智能生成图形游戏资产的评估指标:基于系统调查的框架。

Kaisei Fukaya, Damon Daylamani-Zad, Harry Agius
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引用次数: 0

摘要

图形资产生成系统有可能为用户提供一键式的高质量资产。然而,资产的形式多种多样,生成资产的方法也多种多样。如果从业人员希望验证或比较他们的实施情况,就有必要对这些方法进行量化评估。此外,还可以为新方法提供努力或超越的基准。虽然大多数方法都在各自的领域内使用久经考验的指标进行验证,但并没有找到最合适的统一方法。我们在近 200 篇文献库的基础上提出了一个框架,为选择指标提供指导,以评估所产生的人工制品的有效性和质量,以及方法的操作能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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