Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework
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View abstract on PubMed
Summary
This summary is machine-generated.Evaluating generative graphical asset systems requires a unified approach. This study presents a framework to guide metric selection for assessing asset quality and system capabilities, aiding practitioners in validation and comparison.
Area Of Science
- Computer Graphics
- Artificial Intelligence
- Human-Computer Interaction
Background
- Generative systems offer automated creation of graphical assets.
- Current evaluation methods lack standardization, hindering comparison and validation.
- A unified approach is needed for quantitative assessment of generative methods.
Purpose Of The Study
- To propose a framework for selecting appropriate metrics for evaluating generative graphical asset systems.
- To provide guidance for practitioners in validating and comparing different generative approaches.
- To establish benchmarks for future research in generative asset creation.
Main Methods
- A comprehensive literature review of approximately 200 papers was conducted.
- A framework was developed based on the literature to guide metric selection.
- The framework considers asset validity, quality, and operational capabilities.
Main Results
- The framework facilitates informed decisions on metric selection for diverse generative methods.
- It addresses the need for standardized evaluation in the field of graphical asset generation.
- Guidance is provided for assessing both the output artefacts and the system's performance.
Conclusions
- A unified framework is crucial for the quantitative evaluation of generative graphical asset systems.
- The proposed framework aids in selecting relevant metrics for assessing asset quality and system performance.
- This work supports the advancement of generative AI for graphical asset creation through standardized evaluation.
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