6 citations · 7 across the 5 of their papers we have counts for
7 papers · 1 filter
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler +3
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…
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
Roman Shapovalov, David Novotny, Benjamin Graham +2
We tackle the problem of monocular 3D reconstruction of articulated objects like humans and animals. We contribute DensePose 3D, a method that can learn such reconstructions in a w…
Discovering Relationships between Object Categories via Universal Canonical Maps
Natalia Neverova, Artsiom Sanakoyeu, Patrick Labatut +2
We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predict…
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
Marvin Eisenberger, David Novotny, Gael Kerchenbaum +4
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and po…
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Philipp Henzler, Jeremy Reizenstein, Patrick Labatut +4
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. While several recent works have obtained analogous…
Low Bandwidth Video-Chat Compression using Deep Generative Models
Maxime Oquab, Pierre Stock, Oran Gafni +9
To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's de…