Fluid re-simulation based on physically driven model from video

Abstract

Customizing a desired naturalistic fluid simulation result from video to obtain similar artistic effect is significant in practice. But art-directed customizing is challengeable due to the chaotic nature of the physics contained in it, and this still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. This paper focuses on the problem of physically based fluid re-simulation which is foundational to customize desired naturalistic simulation result from video example. In the previous achievements, conventional algorithms primarily recover 3D geometry of fluid surface or obtain the velocity of fluid particles in video. However, to launch new derivative results, just geometry and velocity are not enough, and physically based driven models are promising. We present a novel method that is capable of efficiently recovering physically driven model from an existing video. We advocate a new approach to calculate the velocity and non-normalized surface geometry under the constraints of the appearance and dynamic behavior of the example fluid in multi-scale framework, and use the calculated physical properties quickly to recover the fluid-driven model. In particular, to calculate the surface geometry more accurately, we introduce a scale factor between normalized geometry and non-normalized geometry to acquire a more accurate result. We propose a novel recovery algorithm, in which the particle densities of lattice Boltzmann method can be recovered more accurately from matching fluid advection geometry with the calculated non-normalized geometry. Fluid re-simulations with different types of density can be achieved, including constrained particle density, auto-advection density, and enhanced particle density. We demonstrate our results in several challenging scenarios and provide qualitative evaluation to our method. Some applications based on our approach are also demonstrated in the implementation.

Enhancement in Realistic Fluid Re-simulation

“…The main idea of velocity calculation is that acquire the preliminary results from work [4] in each scale, and further refine the results using the constraint of physical principle International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2016) which we introduce the SWE. The assumption of SWE is same with the physically driven model LBMSW, thus, the velocity constrained by SWE is effective for the re-simulation when render details automatically.…”

“…Certain features of geometry variation on fluid surface provide the cues to detect the details position. The detect position is necessary to satisfy the following two conditions [4] The previous two conditions are necessary in detail positions detection. The former one is to detect the front in the wave with moving fast, and the later ensures that it is steep enough to generate the breaking or splash.…”

“…James et al [3] couple fluid simulation into flow tracking, and provide a global prior to increase the tracking accuracy and temporal coherence to ensure the realistic re-simulation. To improve practicability, Quan et al [4] use calculated physical properties to recover physically driven model to customize a desired naturalistic fluid re-simulation result. This paper applies the recovered physically driven model to re-simulate the fluid from the video.…”

“…The traditional optical flow methods [5][6] use intensity constraint to compute the velocity while Li et al [7] employ the shallow water to constrain to get physical-based velocity. For the water velocity, Quan et al [4] propose a novel scheme to recover the 2D velocity in multi-scale frame work and get more accurate velocity field. Based on the work [4], we introduce the shallow water equation (SWE) in each-scale to refine the velocity, meanwhile get more accurate surface geometry.…”

“…For the water velocity, Quan et al [4] propose a novel scheme to recover the 2D velocity in multi-scale frame work and get more accurate velocity field. Based on the work [4], we introduce the shallow water equation (SWE) in each-scale to refine the velocity, meanwhile get more accurate surface geometry. As the assumption of SWE is same with the physically driven model LBMSW, the calculated velocity is more effective for LBMSW re-simulation.…”