Search Results for author: Florian Bernard

Found 37 papers, 5 papers with code

HTML: A Parametric Hand Texture Model for 3D Hand Reconstruction and Personalization

no code implementations ECCV 2020 Neng Qian, Jiayi Wang, Franziska Mueller, Florian Bernard, Vladislav Golyanik, Christian Theobalt

3D hand reconstruction from images is a widely-studied problem in computer vision and graphics, and has a particularly high relevance for virtual and augmented reality.

Neural Rendering

A Unified Framework for Implicit Sinkhorn Differentiation

1 code implementation13 May 2022 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.

Neural Implicit Representations for Physical Parameter Inference from a Single Video

no code implementations29 Apr 2022 Florian Hofherr, Lukas Koestler, Florian Bernard, Daniel Cremers

Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics.

A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching

1 code implementation27 Apr 2022 Paul Roetzer, Paul Swoboda, Daniel Cremers, Florian Bernard

We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes.

The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions

no code implementations5 Apr 2022 Dominik Muhle, Lukas Koestler, Nikolaus Demmel, Florian Bernard, Daniel Cremers

However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame.

Frame

HDSDF: Hybrid Directional and Signed Distance Functions for Fast Inverse Rendering

no code implementations30 Mar 2022 Tarun Yenamandra, Ayush Tewari, Nan Yang, Florian Bernard, Christian Theobalt, Daniel Cremers

Implicit neural representations of 3D shapes form strong priors that are useful for various applications, such as single and multiple view 3D reconstruction.

3D Reconstruction

Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis

no code implementations8 Dec 2021 Viktoria Ehm, Daniel Cremers, Florian Bernard

Traditionally, the concept of a shortest path is considered for graphs with scalar edge weights, which makes it possible to compute the length of a path by adding up the individual edge weights.

Semantic Segmentation

Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation

no code implementations NeurIPS 2021 Florian Bernard, Daniel Cremers, Johan Thunberg

We address the non-convex optimisation problem of finding a sparse matrix on the Stiefel manifold (matrices with mutually orthogonal columns of unit length) that maximises (or minimises) a quadratic objective function.

Graph Matching

Scalable Sinkhorn Backpropagation

no code implementations29 Sep 2021 Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers

Our main contribution is deriving a simple and efficient algorithm that performs this backward pass in closed form.

RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video

no code implementations22 Jun 2021 Jiayi Wang, Franziska Mueller, Florian Bernard, Suzanne Sorli, Oleksandr Sotnychenko, Neng Qian, Miguel A. Otaduy, Dan Casas, Christian Theobalt

Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions.

3D Reconstruction Sign Language Recognition

Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections

no code implementations31 Mar 2021 Zhenzhang Ye, Tarun Yenamandra, Florian Bernard, Daniel Cremers

While these approaches mainly focus on learning node and edge attributes, they completely ignore the 3D geometry of the underlying 3D objects depicted in the 2D images.

Graph Matching

Isometric Multi-Shape Matching

no code implementations CVPR 2021 Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer.

3D Reconstruction Object Tracking +1

i3DMM: Deep Implicit 3D Morphable Model of Human Heads

1 code implementation CVPR 2021 Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt

Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair.

PIE: Portrait Image Embedding for Semantic Control

no code implementations20 Sep 2020 Ayush Tewari, Mohamed Elgharib, Mallikarjun B R., Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt

We present the first approach for embedding real portrait images in the latent space of StyleGAN, which allows for intuitive editing of the head pose, facial expression, and scene illumination in the image.

Face Model

StyleRig: Rigging StyleGAN for 3D Control over Portrait Images

no code implementations CVPR 2020 Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt

StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination.

Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation

no code implementations14 Jan 2020 Lingjie Liu, Weipeng Xu, Marc Habermann, Michael Zollhoefer, Florian Bernard, Hyeongwoo Kim, Wenping Wang, Christian Theobalt

In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.

Image-to-Image Translation Novel View Synthesis +1

Convex Optimisation for Inverse Kinematics

no code implementations24 Oct 2019 Tarun Yenamandra, Florian Bernard, Jiayi Wang, Franziska Mueller, Christian Theobalt

We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations.

FML: Face Model Learning from Videos

no code implementations CVPR 2019 Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt

In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces.

3D Reconstruction Face Model +1

Higher-order Projected Power Iterations for Scalable Multi-Matching

no code implementations26 Nov 2018 Florian Bernard, Johan Thunberg, Paul Swoboda, Christian Theobalt

The matching of multiple objects (e. g. shapes or images) is a fundamental problem in vision and graphics.

Neural Rendering and Reenactment of Human Actor Videos

no code implementations11 Sep 2018 Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Hyeongwoo Kim, Florian Bernard, Marc Habermann, Wenping Wang, Christian Theobalt

In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.

Image Generation Neural Rendering

Synchronisation of Partial Multi-Matchings via Non-negative Factorisations

no code implementations16 Mar 2018 Florian Bernard, Johan Thunberg, Jorge Goncalves, Christian Theobalt

In order to deal with the inherent non-convexity of the permutation synchronisation problem, we use an initialisation procedure based on a novel rotation scheme applied to the solution of the spectral relaxation.

Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz

no code implementations CVPR 2018 Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, Christian Theobalt

To alleviate this problem, we present the first approach that jointly learns 1) a regressor for face shape, expression, reflectance and illumination on the basis of 2) a concurrently learned parametric face model.

Face Model

MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

no code implementations ICCV 2017 Ayush Tewari, Michael Zollhöfer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Pérez, Christian Theobalt

In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image.

Face Reconstruction

Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds

1 code implementation26 Feb 2016 Florian Bernard, Luis Salamanca, Johan Thunberg, Alexander Tack, Dennis Jentsch, Hans Lamecker, Stefan Zachow, Frank Hertel, Jorge Goncalves, Peter Gemmar

Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points.

Surface Reconstruction

Linear Shape Deformation Models with Local Support Using Graph-based Structured Matrix Factorisation

no code implementations CVPR 2016 Florian Bernard, Peter Gemmar, Frank Hertel, Jorge Goncalves, Johan Thunberg

Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation.

On Transitive Consistency for Linear Invertible Transformations between Euclidean Coordinate Systems

no code implementations2 Sep 2015 Johan Thunberg, Florian Bernard, Jorge Goncalves

Two direct or centralized synchronization methods are presented for different graph topologies; the first one for quasi-strongly connected graphs, and the second one for connected graphs.

Frame

A Solution for Multi-Alignment by Transformation Synchronisation

no code implementations CVPR 2015 Florian Bernard, Johan Thunberg, Peter Gemmar, Frank Hertel, Andreas Husch, Jorge Goncalves

Simulations demonstrate that for noisy transformations, a large proportion of missing data and even for wrong correspondence assignments the method delivers encouraging results.

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