Search Results for author: Thomas Vetter

Found 13 papers, 8 papers with code

GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression

1 code implementation18 Mar 2022 Dennis Madsen, Jonathan Aellen, Andreas Morel-Forster, Thomas Vetter, Marcel Lüthi

Furthermore, we show how existing algorithms in the GiNGR framework can perform probabilistic registration to obtain a distribution of different registrations instead of a single best registration.

GPR regression

A Closest Point Proposal for MCMC-based Probabilistic Surface Registration

2 code implementations ECCV 2020 Dennis Madsen, Andreas Morel-Forster, Patrick Kahr, Dana Rahbani, Thomas Vetter, Marcel Lüthi

Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence.

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

no code implementations19 Nov 2018 Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.

Bayesian Inference valid

Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?

1 code implementation19 Nov 2018 Adam Kortylewski, Bernhard Egger, Andreas Morel-Forster, Andreas Schneider, Thomas Gerig, Clemens Blumer, Corius Reyneke, Thomas Vetter

We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data.

Data Augmentation Face Model +4

Probabilistic Joint Face-Skull Modelling for Facial Reconstruction

no code implementations CVPR 2018 Dennis Madsen, Marcel Lüthi, Andreas Schneider, Thomas Vetter

We create a probabilistic joint face-skull model and show how to obtain a distribution of plausible face shapes given a skull shape.

Face Model Stochastic Optimization

Training Deep Face Recognition Systems with Synthetic Data

2 code implementations16 Feb 2018 Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster, Thomas Vetter

In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly.

Face Model Face Recognition

Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

2 code implementations5 Dec 2017 Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter

4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation.

Face Recognition

Morphable Face Models - An Open Framework

2 code implementations25 Sep 2017 Thomas Gerig, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Lüthi, Sandro Schönborn, Thomas Vetter

Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs).

Face Model Gaussian Processes

Greedy Structure Learning of Hierarchical Compositional Models

no code implementations CVPR 2019 Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.

Object Transfer Learning

Gaussian Process Morphable Models

no code implementations23 Mar 2016 Marcel Lüthi, Christoph Jud, Thomas Gerig, Thomas Vetter

However, while for SSMs the shape variation is restricted to the span of the example data, with GPMMs we can define the shape variation using any Gaussian process.

Gaussian Processes

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