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We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition to a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters., Comment: 14 pages, 18 figures, and 3 tables

Realistic and detailed liquid splashes require costly fine-scale discretization. We present an efficient post-processing approach for particle-based methods to locally improve the behavior of splashes on coarser liquids. Our method first computes a splash volume over time from the intersections between an identified upsampling volume and colliding volumes. We then upsample particles inside cells of the splash volume; these cells are pre-computed using a likelihood score based on criteria favoring emerging particles. In addition to the advection scheme, enhanced realism is achieved by applying a localized artificial pressure on upsampled particles in order to mimic surface tension in critical regions of splashes. Finally, we propagate waves using a novel implicit model that couples the impact of upsampled particles on the coarser liquid by updating the velocity field at these locations. Our implicit wave model can produce detailed swirls by solely applying velocity updates directly on the underlying particles from the coarse liquid, and prevents from using a high density of surface points. As a result, our approach can generate localized and parameterizable high-resolution splashes from solid-liquid and liquid-liquid interactions, and thus can simulate a wide range of unique and customizable splashes on top of an animated coarse liquid.

We propose an approach for real-time shallow water simulation, building upon the virtual pipes model with multi-layered heightmaps. Our approach introduces the use of extended pipes that are capable of resolving flows through fully flooded passages, which is not possible using current multi-layered techniques. We extend the virtual pipe method with a physically-based viscosity model that is both fast and stable. Our viscosity model is integrated implicitly without the expense of solving a large linear system. Despite the few simplifications necessary to achieve a real-time viscosity model, we show that our new viscosity model produces results that match the behavior of an offline fluid-implicit particle (FLIP) simulation for various viscosity values. The liquid is rendered as a triangular mesh surface built from a heightmap. We propose a novel surface optimization approach that prevents interpenetrations of the liquid surface with the underlying terrain geometry. To improve the realism of small-scale scenarios, we present a meniscus shading approach with a view-dependent adjustment of the liquid surface normals based on a distance field. Our implementation runs in real time on various scenarios of roughly 10 × 10 cm at a resolution of 0.5 mm, with up to five layers.

This paper proposes an approach that generates hair guides from a sculpted 3D mesh, thus accelerating hair creation. Our approach relies on the local curvature on a sculpted mesh to discover the direction of the hair on the surface. We generate hair guides by following the identified strips of polygons matching hair strands. To improve the quality of the guides, some are split to ensure they correspond to hairstyles ranging from straight to wavy, while others are connected so that they correspond to longer hair strands. In order to automatically attach the guides to the scalp of a 3D head, a vector field is computed based on the directions of the guides, and is used in a backward growth of the guides toward the scalp. This approach is novel since there is no state-of-the-art method that generates hair from a sculpted mesh. Furthermore, we demonstrate how our approach works on different hair meshes. Compared to several hours of manual work to achieve a similar result, our guides are generated in a few minutes.