151 citations · 561 across the 38 of their papers we have counts for
15 papers · 1 filter
Continuous Surface Embeddings
Natalia Neverova, David Novotny, Vasil Khalidov +3
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so f…
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs, Sébastien Ehrhadt, Hanbyul Joo +3
We consider the problem of obtaining dense 3D reconstructions of humans from single and partially occluded views. In such cases, the visual evidence is usually insufficient to iden…
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
Iro Laina, Ruth C. Fong, Andrea Vedaldi
The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them. In this paper, we con…
Support-set bottlenecks for video-text representation learning
Mandela Patrick, Po-Yao Huang, Yuki Asano +4
The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to…
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
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…
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart +4
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained…