*ACM Transactions on Graphics (SIGGRAPH), 2024*

Numerous scientific and engineering applications require solutions to boundary value problems (BVPs) involving elliptic partial differential equations, such as the Laplace or Poisson equations, on geometrically intricate domains. We develop a Monte …

*ACM Transactions on Graphics (ToG), 2024*

We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the …

*ACM Transactions on Graphics (ToG), 2024*

Monte Carlo rendering algorithms often utilize correlations between pixels to improve efficiency and enhance image quality. For real-time applications in particular, repeated reservoir resampling offers a powerful framework to reuse samples both …

*ArXiv Preprint, 2023*

We present X3 (pronounced XCube), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to 10243 in a feed-forward fashion …

*SIGGRAPH Asia (Conference Track), 2023*

Motion control of large-scale, multibody physics animations with contact is difficult. Existing approaches, such as those based on optimization, are computationally daunting, and, as the number of interacting objects increases, can fail to find …

*SIGGRAPH Asia (Conference Track), 2023*

Real-time elastodynamic solvers are well-suited for the rapid simulation of ho mogeneous elastic materials, with high-rates generally enabled by aggressive early termination of timestep solves. Unfortunately, the introduction of strong domain …

*SIGGRAPH Asia (Conference Track), 2023*

We present neural collision fields as an alternative to contact point sampling in physics simulations. Our approach is built on top of a novel smoothed integral formulation for the contact surface patches between two triangle meshes. By reformulating …

*ACM Transactions on Graphics (SIGGRAPH), 2023*

Grid-free Monte Carlo methods based on the walk on spheres (WoS) algorithm solve fundamental partial differential equations (PDEs) like the Poisson equation without discretizing the problem domain or approximating functions in a finite basis. Such …

*ACM SIGGRAPH 2023 Talks, 2023*

We present a volumetric, simulation-based pipeline for the automatic creation of strain-based descriptors from facial performance capture provided as surface meshes. Strain descriptors encode facial poses via length elongation/contraction ratios of …

*SIGGRAPH (Conference Track), 2023*

We devise a local global solver dedicated to the simulation of Discrete Elastic Rods (DER) with Coulomb friction that can fully leverage the massively parallel compute capabilities of moderns GPUs. We verify that our simulator can reproduce …

*ACM Transactions on Graphics (SIGGRAPH), 2023*

Grid-free Monte Carlo methods such as walk on spheres can be used to solve elliptic partial differential equations without mesh generation or global solves. However, such methods independently estimate the solution at every point, and hence do not …

*ArXiv Preprint, 2023*

We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation to a level of detail that closely approximates that …

*ACM SIGGRAPH 2022 Talks, 2022*

🏆 **AIS Lumiere Tech Award**

In visual effects for film, replacement of stunt performers’ facial likeness for their doubled actor counterparts using traditional computer graphics methods is a multi-stage, labor intensive task. Recently, deep learning techniques have made a …

*ArXiv Preprint, 2022*

At a fundamental level most physical equations are time reversible. In this paper we propose an integrator that preserves this property at the discrete computational level. Our simulations can be run forward and backwards and trace the same path …

*ACM SIGGRAPH 2021 Talks, 2021*

We introduce a sparse volumetric data structure, dubbed NanoVDB, which is portable to both C++11 and C99 as well as most graphics APIs, e.g. CUDA, OpenCL, OpenGL, WebGL, DirectX 12, OptiX, HLSL, and GLSL. As indicated by its name, NanoVDB is a …

*Advances in Neural Information Processing Systems (NeurIPS), 2020*

In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exotic (non-Euclidean) Riemannian manifolds such as hyperbolic space which …

*ArXiv Preprint, 2020*

This paper proposes a new equation from continuous adjoint theory to compute the gradient of quantities governed by the Transport Theory of light. Unlike discrete gradients ala autograd, which work at the code level, we first formulate the continuous …