Position-based fluid control

We present an interactive user interface to support sketch‐based fluid design with a perceptual understanding of human sketches. In particular, the proposed system generates a 2D fluid animation from hand‐drawn sketches. The proposed system utilizes a conditional generative adversarial network model to generate stationary velocity fields from a sketch input. The network model is trained with hand‐drawn strokes and corresponding 2D velocity fields. On the basis of the generated velocity field, the system calculates fluid dynamics using a semi‐Lagrangian method. We ran a user study of the proposed system and confirmed that the proposed interface is effective for a 2D fluid design and that the system achieves good results based on user input.

Section: Related Workmentioning

confidence: 99%

Sketch2VF: Sketch‐based flow design with conditional generative adversarial network

Xie

Fukusato

et al. 2019

“…However, the grid‐based fluid control based is relatively computational complex, which is restricted from fixed simulation domain. There are also some control methods on particle‐based fluid such as in other works –. Thürey et al used the control force for coarse control, that is, the control force attracts the SPH particles to control the fluid to fill the control target or to flow along the specified path.…”

Section: Related Workmentioning

confidence: 99%

“…Comparison of the control results for a fast‐moving fluid ball among different methods: (a) without spring force, (b) with spring force from the work of Zhang et al, and (c) with our method. (a) Many SPH particles fly out of the ball shape when the control particles of ball move in the pool.…”

Section: Our Algorithmmentioning

confidence: 99%

Parallel SPH fluid control with dynamic details

Zhang

Liu

2018

Real‐time fluid control is indispensable in computer animation, games, virtual reality, etc. In the field of Smoothed Particle Hydrodynamics (SPH) fluid control, the strategy of control force is often employed to control fluid particles; however, the artificial viscosity introduced by the control force would frequently lead to the loss of fine‐scale details. Although the introduction of the low‐pass filter can add back details, it may easily destroy the control target, and the control force method itself cannot make SPH fluid follow the fast‐moving control target. Meanwhile, this type of method is computation intensive and time consuming. To remedy the above problems, this paper proposed a novel, interactive SPH fluid control framework with turbulent details. We run SPH fluid simulation on Compute Unified Device Architecture (CUDA) and greatly improve the efficiency of fluid control. The control particle with curvature framework was adapted in this paper. We specially designed spring forces to make the fluid match a fast‐moving control target. Moreover, fine fluid details were preserved separately by calculating the fluid turbulence under control and the free fluid turbulence. This improved SPH fluid control can run in real time, which can enhance the visual quality of fluid animation as well. Our novel method can be applied in fluid animation with special control effects to guide fluid to form a target shape while greatly preserving the dynamic details of fluid. Various experiment results demonstrated the ability of our novel method.

“…Fluid particles can match the special target shape by the effect of control force. In this paper, the control force includes coarse control force, spring constraint force, and velocity constraint force . The coarse control force is used to attract fluid particles to match the target shape.…”

Section: Algorithmmentioning

confidence: 99%

“…In this paper, the control force includes coarse control force, 2 spring constraint force, 23 and velocity constraint force. 2,23 The coarse control force is used to attract fluid particles to match the target shape. The spring constraint force takes effect only when the fluid particle is far from its corresponding control particle to avoid introducing artificial viscosity.…”

Section: Detail Preserving Via Control Force Transfer 331 Control Fmentioning

confidence: 99%

Detail‐preserving SPH fluid control with deformation constraints

Feng

Liu

2017

It is challenging to drive particle-based smoothed-particle hydrodynamics fluid to match the target shape and the deforming fluid shape between different models smoothly, especially when the natural fluid motion must be preserved. To achieve the desired behavior, we first generate control particles by sampling the target shapes and then apply a deformation constraint to each control particle, with its neighboring fluid particles keeping details within its influence region. For the generation of control particles, we classify input models into source object and target object, then separately sample them by voxelization method, and generate source control particles and target control particles, respectively. Our deformation constraint includes two parts. In the first part, we deform the source control model to the target control model according to specific space point correspondence between source control particles and target control particles; then, fluid particles are attracted by control particles and complete deformation between different shapes. In the second part, to reduce the lacking of fluid details when fluid deforms, we introduce a new control energy transfer mechanism for control particles. This deformation constraint is solved under smoothed-particle hydrodynamics-based fluid simulation framework, which makes our simulation fast, robust, and well suitable for interactive applications. Various experiments demonstrated the effectiveness of our method.