Real2Sim: Visco-elastic parameter estimation from dynamic motion

Abstract: This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures.

Authors/Presenter(s): David Hahn, ETH Zürich, Switzerland
Pol Banzet, ETH Zürich, Switzerland
James M. Bern, ETH Zürich, Switzerland
Stelian Coros, ETH Zürich, Switzerland

Video-Guided Real-to-Virtual Parameter Transfer for Viscous Fluids

Abstract: In physically-based simulation, it is essential to choose appropriate material parameters to generate desirable simulation results. In many cases, however, choosing appropriate material parameters is very challenging, and often tedious trial-and-error parameter tuning steps are inevitable. In this paper, we propose a real-to-virtual parameter transfer framework that identifies material parameters of viscous fluids with example video data captured from real-world phenomena. Our method first extracts positional data of fluids and then uses the extracted data as a reference to identify the viscosity parameters, combining forward viscous fluid simulations and parameter optimization in an iterative process. We evaluate our method with a range of synthetic and real-world example data, and demonstrate that our method can identify the hidden physical variables and viscosity parameters. This set of recovered physical variables and parameters can then be effectively used in novel scenarios to generate viscous fluid behaviors visually consistent with the example videos.

Authors/Presenter(s): Tetsuya Takahashi, University of North Carolina at Chapel Hill (UNC), United States of America
Ming Lin, University of Maryland, College Park; University of North Carolina at Chapel Hill (UNC), United States of America

Fluid Carving: Intelligent Resizing for Fluid Simulation Data

Abstract: We present a method for intelligently resizing fluid simulation data using seam carving methods. While advances in post-processing techniques have allowed artists greater control over content late in the production process, this technology has largely remained confined to image processing. Our fluid carving system allows fluid simulation post-processing by performing content-aware non-uniform scaling on baked-out fluid simulation data. Specifically, we extend video seam carving techniques to 4-dimensional animated fluid volume data with a graph cut energy function based on mean curvature and kinetic energy. To reduce the complexity of performing graph cuts on 4D data, we provide a new graph construction formulation that greatly reduces the run-time and memory consumption, which are otherwise prohibitively expensive. We demonstrate that our system is useful for post-production fluid simulation changes and editable fluid FX libraries.

Authors/Presenter(s): Sean Flynn, Brigham Young University, United States of America
Parris Egbert, Brigham Young University, United States of America
Seth Holladay, Brigham Young University, United States of America
Bryan Morse, Brigham Young University, United States of America

ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning

Abstract: In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-world smoke plumes. In addition, we propose a framework for accurate physics-based reconstructions from a small number of video streams. Central components of our framework are a novel estimation of unseen inflow regions and an efficient optimization scheme constrained by a simulation to capture real-world fluids. Our data set includes a large number of complex natural buoyancy-driven flows. The flows transition to turbulence and contain observable scalar transport processes. As such, the ScalarFlow data set is tailored towards computer graphics, vision, and learning applications. The published data set will contain volumetric reconstructions of velocity and density as well as the corresponding input image sequences with calibration data, code, and instructions how to reproduce the commodity hardware capture setup. We further demonstrate one of the many potential applications: a first perceptual evaluation study, which reveals that the complexity of the reconstructed flows would require large simulation resolutions for regular solvers in order to recreate at least parts of the natural complexity contained in the captured data.

Authors/Presenter(s): Marie-Lena Katharina Noemi Eckert, Technical University of Munich, Germany
Kiwon Um, Technical University of Munich, Germany
Nils Thuerey, Technical University of Munich, Germany