›› 2018, Vol. 33 ›› Issue (3): 452-462.doi: 10.1007/s11390-018-1830-7

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2018 • Previous Articles     Next Articles

Augmented Flow Simulation based on Tight Coupling between Video Reconstruction and Eulerian Models

Feng-Yu Li1, Chang-Bo Wang1,*, Member, CCF, Hong Qin2, Member, IEEE, Hong-Yan Quan1   

  1. 1 School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China;
    2 Department of Computer Science, State University of New York at Stony Brook, NY 11794-4400, U. S. A
  • Received:2018-01-08 Revised:2018-03-20 Online:2018-05-05 Published:2018-05-05
  • Contact: Chang-Bo Wang E-mail:cbwang@sei.ecnu.edu.cn
  • About author:Feng-Yu Li is a graduate student of School of Computer Science and Software Engineering, East China Normal University, Shanghai. He received his B.E. degree in communication engineering from Zhengzhou University, Zhengzhou. His research interest is fluid simulation based on physics and video-based reconstruction.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61532002, 61672237, 61672077 and 61672149, the Natural Science Foundation of USA under Grant Nos. IIS-1715985, IIS-0949467, IIS-1047715, and IIS-1049448, and the National High Technology Research and Development 863 Program of China under Grant No. 2015AA016404.

Hybrid approaches such as combining video data with pure physics-based simulation have been popular in the recent decade for computer graphics. The key motivation is to clearly retain salient advantages from both data-driven method and model-centric numerical simulation, while overcoming certain difficulties of both. The Eulerian method, which has been widely employed in flow simulation, stores variables such as velocity and density on regular Cartesian grids, so it could be associated with (volumetric) video data on the same domain. This paper proposes a novel method for flow simulation, that is tightly coupling video-based reconstruction with physically-based simulation and making use of meaningful physical attributes during re-simulation. First, we reconstruct the density field from a single-view video. Second, we estimate the velocity field using the reconstructed density field as prior. In the iterative process, the pressure projection can be treated as a physical constraint and the results of each step are corrected by obtained velocity field in the Eulerian framework. Third, we use the reconstructed density field and velocity field to guide the Eulerian simulation with anticipated new results. Through the guidance of video data, we can produce new flows that closely match with the real scene exhibited in data acquisition. Moreover, in the multigrid Eulerian simulation, we can generate new visual effects which can't be created from raw video acquisition, with a goal of easily producing many more visually interesting results and respecting true physical attributes at the same time. We demonstrate salient advantages of our hybrid method with a variety of animation examples.

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