I am currently finishing my PhD in physics-based simulation and will start working at Weta Digital in October as Simulation Researcher. Check out my previous website at Thuerey group and have a look at my LinkedIn profile!
My research topics included the improvement of realism and control of fluid simulations. We used the convex optimization technique fast first-order Primal-Dual method [PCBC09] for several complex problems, such as reconstructing both 3D volume and motion of real-world fluid phenomena based on 2D input sequences, which is the inverse problem to forward fluid simulation. We targeted accurate multi-view reconstructions from a sparse number of cameras, which we gather in our data set ScalarFlow, as well as single-view reconstructions. Furthermore, we developed a flexible fluid guiding method and achieved separating boundary conditions for liquids with a common CG solver.
Keywords: fluid simulation, fluid capture, fluid tracking, convex optimization, numeric solvers, neural networks
I have been organizing the exercises for the lectures “Basics: Algorithms and Data Structures” and “Deep Learning and Numerical Simulations for Visual Effects” as well as the seminar “Deep Learning in Physics”. Additionally, I have been supervising Bachelor’s and Master’s Theses, see below.
How to get in Touch
- eMail: ml (at) marielenaeckert.com [or marie-lena.eckert (at) tum.de]
- Phone: +49.89.289.19476
- Fax: +49 89 289 19462
- Room: 02.13.061
also presented as two minutes papers
Marie-Lena Eckert, Kiwon Um, Nils Thuerey, “ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning“, ACM Transactions on Graphics (SIGGRAPH Asia 2019). ScalarFlow is found here.
Marie-Lena Eckert, Wolfgang Heidrich, Nils Thuerey, “Coupled Fluid Density and Motion from Single Views“, CGF Volume 37 (2018), Issue 8, p. 47-58; best paper award from SCA’18
Tiffany Inglis*, Marie-Lena Eckert*, James Gregson, Nils Thuerey, “Primal-Dual Optimization for Fluids“, CGF Volume 36 (2017), Issue 8, p. 354–368
Marie-Lena Eckert, Neslihan Kose, Jean-Luc Dugelay, “Facial cosmetics database and impact analysis on automatic face recognition“, MMSP 2013: 434-439
Marie-Lena Eckert, Andreas Freitag, Florian Matthes, Sascha Roth, Christopher Schulz, “Decision Support for Selecting an Application Landscape Integration Strategy in Mergers and Acquisitions“, ECIS 2012: 88
Marie-Lena Eckert, “Flexible Boundary Conditions in Fluid Solvers Based on Proximal Operators“, M.Sc. Thesis, Technical University of Munich, November 2014. (English)
Talks and Posters
“ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning“, conference presentation, SIGGRAPH Asia, November 2019
“Coupled Fluid Density and Motion from Single Views“, conference presentation, SCA, July 2018
“3D Reconstruction of Volume and Velocity of Real Fluid Phenomena Based on a Single Camera View“, invited talk, Prof. Matthias Teschner – Computer Graphics, Albert-Ludwigs-University Freiburg, May 2017
“Reconstructing Volume and Motion from Real Fluid Phenomena with a Minimal Number of Camera Views“, poster presentation, KAUST Research Conference: Visual Computing – Modeling and Reconstruction, April 2017
“Primal-Dual Optimization for Fluids“, conference presentation, Eurographics, April 2017
SIGGRAPH, SIGGRAPH Asia, Computer & Graphics Journal (CAG)
- Summer 2019: Basics: Algorithms and Data Structures
- Winter 2018: Seminar Deep Learning in Physics
- Summer 2018: Advanced Deep Learning for Physics
- Summer 2017: Deep Learning and Numerical Simulations for Visual Effects
- Summer 2016: Simulation for Visual Effects
- Summer 2015: Simulation for Visual Effects
“Learning to Reconstruct Smoke Volumes from Images” – Daniel Frejek, Master’s Thesis, 2019
“Improving a Low-Cost Capturing Process for Reconstructing Volume and Motion of Real Fluid Phenomena” – Daniel Frejek, Guided Research, 2017
“Capturing Real Fluid Phenomena with Raspberry Pi Cameras” – Florian Alkofer, Bachelor’s Thesis, 2017
“GPU-accelerated Stochastic Tomography for 3D Volume Reconstruction of Real Fluid Phenomena” – Tobias Kammerer, Master’s Thesis, 2017
“Optimized Volume Reconstruction for Fluids with Non-Linear Lighting Models” – Tobias Gottwald, Master’s Thesis, 2017
“Experimental Capture of Smoke and Evaluation of Volume Reconstruction Algorithms” – Florian Reichhold, Master’s Thesis, 2016
“Reconstruction of Fluid Volumes Based on Stochastic Tomography” – Dominik Dechamps, Master’s Thesis, 2016
“Modeling 3D Fluid Volumes Based on Appearance Transfer” – Christoph Pölt, Master’s Thesis, 2015
[PCBC09] POCK T., CREMERS D., BISCHOF H., CHAMBOLLE A.: An algorithm for minimizing the mumford-shah functional. In Proceedings of IEEE International Conference on Computer Vision (2009).