Search Results for author: Attila Szabó

Found 8 papers, 2 papers with code

Learning to Deblur and Rotate Motion-Blurred Faces

no code implementations14 Dec 2021 Givi Meishvili, Attila Szabó, Simon Jenni, Paolo Favaro

Our method handles the complexity of face blur by implicitly learning the geometry and motion of faces through the joint training on three large datasets: FFHQ and 300VW, which are publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we built.

Neural network wave functions and the sign problem

no code implementations11 Feb 2020 Attila Szabó, Claudio Castelnovo

Such states are the likely cause of the obstruction for NQS-based variational Monte Carlo to access the true ground states of these systems.

Variational Monte Carlo

Unsupervised Generative 3D Shape Learning from Natural Images

no code implementations1 Oct 2019 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.

Image Generation

Unsupervised 3D Shape Learning from Image Collections in the Wild

no code implementations26 Nov 2018 Attila Szabó, Paolo Favaro

To achieve realism, the generative model is trained adversarially against a discriminator that tries to distinguish between the output of the renderer and real images from the given data set.

FaceShop: Deep Sketch-based Face Image Editing

no code implementations24 Apr 2018 Tiziano Portenier, Qiyang Hu, Attila Szabó, Siavash Arjomand Bigdeli, Paolo Favaro, Matthias Zwicker

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.

Image Manipulation

Disentangling Factors of Variation by Mixing Them

no code implementations CVPR 2018 Qiyang Hu, Attila Szabó, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored.

General Classification

Challenges in Disentangling Independent Factors of Variation

2 code implementations ICLR 2018 Attila Szabó, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.

Image Generation

Cannot find the paper you are looking for? You can Submit a new open access paper.