Publications related to GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Pascal Fua, Pavan P Ramdya, Adám Gosztolai, Victor Lobato Rios, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, Marco Pietro Abrate

LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques. Markerless three-dimensional (3D) pose estimation has become ...

NATURE PORTFOLIO2021

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2021

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

Biologically plausible unsupervised learning in shallow and deep neural networks

Bernd Albert Illing

The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

EPFL2021

GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss

Pascal Fua, Mathieu Salzmann, Victor Constantin, Shaifali Parashar, Erhan Gündogdu

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the b ...

2020

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Pascal Fua, Mathieu Salzmann, Agata Justyna Mosinska, Mateusz Kozinski

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

ELSEVIER2020

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

2020

An efficient analytical reduction of detailed nonlinear neuron models

Felix Schürmann, Pramod Shivaji Kumbhar, Michael Emiel Gevaert, Oren Amsalem, Idan Segev, Guy Eyal

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these ...

2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

Berend Smit, Matthew David Witman

Applications of atmospheric pressure plasma jets (APPJs) present challenging feedback control problems due to the complexity of the plasma-substrate interactions. The plasma treatment of complex substrates is particularly sensitive to changes in the physic ...

2019

GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

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LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Counting People by Estimating People Flows

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Biologically plausible unsupervised learning in shallow and deep neural networks

GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Shape Reconstruction by Learning Differentiable Surface Representations

An efficient analytical reduction of detailed nonlinear neuron models

Square Attack: a query-efficient black-box adversarial attack via random search

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Biologically plausible unsupervised learning in shallow and deep neural networks

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

An efficient analytical reduction of detailed nonlinear neuron models

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

Counting People by Estimating People Flows

GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss

Shape Reconstruction by Learning Differentiable Surface Representations

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Square Attack: a query-efficient black-box adversarial attack via random search