67 citations · 297 across the 34 of their papers we have counts for
5 papers · 1 filter
Unsupervised Generative 3D Shape Learning from Natural Images
Attila Szabó, Givi Meishvili, Paolo Favaro
In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way. For example, we d…
Learning to Have an Ear for Face Super-Resolution
Givi Meishvili, Simon Jenni, Paolo Favaro
We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input…
On Stabilizing Generative Adversarial Training with Noise
Simon Jenni, Paolo Favaro
We present a novel method and analysis to train generative adversarial networks (GAN) in a stable manner. As shown in recent analysis, training is often undermined by the probabili…
Emergence of Object Segmentation in Perturbed Generative Models
Adam Bielski, Paolo Favaro
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation…
Smart, Deep Copy-Paste
Tiziano Portenier, Qiyang Hu, Paolo Favaro +1
In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input…