2 citations · 6 across the 16 of their papers we have counts for
19 papers
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens
Eren Cetin, Julius Erbach, Vukasin Bozic +6
Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while still recovering what was ob…
GeoFix: Geometry Fixing for 4D Consistent Generation
Yeobin Hong, Hyojun Go, Dohun Lee +7
Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches address this through post-traini…
Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators
Armand Mihai Nicolicioiu, Dominik Narnhofer, Nando Metzger +3
High-resolution digital surface models (DSMs) play an important role in urban analysis, 3D building reconstruction, and infrastructure monitoring, yet their availability remains li…
Unified Panoramic Geometry Estimation via Multi-View Foundation Models
Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer +4
Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only fr…
Stitched Value Model for Diffusion Alignment
Hyojun Go, Hyungjin Chung, Prune Truong +8
For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challen…
Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam +3
Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require c…
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator
Hyojun Go, Dominik Narnhofer, Goutam Bhat +3
The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could…
Continuous Space-Time Video Super-Resolution with 3D Fourier Fields
Alexander Becker, Julius Erbach, Dominik Narnhofer +1
We introduce a novel formulation for continuous space-time video super-resolution. Instead of decoupling the representation of a video sequence into separate spatial and temporal c…
Solving Inverse Problems with FLAIR
Julius Erbach, Dominik Narnhofer, Andreas Dombos +3
Flow-based latent generative models such as Stable Diffusion 3 are able to generate images with remarkable quality, even enabling photorealistic text-to-image generation. Their imp…
A Variational Perspective on Generative Protein Fitness Optimization
Lea Bogensperger, Dominik Narnhofer, Ahmed Allam +2
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landsca…
Video Depth without Video Models
Bingxin Ke, Dominik Narnhofer, Shengyu Huang +5
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of lar…
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms
Lea Bogensperger, Dominik Narnhofer, Alexander Falk +2
Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effecti…
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Dominik J. Mühlematter, Michelle Halbheer, Alexander Becker +4
Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated pred…
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
Jakub Micorek, Horst Possegger, Dominik Narnhofer +2
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this…
Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini +5
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other…
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions
Lea Bogensperger, Dominik Narnhofer, Filip Ilic +1
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches all…
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
Dominik Narnhofer, Andreas Habring, Martin Holler +1
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the post…
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
Dominik Narnhofer, Alexander Effland, Erich Kobler +3
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However,…