171 citations · 535 across the 42 of their papers we have counts for
37 papers · 1 filter
SIGMA: Scale-Invariant Global Sparse Shape Matching
Maolin Gao, Paul Roetzer, Marvin Eisenberger +4
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected Lapl…
Non-Separable Multi-Dimensional Network Flows for Visual Computing
Viktoria Ehm, Daniel Cremers, Florian Bernard
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to…
Unsupervised Learning of Robust Spectral Shape Matching
Dongliang Cao, Paul Roetzer, Florian Bernard
We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous dee…
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching
Dongliang Cao, Florian Bernard
The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common…
Universe Points Representation Learning for Partial Multi-Graph Matching
Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard
Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this wo…
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching
Paul Roetzer, Zorah Lähner, Florian Bernard
We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a short…
HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow
Jiayi Wang, Diogo Luvizon, Franziska Mueller +4
Reconstructing two-hand interactions from a single image is a challenging problem due to ambiguities that stem from projective geometry and heavy occlusions. Existing methods are d…
A Comparative Study of Graph Matching Algorithms in Computer Vision
Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter +5
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide ra…
Unsupervised Deep Multi-Shape Matching
Dongliang Cao, Florian Bernard
3D shape matching is a long-standing problem in computer vision and computer graphics. While deep neural networks were shown to lead to state-of-the-art results in shape matching,…
Efficient and Flexible Sublabel-Accurate Energy Minimization
Zhakshylyk Nurlanov, Daniel Cremers, Florian Bernard
We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recog…
A Unified Framework for Implicit Sinkhorn Differentiation
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +2
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning framework…
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
Paul Roetzer, Paul Swoboda, Daniel Cremers +1
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant forma…
The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions
Dominik Muhle, Lukas Koestler, Nikolaus Demmel +2
The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar…
Neural Implicit Representations for Physical Parameter Inference from a Single Video
Florian Hofherr, Lukas Koestler, Florian Bernard +1
Neural networks have recently been used to analyze diverse physical systems and to identify the underlying dynamics. While existing methods achieve impressive results, they are lim…
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions
Tarun Yenamandra, Ayush Tewari, Nan Yang +3
Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approache…
Shortest Paths in Graphs with Matrix-Valued Edges: Concepts, Algorithm and Application to 3D Multi-Shape Analysis
Viktoria Ehm, Daniel Cremers, Florian Bernard
Finding shortest paths in a graph is relevant for numerous problems in computer vision and graphics, including image segmentation, shape matching, or the computation of geodesic di…
Convex Joint Graph Matching and Clustering via Semidefinite Relaxations
Maximilian Krahn, Florian Bernard, Vladislav Golyanik
This paper proposes a new algorithm for simultaneous graph matching and clustering. For the first time in the literature, these two problems are solved jointly and synergetically w…
RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video
Jiayi Wang, Franziska Mueller, Florian Bernard +6
Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applicati…
Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera
Franziska Mueller, Micah Davis, Florian Bernard +5
We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an exten…
Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections
Zhenzhang Ye, Tarun Yenamandra, Florian Bernard +1
Graph matching aims to establish correspondences between vertices of graphs such that both the node and edge attributes agree. Various learning-based methods were recently proposed…
Isometric Multi-Shape Matching
Maolin Gao, Zorah Lähner, Johan Thunberg +2
Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracki…
i3DMM: Deep Implicit 3D Morphable Model of Human Heads
Tarun Yenamandra, Ayush Tewari, Florian Bernard +4
We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expr…
Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation
Jiayi Wang, Franziska Mueller, Florian Bernard +1
We propose to use a model-based generative loss for training hand pose estimators on depth images based on a volumetric hand model. This additional loss allows training of a hand p…
StyleRig: Rigging StyleGAN for 3D Control over Portrait Images
Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj +5
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 paramet…