Procedural Level Generation with Diffusion Models from a Single Example
DOI:
https://doi.org/10.1609/aaai.v38i9.28865Keywords:
HAI: Game Design -- Procedural Content Generation & Storytelling, ML: Deep Generative Models & AutoencodersAbstract
Level generation is a central focus of Procedural Content Generation (PCG), yet deep learning-based approaches are limited by scarce training data, i.e., human-designed levels. Despite being a dominant framework, Generative Adversarial Networks (GANs) exhibit a substantial quality gap between generated and human-authored levels, alongside rising training costs, particularly with increasing token complexity. In this paper, we introduce a diffusion-based generative model that learns from just one example. Our approach involves two core components: 1) an efficient yet expressive level representation, and 2) a latent denoising network with constrained receptive fields. To start with, our method utilizes token semantic labels, similar to word embeddings, to provide dense representations. This strategy not only surpasses one-hot encoding in representing larger game levels but also improves stability and accelerates convergence in latent diffusion. In addition, we adapt the denoising network architecture to confine the receptive field to localized patches of the data, aiming to facilitate single-example learning. Extensive experiments demonstrate that our model is capable of generating stylistically congruent samples of arbitrary sizes compared to manually designed levels. It suits a wide range of level structures with fewer artifacts than GAN-based approaches. The source code is available at https://github.com/shiqi-dai/diffusioncraft.Downloads
Published
2024-03-24
How to Cite
Dai, S., Zhu, X., Li, N., Dai, T., & Wang, Z. (2024). Procedural Level Generation with Diffusion Models from a Single Example. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10021-10029. https://doi.org/10.1609/aaai.v38i9.28865
Issue
Section
AAAI Technical Track on Humans and AI