I am an independent research group leader (Image & Video Analysis) at the Technical University of Darmstadt, as well as affiliated with the Hessian Center for Artificial Intelligence (hessian.AI). I recently got the renowned Emmy Noether Programme (ENP) fund of the German Research Foundation (DFG) supporting my research group. The focus of my research is on developing efficient, robust, and understandable methods and algorithms for image and video analysis. Before starting my own group, I was a postdoctoral researcher in the Visual Inference Lab of Prof. Stefan Roth. Prior to joining TU Darmstadt, I was a postdoctoral researcher at the Media Technology Center at ETH Zurich working on augmented reality. I obtained my doctoral degree from ETH Zurich, advised by Prof. Dr. Markus Gross and in collaboration with Disney Research Zurich. In my doctoral thesis, awarded with the ETH Medal, I developed novel methods for motion representation and video frame interpolation.

I also head the Efficient Video Analysis (EVA) project group at hessian.AI, researching novel AI methods for visual data with a focus on developing efficient, robust, and controllable methods and algorithms that can learn from limited data by exploiting low-level perception and high-level reasoning. EVA has been created as a DEPTH research group within the HMWK-funded cluster project “The Third Wave of Artificial Intelligence (3AI)”. The cluster project is embedded into hessian.AI, the Hessian Centre for Artificial Intelligence, with its mission to “understand the interplay of AI algorithms, AI systems, and synergies between artificial and natural intelligence to provide the foundation for AI transformation”.



FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods

R. Hesse, S. Schaub-Meyer, S. Roth

ICCV 2023   (Oral Presentation)
[ Paper | Supplement | Talk video | Code ]

Entropy-driven Unsupervised Keypoint Representation Learning in Videos

A. Younes, S. Schaub-Meyer, G. Chalvatzaki

ICML 2023
[ Paper | Project page | Code ]

Content-Adaptive Downsampling in Convolutional Neural Networks

R. Hesse, S. Schaub-Meyer, S. Roth

CVPRW 2023 - Efficient Deep Learning for Computer Vision CVPR Workshop 2023
[ Paper | Supplement | Talk video | Poster | Code ]

Efficient Feature Extraction for High-resolution Video Frame Interpolation

M. Nottebaum, S. Roth, S. Schaub-Meyer

BMVC 2022
[ Paper | Supplement | Video | Talk video | Poster | Code ]

$S^2$-Flow: Joint Semantic and Style Editing of Facial Images

K. Singh, S. Schaub-Meyer, S. Roth

BMVC 2022
[ Paper | Supplement | Talk video | Poster | Code ]

Fast Axiomatic Attribution for Neural Networks

R. Hesse, S. Schaub-Meyer, S. Roth

NeurIPS 2021
[ Paper | Supplement | Talk video | Project page | Code ]

Dense Unsupervised Learning for Video Segmentation

N. Araslanov, S. Schaub-Meyer, S. Roth

NeurIPS 2021
[ Paper | Supplement | Video | Talk video | Code ]

Style Transfer for Keypoint Matching Under Adverse Conditions

A. Uzpak, A. Djelouah, S. Schaub-Meyer

3DV 2020
[ Paper | Code ]

Neural inter-frame compression for video coding

A. Djelouah, J. Campos, S. Schaub-Meyer, C. Schroers

ICCV 2019
[ Paper | Supplement ]

Deep Video Color Propagation

S. Meyer, V. Cornillère, A. Djelouah, C. Schroers, M. Gross

BMVC 2018
[ Preprint (arXiv) | Paper | Supplement | Video ]

PhaseNet for Video Frame Interpolation

S. Meyer, A. Djelouah, B. McWilliams, A. Sorkine-Hornung, M. Gross, C. Schroers

CVPR 2018
[ Paper | Supplement | Video | BibTex ]

Phase-Based Modification Transfer for Video

S. Meyer, A. Sorkine-Hornung, M. Gross

ECCV 2016   (Oral Presentation)
[ Paper | Video ]

Phase-based Frame Interpolation for Video

S. Meyer, O. Wang, H. Zimmer, M. Grosse, A. Sorkine-Hornung

CVPR 2015   (Oral Presentation)
[ Paper | Supplement | Video | Code ]