This repository presents some frontier researches using data driven methods, e.g. machine learning, for procedural content generation purpose. Literally, "procedural" means rule-based, the purpose of machine learning is to learn rules from data. This is paradigm-shifting since tech-artists are used to creating rules to generate assets, but their roles will transform into supervising and guiding computer to understand rules with data-driven methods.
https://github.com/chenweikai/3D-Machine-Learning
http://www.creativeai.net/
Please add through pull requests or open issue.
A step towards procedural terrain generation with GANs paper code
StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks paper
FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs paper code
Learning Design Patterns with Bayesian Grammar Induction paper
Guided proceduralization: Optimizing geometry processing and grammar extraction for architectural models paper
Bayesian grammar learning for inverse procedural modeling paper
Interactive Design of Probability Density Functions for Shape Grammars paper
Adversarially Tuned Scene Generation paper
Deep Convolutional Priors for Indoor Scene Synthesis paper
Make It Home: Automatic Optimization of Furniture Arrangement paper
Game Level Generation Using Neural Networks news
3D hair synthesis using volumetric variational autoencoders paper
HairNet: Single-View Hair Reconstruction using Convolutional Neural Networks paper
Stylizing Face Images via Multiple Exemplars paper
Photorealistic Facial Texture Inference Using Deep Neural Networks paper