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Virtual Reality & Intelligent Hardware

Virtual Reality & Intelligent Hardware

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2021 Vol. 3 No. 2 Previous

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Editorial

Simulation and interaction of fluid and solid dynamics

DOI:10.3724/SP.J.2096-5796.2021.03.02

2021, 3(2) : 1-2

PDF (8) | HTML (17)

Review

Data-driven simulation in fluids animation: A survey

DOI:10.1016/j.vrih.2021.02.002

2021, 3(2) : 87-104

Abstract (33) | PDF (3) | HTML (16)
The field of fluid simulation is developing rapidly, and data-driven methods provide many frameworks and techniques for fluid simulation. This paper presents a survey of data-driven methods used in fluid simulation in computer graphics in recent years. First, we provide a brief introduction of physical-based fluid simulation methods based on their spatial discretization, including Lagrangian, Eulerian, and hybrid methods. The characteristics of these underlying structures and their inherent connection with data-driven methodologies are then analyzed. Subsequently, we review studies pertaining to a wide range of applications, including data-driven solvers, detail enhancement, animation synthesis, fluid control, and differentiable simulation. Finally, we discuss some related issues and potential directions in data-driven fluid simulation. We conclude that the fluid simulation combined with data-driven methods has some advantages, such as higher simulation efficiency, rich details and different pattern styles, compared with traditional methods under the same parameters. It can be seen that the data-driven fluid simulation is feasible and has broad prospects.

Article

Affine particle-in-cell method for two-phase liquid simulation

DOI:10.1016/j.vrih.2020.12.003

2021, 3(2) : 105-117

Abstract (25) | PDF (3) | HTML (14)
Background
The interaction of gas and liquid can produce many interesting phenomena, such as bubbles rising from the bottom of the liquid. The simulation of two-phase fluids is a challenging topic in computer graphics. To animate the interaction of a gas and liquid, MultiFLIP samples the two types of particles, and a Euler grid is used to track the interface of the liquid and gas. However, MultiFLIP uses the fluid implicit particle (FLIP) method to interpolate the velocities of particles into the Euler grid, which suffer from additional noise and instability.
Methods
To solve the problem caused by fluid implicit particles (FLIP), we present a novel velocity transport technique for two individual particles based on the affine particle-in-cell (APIC) method. First, we design a weighed coupling method for interpolating the velocities of liquid and gas particles to the Euler grid such that we can apply the APIC method to the simulation of a two-phase fluid. Second, we introduce a narrowband method to our system because MultiFLIP is a time-consuming approach owing to the large number of particles.
Results
Experiments show that our method is well integrated with the APIC method and provides a visually credible two-phase fluid animation.
Conclusions
The proposed method can successfully handle the simulation of a two-phase fluid.
Helmholtz decomposition-based SPH

DOI:10.1016/j.vrih.2021.01.003

2021, 3(2) : 118-128

Abstract (24) | PDF (1) | HTML (12)
Background
SPH method has been widely used in the simulation of water scenes. As a numerical method of partial differential equations, SPH can easily deal with the distorted and complex boundary. In addition, the implementation of SPH is relatively simple, and the results are stable and not easy to diverge. However, SPH method also has its own limitations. In order to further improve the performance of SPH method and expand its application scope, a series of key and difficult problems restricting the development of SPH need to be improved.
Methods
In this paper, we introduce the idea of Helmholtz decomposition into the framework of smoothed particle hydrodynamics (SPH) and propose a novel velocity projection scheme for three-dimensional water simulation. First, we apply Helmholtz decomposition to a three-dimensional velocity field and decompose it into three orthogonal subspaces. Then, our method combines the idea of spatial derivatives in SPH to obtain a discrete Poisson velocity equation. Finally, the conjugate gradient (CG) is utilized to efficiently solve the Poisson equation.
Results
The experimental results show that the proposed scheme is suitable for various situations and has higher efficiency than the current SPH projection scheme.
Conclusion
Compared with the previous projection scheme, our solution does not need to modify the particle velocity indirectly by pressure projection, but directly by velocity field projection. The new scheme can be well integrated into the existing SPH framework, and can be applied to the interaction of water with static and dynamic obstacles, even for viscous fluid.
Adaptive smoothing length method based on weighted average of neighboring particle density for SPH fluid simulation

DOI:10.1016/j.vrih.2018.12.001

2021, 3(2) : 129-141

Abstract (42) | PDF (7) | HTML (18)
Background
In the smoothed particle hydrodynamics (SPH) fluid simulation method, the smoothing length affects not only the process of neighbor search but also the calculation accuracy of the pressure solver. Therefore, it plays a crucial role in ensuring the accuracy and stability of SPH.
Methods
In this study, an adaptive SPH fluid simulation method with a variable smoothing length is designed. In this method, the smoothing length is adaptively adjusted according to the ratio of the particle density to the weighted average of the density of the neighboring particles. Additionally, a neighbor search scheme and kernel function scheme are designed to solve the asymmetry problems caused by the variable smoothing length.
Results
The simulation efficiency of the proposed algorithm is comparable to that of some classical methods, and the variance of the number of neighboring particles is reduced. Thus, the visual effect is more similar to the corresponding physical reality.
Conclusions
The precision of the interpolation calculation performed in the SPH algorithm is improved using the adaptive-smoothing length scheme; thus, the stability of the algorithm is enhanced, and a larger timestep is possible.
Stains on imperfect textile

DOI:10.1016/j.vrih.2021.01.001

2021, 3(2) : 142-155

Abstract (22) | PDF (2) | HTML (17)
The imperfect material effect is one of the most important themes to obtain photo-realistic results in rendering. Textile material rendering has always been a key area in the field of computer graphics. So far, a great deal of effort has been invested in its unique appearance and physics-based simulation. The appearance of the dyeing effect commonly found in textiles has received little attention. This paper introduces techniques for simulation of staining effects on textiles. Pulling, wearing, squeezing, tearing, and breaking effects are more common imperfect effects of fabrics, these external forces will cause changes in the fabric structure, thus affecting the diffusion effect of stains. Based on the microstructure of yarn, we handle the effect of the stain on the imperfect textile surface. Our simulation results can achieve a photo-realistic effect.
A homogenization method for nonlinear inhomogeneous elastic materials

DOI:10.1016/j.vrih.2021.01.002

2021, 3(2) : 156-170

Abstract (24) | PDF (2) | HTML (19)
Background
Fast simulation techniques are strongly favored in computer graphics, especially for the nonlinear inhomogeneous elastic materials. The homogenization theory is a perfect match to simulate inhomogeneous deformable objects with its coarse discretization, as it reveals how to extract information at a fine scale and to perform efficient computation with much less DOF. The existing homogenization method is not applicable for ubiquitous nonlinear materials with the limited input deformation displacements.
Methods
In this paper, we have proposed a homogenization method for the efficient simulation of nonlinear inhomogeneous elastic materials. Our approach allows for a faithful approximation of fine, heterogeneous nonlinear materials with very coarse discretization. Modal analysis provides the basis of a linear deformation space and modal derivatives extend the space to a nonlinear regime; based on this, we exploited modal derivatives as the input characteristic deformations for homogenization. We also present a simple elastic material model that is nonlinear and anisotropic to represent the homogenized materials. The nonlinearity of material deformations can be represented properly with this model. The material properties for the coarsened model were solved via a constrained optimization that minimizes the weighted sum of the strain energy deviations for all input deformation modes. An arbitrary number of bases can be used as inputs for homogenization, and greater weights are placed on the more important low-frequency modes.
Results
Based on the experimental results, this study illustrates that the homogenized material properties obtained from our method approximate the original nonlinear material behavior much better than the existing homogenization method with linear displacements, and saves orders of magnitude of computational time.
Conclusions
The proposed homogenization method for nonlinear inhomogeneous elastic materials is capable of capturing the nonlinear dynamics of the original dynamical system well.
Cumulus cloud modeling from images based on VAE-GAN

DOI:10.1016/j.vrih.2020.12.004

2021, 3(2) : 171-181

Abstract (19) | PDF (3) | HTML (16)
Background
Cumulus clouds are important elements in creating virtual outdoor scenes. Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud. Image-based modeling is an efficient method to solve this problem. Because of the complexity of cloud shapes, the task of modeling the cloud from a single image remains in the development phase.
Methods
In this study, a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image. The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network. First, a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder. Then, the parameters of the decoder are fixed. A shape reconstruction network is proposed for use instead of the encoder part, and it is trained with rendered images. To train the presented models, we constructed a 3D cumulus dataset that included 200 3D cumulus models. These cumulus clouds were rendered under different lighting parameters.
Results
The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches. Furthermore, some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.
Conclusion
The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes. The presented reconstruction architecture models a cloud from a single image. Experiments demonstrated the effectiveness of the two models.