Masked 3D conditional generative adversarial network for rock mesh generation

Kuang, Ping; Luo, Dingli; Wang, Haoshuang

doi:10.1007/s10586-018-2653-6

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The highlevel 3D categorisations include scenery, hard-surface and characters/creatures. Scenery groups together clouds [24], roads [25], terrain [26], trees [27] and rocks [28]. Hardsurface groups buildings [2], furniture [29], vehicles [5] and props [30].…”

Section: Evaluation Metrics Frameworkmentioning

confidence: 99%

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Fukaya,

Daylamani-Zad,

Agius

2024

IEEE Trans. Pattern Anal. Mach. Intell.

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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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Section: Evaluation Metrics Frameworkmentioning

confidence: 99%

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Fukaya,

Daylamani-Zad,

Agius

2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

Fukaya,

Daylamani-Zad,

Agius

2024

ACM Comput. Surv.

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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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Masked 3D conditional generative adversarial network for rock mesh generation

Cited by 2 publications

References 9 publications

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

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