1 code implementation • CVPR 2023 • Jamie Watson, Mohamed Sayed, Zawar Qureshi, Gabriel J. Brostow, Sara Vicente, Oisin Mac Aodha, Michael Firman
We instead propose an implicit model for depth and use that to predict the occlusion mask directly.
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 • 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.
2 code implementations • 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.
2 code implementations • 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.