261 citations · 852 across the 31 of their papers we have counts for
38 papers
Neural Progressive Meshes
Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman +1
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Inter…
Data-Free Learning of Reduced-Order Kinematics
Nicholas Sharp, Cristian Romero, Alec Jacobson +4
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrate…
Surface Simplification using Intrinsic Error Metrics
Hsueh-Ti Derek Liu, Mark Gillespie, Benjamin Chislett +3
This paper describes a method for fast simplification of surface meshes. Whereas past methods focus on visual appearance, our goal is to solve equations on the surface. Hence, rath…
Fast Complementary Dynamics via Skinning Eigenmodes
Otman Benchekroun, Jiayi Eris Zhang, Siddhartha Chaudhuri +3
We propose a reduced-space elasto-dynamic solver that is well suited for augmenting rigged character animations with secondary motion. At the core of our method is a novel deformat…
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu +2
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fract…
Variable Bitrate Neural Fields
Towaki Takikawa, Alex Evans, Jonathan Tremblay +4
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art r…
Stochastic Poisson Surface Reconstruction
Silvia Sellán, Alec Jacobson
We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function,…
VectorAdam for Rotation Equivariant Geometry Optimization
Selena Ling, Nicholas Sharp, Alec Jacobson
The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same tim…
Learning Smooth Neural Functions via Lipschitz Regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson +2
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields are commonly represented as neural networks which map latent descriptors and 3D…
Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range Analysis
Nicholas Sharp, Alec Jacobson
Neural implicit representations, which encode a surface as the level set of a neural network applied to spatial coordinates, have proven to be remarkably effective for optimizing,…
ILA: Compilable Markdown for Linear Algebra
Yong Li, Shoaib Kamil, Alec Jacobson +1
Communicating linear algebra in written form is challenging: mathematicians must choose between writing in languages that produce well-formatted but semantically-underdefined repre…
Interactive Modelling of Volumetric Musculoskeletal Anatomy
Rinat Abdrashitov, Seungbae Bang, David I. W. Levin +2
We present a new approach for modelling musculoskeletal anatomy. Unlike previous methods, we do not model individual muscle shapes as geometric primitives (polygonal meshes, NURBS…
Surface Multigrid via Intrinsic Prolongation
Hsueh-Ti Derek Liu, Jiayi Eris Zhang, Mirela Ben-Chen +1
This paper introduces a novel geometric multigrid solver for unstructured curved surfaces. Multigrid methods are highly efficient iterative methods for solving systems of linear eq…
Normal-Driven Spherical Shape Analogies
Hsueh-Ti Derek Liu, Alec Jacobson
This paper introduces a new method to stylize 3D geometry. The key observation is that the surface normal is an effective instrument to capture different geometric styles. Centered…
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin +6
Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural…
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
Jun Gao, Wenzheng Chen, Tommy Xiang +4
3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction…
Diffusion Structures for Architectural Stripe Pattern Generation
Abhishek Madan, Alec Jacobson, David I. W. Levin
We present Diffusion Structures, a family of resilient shell structures from the eigenfunctions of a pair of novel diffusion operators. This approach is based on Michell's theorem…
Chordal Decomposition for Spectral Coarsening
Honglin Chen, Hsueh-Ti Derek Liu, Alec Jacobson +1
We introduce a novel solver to significantly reduce the size of a geometric operator while preserving its spectral properties at the lowest frequencies. We use chordal decompositio…
Complementary Dynamics
Jiayi Eris Zhang, Seungbae Bang, David I. W. Levin +1
We present a novel approach to enrich arbitrary rig animations with elastodynamic secondary effects. Unlike previous methods which pit rig displacements and physical forces as adve…
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
Thomas Davies, Derek Nowrouzezahrai, Alec Jacobson
A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Many prior works have focused on _latent-encoded_ neural i…
Least-Squares Affine Reflection Using Eigen Decomposition
Alec Jacobson
This note summarizes the steps to computing the best-fitting affine reflection that aligns two sets of corresponding points.
EMU: Efficient Muscle Simulation In Deformation Space
Vismay Modi, Lawson Fulton, Shinjiro Sueda +2
EMU is an efficient and scalable model to simulate bulk musculoskeletal motion with heterogenous materials. First, EMU requires no model reductions, or geometric coarsening, thereb…
Levitating Rigid Objects with Hidden Rods and Wires
Sarah Kushner, Risa Ulinski, Karan Singh +2
We propose a novel algorithm to efficiently generate hidden structures to support arrangements of floating rigid objects. Our optimization finds a small set of rods and wires betwe…
NiLBS: Neural Inverse Linear Blend Skinning
Timothy Jeruzalski, David I. W. Levin, Alec Jacobson +3
In this technical report, we investigate efficient representations of articulated objects (e.g. human bodies), which is an important problem in computer vision and graphics. To def…