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Patrick Snape

7 accepted papers

2022

AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing

ECCV 2022poster

"Today’s Mixed Reality head-mounted displays track the user’s head pose in world space as well as the user’s hands for interaction in both Augmented Reality and Virtual Reality scenarios. While this is adequate to support user input, it unfortunately limits users’ virtual representations to just the…

2017

DenseReg: Fully Convolutional Dense Shape Regression In-The-Wild

CVPR 2017poster

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks 'in-the-wild'. We use such landmarks to establ…

Cited by 239PDFScholar
2017

Face Normals "In-The-Wild" Using Fully Convolutional Networks

CVPR 2017poster

In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neura…

Cited by 58PDFScholar
2016

Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment

CVPR 2016poster

Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despit…

Cited by 444PDFScholar
2015

Automatic Construction Of Robust Spherical Harmonic Subspaces

CVPR 2015poster

In this paper we propose a method to automatically recover a class specific low dimensional spherical harmonic basis from a set of in-the-wild facial images. We combine existing techniques for uncalibrated photometric stereo and low rank matrix decompositions in order to robustly recover a combined…

Cited by 23SourcePDFScholar