arXiv subjects

Paul Kry

Publications and source records attributed to Paul Kry.

5 recordsLinked to original sources

arXiv2026

Love Handles: Decimation for Deformation Handles with Compact Support and Low Memory Footprints

Estimating the deformation of solids via physical simulation is an important problem spanning fields such as computer animation, engineering and robotics. Such simulations are computationally expensive and scale poorly when the representation of an object is refined by increasing the level of discretization. Reduced Order Methods (ROM) offer computational savings by decreasing the number of degrees of freedom, for example by using \emph{handles} that control groups of vertices. We present the first decimation-based algorithm for computing a sparse, compactly supported set of deformation handles. The crux of our method utilizes iterative algebraic simplification to optimize handle deformation to match any input deformation, such as linear vibration modes. This applies to any volumetric input mesh, including those with high genus or porous features, since we do not alter the geometry. We also devise an efficient algorithm to compute and update compact supports and their associated weights. We leverage compact support to develop an efficient, reduced-cubature computation scheme. Once optimized, our handles offer a memory-efficient solution while enabling real-time elastodynamics simulation of complex geometry. We show real-time performance on a variety of tetrahedral meshes with up to 796,623 tetrahedra.

cs.GR↗

arXiv2025

Neural Kinematic Bases for Fluids

We propose mesh-free fluid simulations that exploit a kinematic neural basis for velocity fields represented by an MLP. We design a set of losses that ensures that these neural bases approximate fundamental physical properties such as orthogonality, divergence-free, boundary alignment, and smoothness. Our neural bases can then be used to fit an input sketch of a flow, which will inherit the same fundamental properties from the bases. We then can animate such flow in real-time using standard time integrators. Our neural bases can accommodate different domains, moving boundaries, and naturally extend to three dimensions.

cs.GR↗

arXiv2020

Learning Elastic Constitutive Material and Damping Models

Commonly used linear and nonlinear constitutive material models in deformation simulation contain many simplifications and only cover a tiny part of possible material behavior. In this work we propose a framework for learning customized models of deformable materials from example surface trajectories. The key idea is to iteratively improve a correction to a nominal model of the elastic and damping properties of the object, which allows new forward simulations with the learned correction to more accurately predict the behavior of a given soft object. Space-time optimization is employed to identify gentle control forces with which we extract necessary data for model inference and to finally encapsulate the material correction into a compact parametric form. Furthermore, a patch based position constraint is proposed to tackle the challenge of handling incomplete and noisy observations arising in real-world examples. We demonstrate the effectiveness of our method with a set of synthetic examples, as well with data captured from real world homogeneous elastic objects.

cs.GR↗

arXiv2019

Locking-free Simulation of Isometric Thin Plates

To efficiently simulate very thin, inextensible materials like cloth or paper, it is tempting to replace force-based thin-plate dynamics with hard isometry constraints. Unfortunately, naive formulations of the constraints induce membrane locking---artificial stiffening of bending modes due to the inability of discrete kinematics to reproduce exact isometries. We propose a simple set of meshless isometry constraints, based on moving-least-squares averaging of the strain tensor, which do not lock, and which can be easily incorporated into standard constrained Lagrangian dynamics integration.

cs.GR↗

arXiv2018

Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data

The accuracy and fidelity of deformation simulations are highly dependent upon the underlying constitutive material model. Commonly used linear or nonlinear constitutive material models only cover a tiny part of possible material behavior. In this work we propose a unified framework for modeling deformable material. The key idea is to use a neural network to correct a nominal model of the elastic and damping properties of the object. The neural network encapsulates a complex function that is hard to explicitly model. It injects force corrections that help the forward simulation to more accurately predict the true behavior of a given soft object, which includes non-linear elastic forces and damping. Attempting to satisfy the requirement from real material interference and animation design scenarios, we learn material models from examples of dynamic behavior of a deformable object's surface. The challenge is that such data is sparse as it is consistently given only on part of the surface. Sparse reduced space-time optimization is employed to gradually generate increasingly accurate training data, which further refines and enhances the neural network. We evaluate our choice of network architecture and show evidence that the modest amount of training data we use is suitable for the problem tackled. Our method is demonstrated with a set of synthetic examples.

cs.GR↗