My current research topic is reconstructing both 3D volume and 3D motion of real fluid phenomena based on only a single 2D input video which is the inverse problem to fluid simulation. Previously, I was working on separating boundary conditions for liquids based on convex optimization methods. Have a look at my LinkedIn profile!

Keywords: fluid simulation, fluid capture, fluid tracking, convex optimization, numeric solvers, neural networks

I am organizing the exercises for the lecture “Deep Learning and Numerical Simulations for Visual Effects”, the seminar “Deep Learning in Physics” and I have been supervising Bachelor’s and Master’s Theses, see below.

How to get in Touch

  • eMail: marie-lena.eckert (at)
  • Phone: +
  • Fax: +49 89 289 19462
  • Room: 02.13.061

also presented as two minutes papers


    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

Coupled Fluid Density and Motion from Single Views“, conference talk, 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 talk, Eurographics, April 2017


Computer & Graphics Journal (CAG)


Supervised Theses

Learning to Reconstruct Smoke Volumes from Images” – Daniel Frejek, Master’s Thesis, 2018

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