Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation

This paper analyzes the simulation of special effects animation through fluid equations and data-driven methods. This paper also considers the needs of computer fluid animation simulation in terms of computational accuracy and simulation efficiency, takes high real-time, high interactivity, and high physical accuracy of simulation algorithm as the research focus and target, and proposes a solution algorithm and acceleration scheme based on deep neural network framework for the key problems of simulation of natural phenomena including smoke and liquid. With the deep development of artificial intelligence technology, deep neural network models are widely used in research fields such as computer image classification, speech recognition, and fluid detail synthesis with their powerful data learning capability. Its stable and efficient computational model provides a new problem-solving approach for computerized fluid animation simulation. In terms of time series reconstruction, this paper adopts a tracking-based reconstruction method, including target tracking, 2D trajectory fitting and repair, and 3D trajectory reconstruction. For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. The experimental results show that in terms of spatial reconstruction, the matching method proposed in this paper is more accurate compared with the traditional stereo matching algorithm; in terms of time series reconstruction, the error of target tracking reduced. Finally, the 3D motion trajectory of the point feature object and the motion pattern at a certain moment are shown, and the method in this paper obtains more ideal results, which proves the effectiveness of the method.

A Coupled Grid-Particle Method for Fluid Animation on GPU

In digital production environments, high-quality visual effects play a key role in our mobile device such as game and film. The simulation of fluid animation with free surface is an important area in computer graphic. However, the tracking of fluid surface is a challenging problem because of its instability. In this paper, a coupled grid-particle method for fluid animation surface tracking and detail preserving is proposed. Firstly, based on the nonequilibrium extrapolation method, we design a novel method for reconstructing distribution functions (DFs) of interface grids of lattice Boltzmann method (LBM) and couple the reconstruction method with LBM and volume of fluid (VOF) to track the free surface, which can obtain the accurate surface. Secondly, in order to avoid the loss of details caused by weaknesses in the traditional LBM-VOF method, we design a coupled grid-particle method that not only makes full use of the advantages of the coupled grid-particle method but also realizes the two-way coupling between grid method and particle method. Furthermore, for achieving the real-time requirements of fluid animation, we use GPU parallel computing to accelerate the simulation and use an improved screen space fluid (SSF) rendering method for realistic rendering. The various experiments show that this work can track the fluid surface with high precision and preserve the details of the fluid surface, and it also achieves good real-time performance in large-scale fluid simulation.