Simulating realistic fluids in virtual reality (VR) is computationally demanding, often limiting the scale and complexity of immersive environments. Existing foveated fluid simulation approaches primarily focus on spatial adaptivity. In this paper, we introduce a gaze-contingent fluid simulation system from a temporal perspective. We conduct a perceptual study to quantify the relationship between gaze eccentricity, fluid density deviation, and the perceptual threshold for simulation timesteps. Based on these findings, we fit a perceptual model that predicts the timestep requirements for maintaining perceptual realism in VR fluid animation. To exploit this model, we propose an asynchronous position-based fluids (PBF) algorithm that assigns fluid particles different local timesteps according to their visual importance and density deviation, ensuring both physical stability and perceptual validity. Our solver performs high-frequency updates in perceptually critical regions while progressively reducing updates elsewhere. A validation user study shows that our method remains perceptually indistinguishable from a high-fidelity, uniform-timestep PBF simulation. Objective evaluations further confirm that our approach improves efficiency while maintaining perceptual quality. Runtime experiments demonstrate speed-ups of up to 1.52× across diverse fluid scenarios, enabling more complex and larger-scale fluid phenomena in real-time VR. Our findings extend the paradigm of foveated fluid animation into the temporal domain, providing a perceptually grounded framework for fluid simulation in VR.

Physically-based fluid animation in Virtual Reality (VR) significantly enhances the user experience through visually engaging flow motions. Nonetheless, such simulations are often limited by their substantial computational demands. A tailored adaptive simulation algorithm is important for high-performance VR fluid simulations, which dynamically allocate degrees of freedom (DoF) while accounting for user perception in VR. This paper proposes a novel scene-based gaze-contingent fluid simulation system for VR, featuring a highly adaptive fluid simulator integrated with a VR perceptual model that accounts for the foveation and geometry of fluid. Our method leverages an eccentricity and curvature-dependent perceptual model to dynamically allocate computational resources, improving the efficiency and maintaining spatio-temporal stability of fluid animation in VR. A user study was conducted to measure the simulation resolution thresholds for fluid animations in VR, considering various levels of eccentricity and curvature. Our findings indicate notable differences in perceptual thresholds based on these metrics. By incorporating these insights into our adaptive fluid simulator as a unified sizing function, we maintain perceptually optimal particle resolution, achieving up to a 3.62× performance improvement while delivering superior perceptual realism and user experience, as validated by a subjective evaluation study.

Large-scale fluid simulation is widely useful in various Virtual Reality (VR) applications. While physics-based fluid animation holds the promise of generating highly realistic fluid details, it often imposes significant computational demands, particularly when simulating high-resolution fluid for VR. In this paper, we propose a novel foveated fluid simulation method that enhances both the visual quality and computational efficiency of physics-based fluid simulation in VR. To leverage the natural foveation feature of human vision, we divide the visible domain of the fluid simulation into foveal, peripheral, and boundary regions. Our foveated fluid system dynamically allocates computational resources, striking a balance between simulation accuracy and computational efficiency. We implement this approach using a multi-scale method. To evaluate the effectiveness of our approach, we have conducted subjective studies. Our findings show a significant reduction in computational resource requirements, resulting in a speedup of up to 2.27 times. It is crucial to note that our method preserves the visual quality of fluid animations at a level that is perceptually identical to full-resolution outcomes. Additionally, we investigate the impact of various metrics, including particle radius and viewing distance, on the visual effects of fluid animations. Our work provides new techniques and evaluations tailored to facilitate real-time foveated fluid simulation in VR, which can enhance the efficiency and realism of fluids in VR applications.

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 physicalbased 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 datadriven 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.