1 code implementation • ICCV 2023 • Roman Shapovalov, Yanir Kleiman, Ignacio Rocco, David Novotny, Andrea Vedaldi, Changan Chen, Filippos Kokkinos, Ben Graham, Natalia Neverova
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
no code implementations • CVPR 2023 • Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint?
no code implementations • CVPR 2023 • Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein, Ignacio Rocco, Natalia Neverova, Andrea Vedaldi, David Novotny
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
no code implementations • CVPR 2022 • David Novotny, Ignacio Rocco, Samarth Sinha, Alexandre Carlier, Gael Kerchenbaum, Roman Shapovalov, Nikita Smetanin, Natalia Neverova, Benjamin Graham, Andrea Vedaldi
Compared to weaker deformation models, this significantly reduces the reconstruction ambiguity and, for dynamic objects, allows Keypoint Transporter to obtain reconstructions of the quality superior or at least comparable to prior approaches while being much faster and reliant on a pre-trained monocular depth estimator network.
1 code implementation • ICCV 2021 • Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotny
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data.
no code implementations • ICCV 2021 • Roman Shapovalov, David Novotny, Benjamin Graham, Patrick Labatut, Andrea Vedaldi
The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object.
no code implementations • CVPR 2021 • Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.
1 code implementation • NeurIPS 2020 • David Novotny, Roman Shapovalov, Andrea Vedaldi
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects.
no code implementations • 23 Jun 2014 • Roman Shapovalov, Dmitry Vetrov, Anton Osokin, Pushmeet Kohli
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training.
no code implementations • CVPR 2013 • Roman Shapovalov, Dmitry Vetrov, Pushmeet Kohli
Experimental results show that the spatial dependencies learned by our method significantly improve the accuracy of segmentation.