A step towards procedural terrain generation with GANs

Abstract

Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA.

References in corpus (3)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Adversarial Feature Learning
• Adversarially Learned Inference

Cited by in corpus (2)

• DOOM Level Generation using Generative Adversarial Networks
• Interpreting Spatially Infinite Generative Models