Adaptive & Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise
Abstract
This paper proposes a novel adaptive multi-resolution framework for generating terrains. Our framework combines diffusion-based generative network and novel frequency separated terrain features for terrain patch generation. Additionally, we propose to leverage learnable terrain super-resolution for enhancing generated terrain patch followed by novel kernel-based blending of these patches using Perlin noise to generate infinite terrain with realistic terrain features. We provide a comprehensive quantitative and qualitative evaluation of the proposed framework.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.