48 citations · 148 across the 8 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos
Aron Monszpart, Paul Guerrero, Duygu Ceylan +2
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video. While the problem remains a subj…
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…
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…
Learning A Physical Long-term Predictor
Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra +1
Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena. Humans have successfully developed laws o…
SMASH: Physics-guided Reconstruction of Collisions from Videos
Aron Monszpart, Nils Thuerey, Niloy J. Mitra
Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in the real world can be difficult, as one has to g…