“Interpolations of Smoke and Liquid Simulations” by Thuerey

  • ©Nils Thuerey

Conference:


Type:


Title:

    Interpolations of Smoke and Liquid Simulations

Session/Category Title: Fluid Control & Synthesis


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    We present a novel method to interpolate smoke and liquid simulations in order to perform data-driven fluid simulations. Our approach calculates a dense space-time deformation using grid-based signed-distance functions of the inputs.

    A key advantage of this implicit Eulerian representation is that it allows us to use powerful techniques from the optical flow area. We employ a five-dimensional optical flow solve. In combination with a projection algorithm, and residual iterations, we achieve a robust matching of the inputs. Once the match is computed, arbitrary in-between variants can be created very efficiently. To concatenate multiple long-range deformations, we propose a novel alignment technique.

    Our approach has numerous advantages, including automatic matches without user input, volumetric deformations that can be applied to details around the surface, and the inherent handling of topology changes. As a result, we can interpolate swirling smoke clouds, and splashing liquid simulations. We can even match and interpolate phenomena with fundamentally different physics: a drop of liquid, and a blob of heavy smoke.

References:


    1. Morten Bojsen-Hansen, Hao Li, and Chris Wojtan. 2012. Tracking surfaces with evolving topology. ACM Transactions on Graphics 31, 4, Article 53 (July 2012), 10 pages. 
    2. Robert Bridson. 2016. Fluid Simulation for Computer Graphics. AK Peters/CRC Press. 
    3. Paolo Cignoni, Claudio Rocchini, and Roberto Scopigno. 1996. Metro: Measuring Error on Simplified Surfaces. Technical Report. Paris, France. 
    4. Daniel Cremers and Stefano Soatto. 2003. A pseudo-distance for shape priors in level set segmentation. In Proceedings of the 2nd IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision. 169–176.
    5. Jan Ehrhardt, René Werner, Dennis Säring, Thorsten Frenzel, Wei Lu, Daniel Low, and Heinz Handels. 2007. An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. Medical Physics 34, 2 (2007), 711–721. 
    6. Nick Foster and Dimitri Metaxas. 1996. Realistic animation of liquids. Graphical Models and Image Processing 58, 5 (Sept. 1996), 471–483. DOI:http://dx.doi.org/10.1006/gmip.1996.0039 
    7. James Gregson, Nils Thuerey, Ivo Ihrke, and Wolfgang Heidrich. 2014. From capture to simulation—Connecting forward and inverse problems in fluids. ACM Transactions on Graphics 33 (4) (August 2014), 10. 
    8. Berthold Horn and Brian Schunck. 1981. Determining optical flow. Artificial Intelligence 17, 1 (1981), 185–203. 
    9. Markus Ihmsen, Jens Orthmann, Barbara Solenthaler, Andreas Kolb, and Matthias Teschner. 2014. SPH fluids in computer graphics. In Eurographics—State of the Art Reports. 21–42.
    10. Won-Ki Jeong and Ross T. Whitaker. 2008. A fast iterative method for eikonal equations. SIAM Journal of Scientific Computing 30, 5 (2008), 2512–2534. 
    11. Theodore Kim and John Delaney. 2013. Subspace fluid re-simulation. ACM Transactions on Graphics 32, 4, Article 62 (July 2013), 9 pages. 
    12. Theodore Kim, Nils Thürey, Doug James, and Markus Gross. 2008. Wavelet turbulence for fluid simulation. ACM Transactions on Graphics 27, 3, Article 50 (August 2008), 1–6. 
    13. Lubor Ladicky, SoHyeon Jeong, Barbara Solenthaler, Marc Pollefeys, Markus Gross, and others. 2015. Data-driven fluid simulations using regression forests. ACM Transactions on Graphics (TOG) 34, 6 (2015), 199. 
    14. Michael Lentine, Mridul Aanjaneya, and Ronald Fedkiw. 2011. Mass and momentum conservation for fluid simulation. In Proceedings of Symposium on Computer Animation. ACM, 91–100. 
    15. Yangyan Li, Xiaochen Fan, Niloy J. Mitra, Daniel Chamovitz, Daniel Cohen-Or, and Baoquan Chen. 2013. Analyzing growing plants from 4D point cloud data. ACM Transactions on Graphics 32, 6, Article 157 (Nov. 2013), 10 pages. 
    16. Enric Meinhardt-Llopis, Javier Sánchez Pérez, and Daniel Kondermann. 2013. Horn-Schunck optical flow with a multi-scale strategy. Image Processing On-Line 2013 (2013), 151–172. 
    17. M. Müller, D. Charypar, and M. Gross. 2003. Particle-based fluid simulation for interactive applications. In Proceedings of Symposium on Computer Animation. 154–159. 
    18. Michael B. Nielsen, Andreas Söderström, and Robert Bridson. 2013. Synthesizing waves from animated height fields. ACM Transactions on Graphics 32, 1, Article 2 (Feb. 2013), 9 pages. 
    19. Zherong Pan, Jin Huang, Yiying Tong, Changxi Zheng, and Hujun Bao. 2013. Interactive localized liquid motion editing. ACM Transactions on Graphics (SIGGRAPH Asia 2013) 32, 6 (Nov. 2013). 
    20. Nikos Paragios, Mikael Rousson, and Visvanathan Ramesh. 2003. Non-rigid registration using distance functions. Computer Vision and Image Understanding 89, 2 (2003), 142–165. 
    21. Tiberiu Popa, Ian South-Dickinson, Derek Bradley, Alla Sheffer, and Wolfgang Heidrich. 2010. Globally consistent space-time reconstruction. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 1633–1642. 
    22. Karthik Raveendran, Nils Thuerey, Chris Wojtan, and Greg Turk. 2014. Blending liquids. ACM Transactions on Graphics 33, 4 (August 2014), 10. 
    23. Andrew Selle, Ronald Fedkiw, Byungmoon Kim, Yingjie Liu, and Jarek Rossignac. 2008. An unconditionally stable MacCormack method. Journal of Scientific Computing 35, 2–3 (June 2008), 350–371. 
    24. Andrei Sharf, Dan A. Alcantara, Thomas Lewiner, Chen Greif, Alla Sheffer, Nina Amenta, and Daniel Cohen-Or. 2008. Space-time surface reconstruction using incompressible flow. ACM Transactions on Graphics 27, 5, Article 110 (Dec. 2008), 10 pages. 
    25. Lin Shi and Yizhou Yu. 2005. Taming liquids for rapidly changing targets. In Proceedings of Symposium on Computer Animation. 229–236. 
    26. Jos Stam. 1999. Stable fluids. In Proceedings of ACM SIGGRAPH. 121–128. 
    27. Jos Stam and Ryan Schmidt. 2011. On the velocity of an implicit surface. ACM Transactions on Graphics 30, 3 (2011), 7. 
    28. Matt Stanton, Ben Humberston, Brandon Kase, James O’Brien, Kayvon Fatahalian, and Adrien Treuille. 2014. Self-refining games using player analytics. ACM Transactions on Graphics 33, 4 (2014), 9. 
    29. Deqing Sun, Stefan Roth, and Michael J. Black. 2014. A quantitative analysis of current practices in optical flow estimation and the principles behind them. In International J ournal of Computer Vision 106, 2 (Jan. 2014), 115–137. 
    30. Nils Thuerey, Richard Keiser, Ulrich Ruede, and Mark Pauly. 2006. Detail-preserving fluid control. In Proceedings of Symposium on Computer Animation. 7–12. 
    31. Nils Thuerey and Tobias Pfaff. 2016. MantaFlow. (2016). http://mantaflow.com.
    32. Adrien Treuille, Andrew Lewis, and Zoran Popović. 2006. Model reduction for real-time fluids. ACM Transactions on Graphics 25, 3 (July 2006), 826–834. 
    33. Sundar Vedula, Simon Baker, Peter Rander, Robert Collins, and Takeo Kanade. 1999. Three-dimensional scene flow. In Proceedings of Computer Vision, Vol. 2. IEEE, 722–729. 
    34. Andreas Wedel and Daniel Cremers. 2011. Stereoscopic Scene Flow for 3D Motion Analysis. Springer. 
    35. Zexiang Xu, Hsiang-Tao Wu, Lvdi Wang, Changxi Zheng, Xin Tong, and Yue Qi. 2014. Dynamic hair capture using spacetime optimization. ACM Transactions on Graphics 33, 6, Article 224 (Nov. 2014), 11 pages. 
    36. C. Zach, T. Pock, and H. Bischof. 2007. A duality based approach for realtime TV-L1 optical flow. In Proceedings of the 29th DAGM Conference on Pattern Recognition. 214–223. 
    37. Yongning Zhu and Robert Bridson. 2005. Animating sand as a fluid. ACM Transactions on Graphics 24, 3 (2005), 965–972. 
    38. Darko Zikic, Ali Kamen, and Nassir Navab. 2010. Revisiting Horn and Schunck: Interpretation as Gauss-Newton optimisation. In Proceedings of the British Machine Vision Conference. 1–12.

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