no code implementations • 12 Apr 2024 • Lucas Relic, Roberto Azevedo, Markus Gross, Christopher Schroers
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates.
no code implementations • 29 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.
no code implementations • 12 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.
no code implementations • 27 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.
no code implementations • 26 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.
no code implementations • 6 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.
no code implementations • 27 Nov 2023 • Lei Shu, Vinicius Azevedo, Barbara Solenthaler, Markus Gross
The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.
no code implementations • 23 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.
no code implementations • 30 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.
Ranked #7 on 3D Face Reconstruction on REALY (side-view)
no code implementations • 23 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.
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.
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.
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.
no code implementations • 7 Jan 2022 • Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah, Markus Gross, Christopher Schroers
Recently, significant progress has been made in learned image and video compression.
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.
no code implementations • 12 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.
no code implementations • 9 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.
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.
2 code implementations • 29 Jul 2020 • Philipp Rimle, Pelin Dogan, Markus Gross
Understanding video content and generating caption with context is an important and challenging task.
1 code implementation • 2 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.
1 code implementation • 2 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.
no code implementations • 25 Sep 2019 • Sebastien Foucher, Jingwei Tang, Vinicius da Costa de Azevedo, Byungsoo Kim, Markus Gross, Barbara Solenthaler
In this paper we propose a physics-aware neural network for inpainting fluid flow data.
no code implementations • 17 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.
no code implementations • SEMEVAL 2019 • Yeyao Zhang, Eleftheria Tsipidi, Sasha Schriber, Mubbasir Kapadia, Markus Gross, Ashutosh Modi
However, translating natural language text into animation is a challenging task.
1 code implementation • 26 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.
no code implementations • CVPR 2019 • Pelin Dogan, Leonid Sigal, Markus Gross
We propose an end-to-end approach for phrase grounding in images.
no code implementations • 10 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.
2 code implementations • 11 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.
no code implementations • 9 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.
1 code implementation • 6 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.
no code implementations • CVPR 2018 • Riccardo Roveri, Lukas Rahmann, Cengiz Oztireli, Markus Gross
We propose a novel neural network architecture for point cloud classification.
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.
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.
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.
no code implementations • 15 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.
no code implementations • CVPR 2017 • Endri Dibra, Himanshu Jain, Cengiz Oztireli, Remo Ziegler, Markus Gross
In this work, we present a novel method for capturing human body shape from a single scaled silhouette.
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.
no code implementations • ICCV 2015 • Federico Perazzi, Oliver Wang, Markus Gross, Alexander Sorkine-Hornung
We present a novel approach to video segmentation using multiple object proposals.
Ranked #77 on Semi-Supervised Video Object Segmentation on DAVIS 2016