Search Results for author: Markus Gross

Found 37 papers, 10 papers with code

Learning a Generalized Physical Face Model From Data

no code implementations29 Feb 2024 Lingchen Yang, Gaspard Zoss, Prashanth Chandran, Markus Gross, Barbara Solenthaler, Eftychios Sifakis, Derek Bradley

Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits.

Anatomy Collision Avoidance +1

Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles

no code implementations12 Feb 2024 Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll

Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation.

3D Semantic Occupancy Prediction

An Implicit Physical Face Model Driven by Expression and Style

no code implementations27 Jan 2024 Lingchen Yang, Gaspard Zoss, Prashanth Chandran, Paulo Gotardo, Markus Gross, Barbara Solenthaler, Eftychios Sifakis, Derek Bradley

At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities.

Face Model Style Transfer

Implicit Neural Representation for Physics-driven Actuated Soft Bodies

no code implementations26 Jan 2024 Lingchen Yang, Byungsoo Kim, Gaspard Zoss, Baran Gözcü, Markus Gross, Barbara Solenthaler

Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation.

Artist-Friendly Relightable and Animatable Neural Heads

no code implementations6 Dec 2023 Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley

An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields.

Novel View Synthesis

Spatially Adaptive Cloth Regression with Implicit Neural Representations

no code implementations27 Nov 2023 Lei Shu, Vinicius Azevedo, Barbara Solenthaler, Markus Gross

The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.

Computational Efficiency regression

Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation

no code implementations23 Nov 2023 Markus Gross, Arne P. Raulf, Christoph Räth

We investigate the stationary (late-time) training regime of single- and two-layer linear underparameterized neural networks within the continuum limit of stochastic gradient descent (SGD) for synthetic Gaussian data.

A Perceptual Shape Loss for Monocular 3D Face Reconstruction

no code implementations30 Oct 2023 Christopher Otto, Prashanth Chandran, Gaspard Zoss, Markus Gross, Paulo Gotardo, Derek Bradley

In this work we propose a new loss function for monocular face capture, inspired by how humans would perceive the quality of a 3D face reconstruction given a particular image.

3D Face Reconstruction

Controllable Inversion of Black-Box Face Recognition Models via Diffusion

no code implementations23 Mar 2023 Manuel Kansy, Anton Raël, Graziana Mignone, Jacek Naruniec, Christopher Schroers, Markus Gross, Romann M. Weber

Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another.

Denoising Face Recognition

ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting

no code implementations ICCV 2023 Yingyan Xu, Gaspard Zoss, Prashanth Chandran, Markus Gross, Derek Bradley, Paulo Gotardo

Recent work on radiance fields and volumetric inverse rendering (e. g., NeRFs) has provided excellent results in building data-driven models of real scenes for novel view synthesis with high photorealism.

Inverse Rendering Novel View Synthesis

Kernel Aware Resampler

no code implementations CVPR 2023 Michael Bernasconi, Abdelaziz Djelouah, Farnood Salehi, Markus Gross, Christopher Schroers

This renders our model applicable for different types of data not seen during the training such as normals.

Super-Resolution

Frame Interpolation Transformer and Uncertainty Guidance

no code implementations CVPR 2023 Markus Plack, Karlis Martins Briedis, Abdelaziz Djelouah, Matthias B. Hullin, Markus Gross, Christopher Schroers

Through this error estimation, our method can produce even higher-quality intermediate frames using only a fraction of the time compared to a full rendering.

Optical Flow Estimation Video Frame Interpolation

Adaptive Convolutions for Structure-Aware Style Transfer

2 code implementations CVPR 2021 Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Markus Gross, Derek Bradley

Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content.

Image Generation Style Transfer

DuctTake: Spatiotemporal Video Compositing

no code implementations12 Jan 2021 Jan Rueegg, Oliver Wang, Aljoscha Smolic, Markus Gross

DuctTake is a system designed to enable practical compositing of multiple takes of a scene into a single video.

Semantic Segmentation

Blind Image Restoration with Flow Based Priors

no code implementations9 Sep 2020 Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah, Markus Gross, Christopher Schroers

In contrast to this, we propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation.

Denoising Image Enhancement +1

Lossy Image Compression with Normalizing Flows

no code implementations ICLR Workshop Neural_Compression 2021 Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Christopher Schroers

However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization.

Image Compression Quantization

Enriching Video Captions With Contextual Text

2 code implementations29 Jul 2020 Philipp Rimle, Pelin Dogan, Markus Gross

Understanding video content and generating caption with context is an important and challenging task.

Video Captioning

Shapley Value as Principled Metric for Structured Network Pruning

1 code implementation2 Jun 2020 Marco Ancona, Cengiz Öztireli, Markus Gross

The usual pruning pipeline consists of ranking the network internal filters and activations with respect to their contributions to the network performance, removing the units with the lowest contribution, and fine-tuning the network to reduce the harm induced by pruning.

Network Pruning

Lagrangian Neural Style Transfer for Fluids

1 code implementation2 May 2020 Byung-soo Kim, Vinicius C. Azevedo, Markus Gross, Barbara Solenthaler

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production.

Style Transfer

Transport-Based Neural Style Transfer for Smoke Simulations

no code implementations17 May 2019 Byung-soo Kim, Vinicius C. Azevedo, Markus Gross, Barbara Solenthaler

Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics.

Style Transfer

Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation

1 code implementation26 Mar 2019 Marco Ancona, Cengiz Öztireli, Markus Gross

The problem of explaining the behavior of deep neural networks has recently gained a lot of attention.

Disentangled Dynamic Representations from Unordered Data

no code implementations10 Dec 2018 Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Romann M. Weber

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input.

Neural Importance Sampling

2 code implementations11 Aug 2018 Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus Gross, Jan Novák

We propose to use deep neural networks for generating samples in Monte Carlo integration.

Deep Video Color Propagation

no code implementations9 Aug 2018 Simone Meyer, Victor Cornillère, Abdelaziz Djelouah, Christopher Schroers, Markus Gross

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames.

Style Transfer

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

1 code implementation6 Jun 2018 Byung-soo Kim, Vinicius C. Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, Barbara Solenthaler

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters.

PhaseNet for Video Frame Interpolation

no code implementations CVPR 2018 Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus Gross, Christopher Schroers

We show that this is superior to the hand-crafted heuristics previously used in phase-based methods and also compares favorably to recent deep learning based approaches for video frame interpolation on challenging datasets.

Video Frame Interpolation

A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)

1 code implementation CVPR 2018 Pelin Dogan, Boyang Li, Leonid Sigal, Markus Gross

The alignment of heterogeneous sequential data (video to text) is an important and challenging problem.

Dynamic Time Warping

Towards better understanding of gradient-based attribution methods for Deep Neural Networks

2 code implementations ICLR 2018 Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross

Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years.

text-classification Text Classification

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

no code implementations15 Sep 2017 Simon Kallweit, Thomas Müller, Brian McWilliams, Markus Gross, Jan Novák

To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source.

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

1 code implementation CVPR 2016 Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc van Gool, Markus Gross, Alexander Sorkine-Hornung

The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes.

Segmentation Semantic Segmentation +3

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