | Language |
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dc.contributor.author | Fukaya, K | - |
dc.contributor.author | Daylamani-Zad, D | - |
dc.contributor.author | Agius, H | - |
dc.date.accessioned | 2024-01-15T14:58:21Z | - |
dc.date.available | 2024-01-15T14:58:21Z | - |
dc.date.issued | 2024 | - |
dc.identifier | ORCID iD: Damon Daylamani-Zad https://orcid.org/0000-0001-7849-458X | - |
dc.identifier | ORCID iD: Harry Agius https://orcid.org/0000-0002-8818-2683 | - |
dc.identifier.citation | Fukaya, K., Daylamani-Zad, D. and Agius, H. (2024) 'Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art', ACM Computing Surveys, 0 (accepted, in press), pp. 1 - 25. | en_US |
dc.identifier.issn | 0360-0300 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28016 | - |
dc.description | A preprint, arXiv:2311.10129v1 [cs.GR], is available on arXiv at: https://arxiv.org/abs/2311.10129. It has not been certified by peer reviewed. | - |
dc.description.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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ACM | en_US |
dc.relation.uri | https://arxiv.org/pdf/2311.10129.pdf | - |
dc.subject | graphical asset generation | en_US |
dc.subject | procedural generation | en_US |
dc.subject | games | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | deep learning | en_US |
dc.title | Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art | en_US |
dc.type | Article | en_US |
dc.relation.isPartOf | ACM Computing Surveys | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Brunel Design School Embargoed Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Embargoed until publication | 1.21 MB | Adobe PDF | View/Open |
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