no code implementations • 22 Dec 2022 • 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 • 11 Oct 2022 • Eduardo Arnold, Jamie Wynn, Sara Vicente, Guillermo Garcia-Hernando, Áron Monszpart, Victor Adrian Prisacariu, Daniyar Turmukhambetov, Eric Brachmann
Can we relocalize in a scene represented by a single reference image?
no code implementations • CVPR 2022 • Ivor J. A. Simpson, Sara Vicente, Neill D. F. Campbell
Similarly to distillation approaches, our single network is trained to maximise the probability of samples from pre-trained probabilistic models, in this work we use a fixed ensemble of networks.
no code implementations • CVPR 2020 • Garoe Dorta, Sara Vicente, Neill D. F. Campbell, Ivor J. A. Simpson
Deep neural networks have recently been used to edit images with great success, in particular for faces.
1 code implementation • 3 Apr 2018 • Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, Ivor Simpson
This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modeled.
1 code implementation • CVPR 2018 • Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, Ivor Simpson
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image.
no code implementations • 22 Mar 2015 • Joao Carreira, Sara Vicente, Lourdes Agapito, Jorge Batista
In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image.
no code implementations • CVPR 2014 • Sara Vicente, Joao Carreira, Lourdes Agapito, Jorge Batista
We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations.