Search Results for author: Patrik Huber

Found 13 papers, 4 papers with code

Text2Face: A Multi-Modal 3D Face Model

no code implementations5 Mar 2023 Will Rowan, Patrik Huber, Nick Pears, Andrew Keeling

We present the first 3D morphable modelling approach, whereby 3D face shape can be directly and completely defined using a textual prompt.

Face Model

Neural Implicit Surface Reconstruction from Noisy Camera Observations

no code implementations2 Oct 2022 Sarthak Gupta, Patrik Huber

Representing 3D objects and scenes with neural radiance fields has become very popular over the last years.

Camera Calibration Surface Reconstruction

Neural apparent BRDF fields for multiview photometric stereo

no code implementations14 Jul 2022 Meghna Asthana, William A. P. Smith, Patrik Huber

We propose to tackle the multiview photometric stereo problem using an extension of Neural Radiance Fields (NeRFs), conditioned on light source direction.

Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild

no code implementations14 Mar 2018 Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter JB Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, Matthias Rätsch

To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans.

3D Face Reconstruction 3D Reconstruction +1

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

6 code implementations CVPR 2018 Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu

We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).

 Ranked #1 on Face Alignment on 300W (NME_inter-pupil (%, Common) metric)

Data Augmentation Face Alignment

3D Morphable Models as Spatial Transformer Networks

1 code implementation23 Aug 2017 Anil Bas, Patrik Huber, William A. P. Smith, Muhammad Awais, Josef Kittler

In this paper, we show how a 3D Morphable Model (i. e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network.

A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment

no code implementations1 Jun 2016 Michael Grupp, Philipp Kopp, Patrik Huber, Matthias Rätsch

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics.

3D Face Modelling Face Recognition +1

3D Face Tracking and Texture Fusion in the Wild

no code implementations22 May 2016 Patrik Huber, Philipp Kopp, Matthias Rätsch, William Christmas, Josef Kittler

We present a fully automatic approach to real-time 3D face reconstruction from monocular in-the-wild videos.

3D Face Reconstruction Face Model

A Multiresolution 3D Morphable Face Model and Fitting Framework

1 code implementation1 Feb 2016 Patrik Huber, Guosheng Hu, Rafael Tena, Pouria Mortazavian, Willem P. Koppen, William Christmas, Matthias Rätsch, Josef Kittler

In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes.

3D Face Reconstruction Face Generation +4

Fitting 3D Morphable Models using Local Features

1 code implementation8 Mar 2015 Patrik Huber, Zhen-Hua Feng, William Christmas, Josef Kittler, Matthias Rätsch

Our approach is unique in that we are the first to use local features to fit a Morphable Model.


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