Mixed Material Point Methods for Stiff Elastoplasticity
ACM Transactions on Graphics (SIGGRAPH), 2026
We present a family of mixed Material Point Methods well suited to CFL-rate simulation of stiff elastoviscoplastic materials, up to the incompressible limit. Our work builds on the mixed discretization from Daviet and Bertails-Descoubes [2016a] and …
Adaptive Volumetric Mechanical Property Fields Invariant to Resolution
International Conference on Machine Learning (ICML), 2026
Accurate mechanical properties (or materials) Young’s modulus, Poisson’s ratio and density are essential for reliable physics simulation of digital worlds, but most 3D assets lack this information. We propose AdaVoMP, a method for …
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to …
VoMP: Predicting Volumetric Mechanical Property Fields
International Conference on Learning Representations (ICLR), 2026
Physical simulation relies on spatially-varying mechanical properties, typically laboriously hand-crafted. We present the first feed-forward model to predict fine-grained mechanical properties, Young’s modulus, Poisson’s ratio, and …
Robust Derivative Estimation with Walk on Stars
ACM Transactions on Graphics (SIGGRAPH Asia), 2025
Monte Carlo methods based on the walk on spheres (WoS) algorithm offer a parallel, progressive, and output-sensitive approach for solving partial differential equations (PDEs) in complex geometric domains. Building on this foundation, the walk on …
Harmonic Caching for Walk on Spheres
ACM Transactions on Graphics (SIGGRAPH Asia), 2025
We present a variance reduction technique for Walk on Spheres (WoS) that solves elliptic partial differential equations (PDEs) by combining overlapping harmonic expansions of the solution, each estimated using unbiased Monte Carlo random walks. Our …
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
ArXiv Preprint, 2025
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, …
State of the Art in Grid-Free Monte Carlo Methods for Partial Differential Equations
ACM Transactions on Graphics (SIGGRAPH Courses), 2025
OpenVDB
ACM Transactions on Graphics (SIGGRAPH Courses), 2025
This course will cover the compact volume data structure and various tools available in the open-source library OpenVDB. Since its release in 2012 it has become an industry standard and has been used in numerous VFX movies release in the past decade. …
Unbiased Differential Visibility Using Fixed-Step Walk-on-Spherical-Caps And Closest Silhouettes
ACM Transactions on Graphics (SIGGRAPH), 2025
Computing derivatives of path integrals under evolving scene geometry is a fundamental problem in physics-based differentiable rendering, which requires differentiating discontinuities in the visibility function. Warped area reparameterization is a …
Stochastic Barnes-Hut Approximation for Fast Summation on the GPU
ACM Transactions on Graphics (SIGGRAPH), 2025
We present a novel stochastic version of the Barnes-Hut approximation. Regarding the level-of-detail (LOD) family of approximations as control variates, we construct an unbiased estimator of the kernel sum being approximated. Through several examples …
Solving partial differential equations in participating media
ACM Transactions on Graphics (SIGGRAPH), 2025
We consider the problem of solving partial differential equations (PDEs) in domains with complex microparticle geometry that is impractical, or intractable, to model explicitly. Drawing inspiration from volume rendering, we propose tackling this …
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing and human body using a unified geometry or produce …
Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains
ACM Transactions on Graphics (SIGGRAPH), 2025
We present an elastic simulator for domains defined as evolving implicit functions, which is efficient, robust, and differentiable with respect to both shape and material. This simulator is motivated by applications in 3D reconstruction: it is …
WaveBlender: Practical Sound-Source Animation in Blended Domains
SIGGRAPH Asia (Conference Track), 2024
Synthesizing sound sources for modern physics-based animation is challenging due to rapidly moving, deforming, and vibrating interfaces that produce acoustic waves within the air domain. Not only must the methods synthesize sounds that are faithful …
Differential Walk on Spheres
ACM Transactions on Graphics (SIGGRAPH Asia), 2024
We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary …
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution
ACM Transactions on Graphics (SIGGRAPH), 2024
We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, real-time physics-based simulation to a level of detail that closely approximates that …
Walkin' Robin: Walk on Stars with Robin Boundary Conditions
ACM Transactions on Graphics (SIGGRAPH), 2024
🏆 Best Paper
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 …
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
ACM Transactions on Graphics (SIGGRAPH), 2024
We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, …
Monte Carlo Geometry Processing
Symposium on Geometry Processing Graduate School Courses, 2024
NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks
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 …
Decorrelating ReSTIR Samplers via MCMC Mutations
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 …
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
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 …
ViCMA: Visual Control of Multibody Animations
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 …
Subspace Mixed Finite Elements for Real-Time Heterogeneous Elastodynamics
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 …
Neural Collision Fields for Triangle Primitives
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 …
Walk on Stars: Grid-Free Monte Carlo for Neumann Boundary Conditions
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 …
Simulation-aided face strain extraction for ML animation systems
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 …
Interactive Hair Simulation on the GPU using ADMM
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 …
Boundary Value Caching for Walk on Spheres
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 …
Deep Learned Face Swapping in Feature Film Production
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 …
An Exact Bitwise Reversible Integrator
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 …
NanoVDB: A GPU-Friendly and Portable VDB Data Structure For Real-Time Rendering And Simulation
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 …
Ultrahyperbolic Representation Learning
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 …
Computing Light Transport Gradients Using the Adjoint Method
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 …