no code implementations • ECCV 2020 • Simon Jenni, Givi Meishvili, Paolo Favaro
Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects.
no code implementations • 15 Oct 2024 • Givi Meishvili, James Clemoes, Charlie Hewitt, Zafiirah Hosenie, Xian Xiao, Martin de La Gorce, Tibor Takacs, Tadas Baltrusaitis, Antonio Criminisi, Chyna McRae, Nina Jablonski, Marta Wilczkowiak
We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system.
no code implementations • 14 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.
no code implementations • 21 Jul 2020 • Simon Jenni, Givi Meishvili, Paolo Favaro
Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects.
no code implementations • 1 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.
no code implementations • CVPR 2020 • Givi Meishvili, Simon Jenni, Paolo Favaro
To combine the aural and visual modalities, we propose a method to first build the latent representations of a face from the lone audio track and then from the lone low-resolution image.
1 code implementation • CVPR 2018 • Meiguang Jin, Givi Meishvili, Paolo Favaro
We present a method to extract a video sequence from a single motion-blurred image.