Daniel Cremers

Carl von Linde Senior Fellowship

Technical University of Munich

Computer Vision Group

Focus Group
Computer Vision & Machine Learning

Short CV

Daniel Cremers studied physics and mathematics at Heidelberg University, Indiana State and Stony Brook. He awarded a doctorate in computer science in 2002 at the University of Mannheim. Following this, he worked as a postdoctoral researcher at UCLA. In 2004, he joined Siemens Corporate Research (Princeton) as a member of staff. In 2005, he accepted an appointment to a professorship at the University of Bonn. Professor Cremers has been full professor of computer vision and artificial intelligence at TUM since 2009. He has served on the editorial boards of the International Journal of Computer Vision, IEEE Transactions on Pattern Recognition and Machine Intelligence (until 2014), the SIAM Journal of Imaging Sciences and the Dagstuhl Open Access Series in Informatics (OASIcs).


Selected Awards

2016 Gottfried Wilhelm Leibniz-Preis der DFG

2016 Best Paper Award, Symposium on Graphics Processing

2014 European Research Council Consolidator Grant

2009 European Research Council Starting Grant

2005 Emmy Noether Scholarship

2005 UCLA Chancellor’s Award for Postdoctoral Research

2004 Olympuspreis der Deutschen Arbeitsgemeinschaft für Mustererkennung

2003Best Paper of the Year, Int. Pattern Recognition Society


Research Interests

Daniel Cremers conducts research on computer vision, machine learning, robotics and optimization. The primary objective of this research is to improve the ability of machines to analyze and interpret image data. His research focuses on convex optimization methods, partial differential equations, graph theoretic algorithms, deep learning and statistical inference.


Selected Publications

  • Litany, O.; Rodolà, E.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.: Non-Rigid Puzzles. Computer Graphics Forum 35 (5), 2016, 135-143.
  • Strekalovskiy, Evgeny; Chambolle, Antonin; Cremers, Daniel: Convex Relaxation of Vectorial Problems with Coupled Regularization. SIAM Journal on Imaging Sciences 7 (1), 2014, 294-336.
  • Goldlücke, Bastian; Aubry, Mathieu; Kolev, Kalin; Cremers, Daniel: A Super-Resolution Framework for High-Accuracy Multiview Reconstruction. International Journal of Computer Vision 106 (2), 2013, 172-191.
  • Chambolle, Antonin; Cremers, Daniel; Pock, Thomas: A Convex Approach to Minimal Partitions. SIAM Journal on Imaging Sciences 5 (4), 2012, 1113-1158.
  • Cremers, D; Kolev, K: Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (6), 2011, 1161-1174.
  • Schoenemann, Thomas; Cremers, Daniel: A Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (7), 2010, 1153-1164.
  • Pock, Thomas; Cremers, Daniel; Bischof, Horst; Chambolle, Antonin: Global Solutions of Variational Models with Convex Regularization. SIAM Journal on Imaging Sciences 3 (4), 2010, 1122-1145.
  • Cremers, D.: Dynamical statistical shape priors for level set-based tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (8), 2006, 1262-1273.
  • Cremers, Daniel; Osher, Stanley J.; Soatto, Stefano: Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation. International Journal of Computer Vision 69 (3), 2006, 335-351.

Publications as TUM-IAS Fellow


  • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe: CVPR19 Tracking and Detection Challenge: How crowded can it get? 2019 more… BibTeX
  • Qadeer Khan, Patrick Wenzel, Daniel Cremers, Laura Leal-Taixé: Towards Generalizing Sensorimotor Control Across Weather Conditions. 2019 more… BibTeX


  • Laude, Emanuel; Lange, Jan-Hendrik; Schupfer, Jonas; Domokos, Csaba; Leal-Taixe, Laura; Schmidt, Frank R.; Andres, Bjoern; Cremers, Daniel: Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018 more… BibTeX Full text ( DOI )
  • Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation. 2018 more… BibTeX
  • S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian Reid: Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks. 2018 more… BibTeX


  • Caelles, S.; Maninis, K. -K.; Pont-Tuset, J.; Leal-Taixe, L.; Cremers, D.; Gool, L. Van: One-Shot Video Object Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017 more… BibTeX Full text ( DOI )
  • Walch, F.; Hazirbas, C.; Leal-Taixe, L.; Sattler, T.; Hilsenbeck, S.; Cremers, D.: Image-Based Localization Using LSTMs for Structured Feature Correlation. 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017 more… BibTeX Full text ( DOI )