3 citations · 3 across the 6 of their papers we have counts for
9 papers
Brain2Face: A Study on the Relation of EEG Signals and Spatio-temporal Head Avatars
Camilo Andrés Martínez Martínez, Olaf Dünkel, Kartik Teotia +3
Driving digital head avatars is classically done by hand-crafted artist design or by image-based facial performance capture. In this work, we ask whether spatio-temporal tracking o…
Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence
Artur Jesslen, Olaf Dünkel, Adam Kortylewski
Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation. However, because these featu…
CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models
León Begiristain, Olaf Dünkel, Adam Kortylewski
Video prediction is increasingly viewed as a path toward generalizable world models, yet it remains unclear whether these systems learn underlying causal structure or merely exploi…
Attention (as Discrete-Time Markov) Chains
Yotam Erel, Olaf Dünkel, Rishabh Dabral +3
We introduce a new interpretation of the attention matrix as a discrete-time Markov chain. Our interpretation sheds light on common operations involving attention scores such as se…
CNS-Bench: Benchmarking Image Classifier Robustness Under Continuous Nuisance Shifts
Olaf Dünkel, Artur Jesslen, Jiahao Xie +3
An important challenge when using computer vision models in the real world is to evaluate their performance in potential out-of-distribution (OOD) scenarios. While simple synthetic…
Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels
Olaf Dünkel, Thomas Wimmer, Christian Theobalt +2
Finding correspondences between semantically similar points across images and object instances is one of the everlasting challenges in computer vision. While large pre-trained visi…
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space
Leonhard Sommer, Olaf Dünkel, Christian Theobalt +1
3D morphable models (3DMMs) are a powerful tool to represent the possible shapes and appearances of an object category. Given a single test image, 3DMMs can be used to solve variou…
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
Olaf Dünkel, Tim Salzmann, Florian Pfaff
Normalizing flows have proven their efficacy for density estimation in Euclidean space, but their application to rotational representations, crucial in various domains such as robo…
Sample-Specific Output Constraints for Neural Networks
Mathis Brosowsky, Olaf Dünkel, Daniel Slieter +1
Neural networks reach state-of-the-art performance in a variety of learning tasks. However, a lack of understanding the decision making process yields to an appearance as black box…