6 citations · 6 across the 1 of their papers we have counts for
5 papers
World-GAN: a Generative Model for Minecraft Worlds
Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generat…
TOAD-GAN: Coherent Style Level Generation from a Single Example
Maren Awiszus, Frederik Schubert, Bodo Rosenhahn
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 to…
Learning Disentangled Representations via Independent Subspaces
Maren Awiszus, Hanno Ackermann, Bodo Rosenhahn
Image generating neural networks are mostly viewed as black boxes, where any change in the input can have a number of globally effective changes on the output. In this work, we pro…
Unsupervised Features for Facial Expression Intensity Estimation over Time
Maren Awiszus, Stella Graßhof, Felix Kuhnke +1
The diversity of facial shapes and motions among persons is one of the greatest challenges for automatic analysis of facial expressions. In this paper, we propose a feature describ…
Markov Chain Neural Networks
Maren Awiszus, Bodo Rosenhahn
In this work we present a modified neural network model which is capable to simulate Markov Chains. We show how to express and train such a network, how to ensure given statistical…