Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach
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Abstract (arXiv)
Graphical assets play an important role in the design and development of games. There is potential in the use of AI-driven 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. There also no guidelines or heuristics for creating such tools. To address this gap we conducted a user study with 16 game designers and developers to examine their behaviour and interaction with generative tools for graphical assets. The findings highlight that early design stage is preferred by all participants. Designers and developers are inclined 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. The results also strongly 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 smoothly integrate into existing environments. 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 heuristics for creating tools that meet the expectations and needs of game designers and developers.
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References · 10
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2017Variational Autoencoders for Deforming 3D Mesh Modelscited by 69
- 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 67
- 2019DeepSketchHair: Deep Sketch-based 3D Hair Modelingcited by 24
- 2021ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesiscited by 23
- 2021MeInGame: Create a Game Character Face from a Single Portraitcited by 16
- 2020Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industrycited by 5
- 2020Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Studycited by 4
- 2021ShapeMOD: Macro Operation Discovery for 3D Shape Programscited by 4
- 2023Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Artcited by 1
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