This paper presents a unified framework to simulate surface and wave foams efficiently and realistically. The framework is designed first to project thee‐dimensional (3D) water particles from an underlying water solver onto two‐dimensional screen space to reduce the computational complexity of determining where foam particles should be generated. Because foam effects are often created primarily in fast and complicated water flows, we analyze the acceleration and curvature values to identify the areas exhibiting such flow patterns. Foam particles are emitted from the identified areas in 3D space, and each foam particle is advected according to its type, which is classified on the basis of velocity, thereby capturing the essential characteristics of foam wave motions. We improve the realism of the resulting foam by classifying it into two types: surface foam and wave foam. Wave foam is characterized by the sharp wave patterns of torrential flows, and surface foam is characterized by a cloudy foam shape, even in water with reduced motion. Based on these features, we propose a technique to correct the velocity and position of a foam particle. In addition, we propose a kernel technique using the screen space density to reduce redundant foam particles efficiently, resulting in improved overall memory efficiency without loss of visual detail in terms of foam effects. Experiments convincingly demonstrate that the proposed approach is efficient and easy to use while delivering high‐quality results.

In this paper, we propose a sprite animation synthesis technique that can efficiently represent the fire-flake effects seen in the natural phenomenon of fire. The proposed method uses the actual fire video or animated fire video as inputs and performs the following steps: 1) Extraction of feature vectors that can predict the direction of flame from image, 2) calculation of artificial buoyancy field, 3) creation and advection of fire-flake texture, 4) calculation of artificial motion blur using buoyancy flow, and 5) high quality composition. First, we detect the edges from the image and calculate the feature vectors needed to calculate the artificial buoyancy field. The computed 2D feature vectors are integrated into the Navier-Stokes equation and used to calculate the buoyancy field, which generates and advects anisotropic fire-flake textures. Finally, we apply artificial motion blur according to buoyancy direction to improve composition result of sprite animation. As a result, this method is based on image synthesis, which is faster than the existing 3D simulation-based approach. Experimental results show that high quality results can be easily and reliably obtained. In addition, since the final result is a sprite animation format, it can be easily used in existing game engines. INDEX TERMS Sprite animation, fire effects, anisotropic fire-flake texture, artificial buoyancy field. FIGURE 1. Various fire effects in animation and game (a: animation 'ZETMAN', b: mobile game 'Raising Fire Magician'.

In this paper, we present a novel method that can stably express the directional ice form caused by freezing of flowing water. The key to the proposed framework is to reflect the flow of fluids with viscosity in the direction of ice growth. Water is simulated by applying a new viscous technique to the implicit incompressible fluid simulation, and the proposed anisotropic freezing solution is used to express directional ice and glaze effects. The conditions under which water particles turn into ice particles are calculated according to a new energy function based on humidity and water flow. The humidity is approximated based on the virtual water film on the surface of the object, and the flow of fluid is incorporated into our anisotropic freezing solution to guide the growth direction of the ice. As a result, the proposed technique reliably produces glaze and directional freezing effects according to the flow direction of viscous water.

We propose an anisotropic constrained-boundary convolutional neural networks (hereafter, AnisoCBConvNet) that can stably express high-quality meshes without oscillation by applying super-resolution operations to low-resolution cloth meshes. As a training set for the neural network, we use a pair between simulation data of low resolution (LR) cloth and data obtained by applying the same simulation to high resolution (HR) cloth with increased quad mesh resolution of LR cloth. The actual data used for training are 2D geometry images converted from 3D meshes. The proposed AnisoCBConvNet is used to train an image synthesizer that converts LR geometry images to HR geometry images. In particular, by controlling the weights anisotropically near the boundary, the problem of surface wrinkling caused by oscillation is alleviated. When the HR geometry image obtained through AnisoCBConvNet is converted back to the HR cloth mesh, details including wrinkles are expressed better than the input cloth mesh. In addition, our results improved the noise problem in the existing geometry image approach. We tested AnisoCBConvNet-based super-resolution in various simulation scenarios, and confirmed stable and efficient performance in most of the results. By using our method, it will be possible to effectively produce CG VFX created using high-quality cloth simulation in games and movies.

The above article from Computer Graphics Forum, published online on 28 June 2018 in Wiley Online Library (http://wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editors Min Chen and Bedrich Benes and John Wiley & Sons Ltd. The retraction has been agreed due to both authors wishing to adhere to the high professional standard for academic publications.