Generative Tools for Graphical Assets: Empirical Guidelines based on Game Designers' and Developers' Preferences
Graphical assets play an important role in the design and development of games. There is potential in the use of generative tools, to aid in creating graphical assets, thus improving game design and development pipelines. However, there is little research to address how the generative methods can fit into the wider pipeline. We conducted a user study with 16 game designers and developers to examine their preferences regarding generative tools for graphical assets. The findings highlight that early design stage is preferred by all participants (mean values above 0.67 and p < .001 for early stages). Designers and developers prefer to use such tools for creating large amounts of variations at the cost of quality as they can improve the quality of the artefacts once they generate a suitable asset (mean value 0.17 where 1 is high quality, p < .001). They also strongly (mean value .78, p < .001) raised the need for better integration of such tools in existing design and development environments and the need for the outputs to be in common data formats, to be manipulatable and integrate smoothly into existing environments (mean 3.5 out of 5, p = .004). The study also highlights the requirement for further emphasis on the needs of the users to incorporate these tools effectively in existing pipelines. Informed by these results, we provide a set of guidelines for creating tools that meet the expectations and needs of game designers and developers.
Code (0)
등록된 구현이 없습니다.
Tasks
Game DesignMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AI-Generated Figures in Academic Publishing: Policies, Tools, and Practical Guidelines
The rapid advancement of generative AI has introduced a new class of tools capable of producing publication-quality scientific figures, graphical abstracts, and data visualizations. However, academic publishers have resp…
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
Generating explicit textured 3D city assets from a single satellite image is important for urban simulation and digital twins. Most prior methods, however, learn 3D proxies optimized for rendering street-view images or v…
A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series
We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbatio…
ClusteringTime SeriesTime Series AnalysisGuidelines and a Corpus for Extracting Biographical Events
Despite biographies are widely spread within the Semantic Web, resources and approaches to automatically extract biographical events are limited. Such limitation reduces the amount of structured, machine-readable biograp…
Event ExtractionA Bayesian Graphical Approach for Large-Scale Portfolio Management with Fewer Historical Data
Managing a large-scale portfolio with many assets is one of the most challenging tasks in the field of finance. It is partly because estimation of either covariance or precision matrix of asset returns tends to be unstab…
Management