activity
20162022
most citediMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos

48 citations · 148 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2022

Removing Objects From Neural Radiance Fields

Silvan Weder, Guillermo Garcia-Hernando, Aron Monszpart +4

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Befo…

cs.CV2021

Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision

Mehmet Ozgur Turkoglu, Eric Brachmann, Konrad Schindler +2

Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment. The highest-scoring methods are…

cs.CV2020★ 35 cited

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…

cs.CV2020

Footprints and Free Space from a Single Color Image

Jamie Watson, Michael Firman, Aron Monszpart +1

Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to…

cs.CV2019★ 3 cited

Unsupervised Intuitive Physics from Past Experiences

Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra +1

We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning. In addition to learning general physical p…

cs.CV2018

Unsupervised Intuitive Physics from Visual Observations

Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra +1

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they re…

cs.CV2017★ 8 cited

Learning to Represent Mechanics via Long-term Extrapolation and Interpolation

Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi +1

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated…