{"title":"智能生成图形游戏资产的评估指标:基于系统调查的框架。","authors":"Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius","doi":"10.1109/TPAMI.2024.3398998","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework.\",\"authors\":\"Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius\",\"doi\":\"10.1109/TPAMI.2024.3398998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":94034,\"journal\":{\"name\":\"IEEE transactions on pattern analysis and machine intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on pattern analysis and machine intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TPAMI.2024.3398998\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on pattern analysis and machine intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TPAMI.2024.3398998","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/6 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework.
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.