# Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach

arXiv: https://arxiv.org/abs/2503.02703 · HTML: https://mlsanity.com/paper/2503.02703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Variational Autoencoders for Deforming 3D Mesh Models](https://mlsanity.com/paper/1709.04307/) (2017)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [DeepSketchHair: Deep Sketch-based 3D Hair Modeling](https://mlsanity.com/paper/1908.07198/) (2019)
- [ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis](https://mlsanity.com/paper/2106.04912/) (2021)
- [MeInGame: Create a Game Character Face from a Single Portrait](https://mlsanity.com/paper/2102.02371/) (2021)
- [Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry](https://mlsanity.com/paper/2005.09324/) (2020)
- [Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study](https://mlsanity.com/paper/2005.07478/) (2020)
- [ShapeMOD: Macro Operation Discovery for 3D Shape Programs](https://mlsanity.com/paper/2104.06392/) (2021)
- [Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art](https://mlsanity.com/paper/2311.10129/) (2023)

## 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.