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Found 5 papers, 2 papers with code
World-GAN: a Generative Model for Minecraft Worlds
This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example.
TOAD-GAN: Coherent Style Level Generation from a Single Example
In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels.
Learning Disentangled Representations via Independent Subspaces
We use face images as our example of choice.
Markov Chain Neural Networks
In this work we present a modified neural network model which is capable to simulate Markov Chains.
Unsupervised Features for Facial Expression Intensity Estimation over Time
The proposed feature is compared to a state-of-the-art method for expression intensity estimation, which it outperforms.
