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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 | 2024-01-15T16:37:06Z | - |
dc.date.available | 2024-01-15T16:37:06Z | - |
dc.date.issued | 2024-05-09 | - |
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) 'Evaluation metrics for intelligent generation of graphical game assets: a systematic survey-based framework', IEEE Transactions on Pattern Analysis and Machine Intelligence, 0 (early access), pp. 1 - 20. doi: 10.1109/TPAMI.2024.3398998. | en_US |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28019 | - |
dc.description.abstract | Generative systems for graphical assets have the potential to provide users with high quality assets at the push of a button. However, there are many forms of assets, and many approaches for producing them. Quantitative evaluation of these methods is necessary if practitioners wish to validate or compare their implementations. Furthermore, providing benchmarks for new methods to strive for or surpass. While most methods are validated using tried-and-tested metrics within their own domains, there is no unified method of finding the most appropriate. We present a framework based on a literature pool of close to 200 papers, that provides guidance in selecting metrics to evaluate the validity and quality of artefacts produced, and the operational capabilities of the method. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | © Copyright 2024 The Author(s). Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License (). For more information, see https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | evaluation metrics | en_US |
dc.subject | graphical game assets | en_US |
dc.subject | artificial Intelligence | en_US |
dc.subject | PCG | en_US |
dc.title | Evaluation metrics for intelligent generation of graphical game assets: a systematic survey-based framework | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TPAMI.2024.3398998 | - |
dc.relation.isPartOf | IEEE Transactions on Pattern Analysis and Machine Intelligence | - |
pubs.publication-status | Published online | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Brunel Design School Research Papers |
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