79 citations · 79 across the 2 of their papers we have counts for
4 papers
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
R. Kenny Jones, Theresa Barton, Xianghao Xu +5
Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: the…
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Wallace Lira, Johannes Merz, Daniel Ritchie +2
We introduce GANHopper, an unsupervised image-to-image translation network that transforms images gradually between two domains, through multiple hops. Instead of executing transla…
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
Daniel Ritchie, Kai Wang, Yu-an Lin
We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based represe…
An Improved Training Procedure for Neural Autoregressive Data Completion
Maxime Voisin, Daniel Ritchie
Neural autoregressive models are explicit density estimators that achieve state-of-the-art likelihoods for generative modeling. The D-dimensional data distribution is factorized in…