Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

Neural radiance fields achieve unprecedented quality for novel view synthesis, but their
volumetric formulation remains expensive, requiring a huge number of samples to render …

Lagrangian/Eulerian hybrid strand-based hair simulation techniques have quickly become a
popular approach in VFX and real-time graphics applications. With Lagrangian hair …

The proliferation of 3D representations, from explicit meshes to implicit neural fields and
more, motivates the need for simulators agnostic to representation. We present a data …

We introduce NeuralVDB, which improves on an existing industry standard for efficient
storage of sparse volumetric data, denoted VDB [Museth], by leveraging recent …

Strand-based hair simulations have recently become increasingly popular for a range of real-
time applications. However, accurately simulating the full number of hair strands remains …

Many machine/deep learning artificial neural networks are trained to simply be interpolation
functions that map input variables to output values interpolated from the training data in a …

We propose a novel solid-fluid coupling method to capture the subtle hydrophobic and
hydrophilic interactions between liquid, solid, and air at their multi-phase junctions. The key …

This paper proposes a technique for transforming low-resolution (LR) simulations of hair and
fur into high-resolution (HR) representations without noise, using strand geometry images in …

We propose a framework for simulating the complex dynamics of strands interacting with
compressible, shear-dependent liquids, such as oil paint, mud, cream, melted chocolate …