49 citations · 122 across the 5 of their papers we have counts for
6 papers
Learning to Deblur and Rotate Motion-Blurred Faces
Givi Meishvili, Attila Szabó, Simon Jenni +1
We propose a solution to the novel task of rendering sharp videos from new viewpoints from a single motion-blurred image of a face. Our method handles the complexity of face blur b…
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…
Unsupervised 3D Shape Learning from Image Collections in the Wild
Attila Szabó, Paolo Favaro
We present a method to learn the 3D surface of objects directly from a collection of images. Previous work achieved this capability by exploiting additional manual annotation, such…
FaceShop: Deep Sketch-based Face Image Editing
Tiziano Portenier, Qiyang Hu, Attila Szabó +3
We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features…
Challenges in Disentangling Independent Factors of Variation
Attila Szabó, Qiyang Hu, Tiziano Portenier +2
We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classificat…
Disentangling Factors of Variation by Mixing Them
Qiyang Hu, Attila Szabó, Tiziano Portenier +2
We propose an approach to learn image representations that consist of disentangled factors of variation without exploiting any manual labeling or data domain knowledge. A factor of…