no code implementations • CVPR 2023 • Silvan Weder, Guillermo Garcia-Hernando, Aron Monszpart, Marc Pollefeys, Gabriel Brostow, Michael Firman, Sara Vicente
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
1 code implementation • 6 Apr 2021 • Mehmet Ozgur Turkoglu, Eric Brachmann, Konrad Schindler, Gabriel Brostow, Aron Monszpart
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.
1 code implementation • NeurIPS 2020 • Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, Andrea Vedaldi
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects.
1 code implementation • CVPR 2020 • Jamie Watson, Michael Firman, Aron Monszpart, Gabriel J. Brostow
We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input.
no code implementations • 26 May 2019 • Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi
We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.
no code implementations • 20 Jun 2018 • Aron Monszpart, Paul Guerrero, Duygu Ceylan, Ersin Yumer, Niloy J. Mitra
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.
no code implementations • 14 May 2018 • Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi
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 require external supervision, such as explicit access to physical states, at training and sometimes even at test times.
no code implementations • 22 Dec 2017 • Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi
In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data.
no code implementations • 6 Jun 2017 • Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra
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 parameters.
no code implementations • 1 Mar 2017 • Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi
Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena.
1 code implementation • 29 Mar 2016 • Aron Monszpart, Nils Thuerey, Niloy J. Mitra
Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized.