activity
20172022
most citedA high fidelity synthetic face framework for computer vision

4 citations · 6 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Photo-realistic 360 Head Avatars in the Wild

Stanislaw Szymanowicz, Virginia Estellers, Tadas Baltrusaitis +1

Delivering immersive, 3D experiences for human communication requires a method to obtain 360 degree photo-realistic avatars of humans. To make these experiences accessible to all,…

cs.CV20211 cited

Fake It Till You Make It: Face analysis in the wild using synthetic data alone

Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt +5

We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone. The community has long enjoyed the benefits of synthesizing train…

cs.CV20204 cited

A high fidelity synthetic face framework for computer vision

Tadas Baltrusaitis, Erroll Wood, Virginia Estellers +6

Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition…

cs.CV2020

CONFIG: Controllable Neural Face Image Generation

Marek Kowalski, Stephan J. Garbin, Virginia Estellers +3

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generativ…

cs.CV2019

Contrastive Learning for Lifted Networks

Christopher Zach, Virginia Estellers

In this work we address supervised learning of neural networks via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel ha…

cs.CV2017

Compression for Smooth Shape Analysis

V. Estellers, F. R. Schmidt, D. Cremers

Most 3D shape analysis methods use triangular meshes to discretize both the shape and functions on it as piecewise linear functions. With this representation, shape analysis requir…