Physics-Based Animation

Kemeng Huang, Xinyu Lu, Huancheng Lin, Taku Komura, Minchen Li

Incremental Potential Contact (IPC) is a widely used, robust, and accurate method for simulating complex frictional contact behaviors. However, achieving high efficiency remains a major challenge, particularly as material stiffness increases, which leads to slower Preconditioned Conjugate Gradient (PCG) convergence, even with the state-of-the-art preconditioners. In this paper, we propose a fully GPU-optimized IPC simulation framework capable of handling materials across a wide range of stiffnesses, delivering consistent high performance and scalability with up to 10 × speedup over state-of-the-art GPU IPC methods. Our framework introduces three key innovations: 1) A novel connectivity-enhanced Multilevel Additive Schwarz (MAS) preconditioner on the GPU, designed to efficiently capture both stiff and soft elastodynamics and improve PCG convergence at a reduced preconditioning cost. 2) A C²-continuous cubic energy with an analytic eigensystem for inexact strain limiting, enabling more parallel-friendly simulations of stiff membranes, such as cloth, without membrane locking. 3) For extremely stiff behaviors where elastic waves are barely visible, we employ affine body dynamics (ABD) with a hash-based two-level reduction strategy for fast Hessian assembly and efficient affine-deformable coupling. We conduct extensive performance analyses and benchmark studies to compare our framework against state-of-the-art methods and alternative design choices. Our system consistently delivers the fastest performance across soft, stiff, and hybrid simulation scenarios, even in cases with high resolution, large deformations, and high-speed impacts.

StiffGIPC: Advancing GPU IPC for Stiff Affine-Deformable Simulation

Duowen Chen, Junwei Zhou, Bo Zhu

We propose a neural particle level set (Neural PLS) method to accommodate tracking and evolving dynamic neural representations. At the heart of our approach is a set of oriented particles serving dual roles of interface trackers and sampling seeders. These dynamic particles are used to evolve the interface and construct neural representations on a multi-resolution grid-hash structure to hybridize coarse sparse distance fields and multi-scale feature encoding. Based on these parallel implementations and neural-network-friendly architectures, our neural particle level set method combines the computational merits on both ends of the traditional particle level sets and the modern implicit neural representations, in terms of feature representation and dynamic tracking. We demonstrate the efficacy of our approach by showcasing its performance surpassing traditional level-set methods in both benchmark tests and physical simulations.

A Neural Particle Level Set Method for Dynamic Interface Tracking

Qixin Liang

We present six thin plate/shell models, derived from three distinct types of curvature operators formulated within the corotational frame, for simulating both rest-flat and rest-curved triangular meshes. Each curvature operator derives a curvature expression corresponding to both a plate model and a shell model. The corotational edge-based hinge model uses an edge-based stencil to compute directional curvature, while the corotational FVM hinge model utilizes a triangle-centered stencil, applying the finite volume method (FVM) to superposition directional curvatures across edges, yielding a generalized curvature. The corotational smoothed hinge model also employs a triangle-centered stencil but transforms directional curvatures into a generalized curvature based on a quadratic surface fit. All models assume small strain and small curvature, leading to constant bending energy Hessians, which benefit implicit integrators. Through quantitative benchmarks and qualitative elastodynamic simulations with large time steps, we demonstrate the accuracy, efficiency, and stability of these models. Our contributions enhance the thin plate/shell library for use in both computer graphics and engineering applications.

Corotational Hinge-based Thin Plates/Shells

Deshan Gong, Yin Yang, Tianjia Shao, He Wang

Persistent wrinkles are often observed on crumpled garments e.g., the wrinkles around the knees after sitting for a while. Such wrinkles can be easily recovered if not deformed for long, and otherwise be persistent. Since they are vital to the visual realism of cloth animation, we aim to simulate realistic looking persistent wrinkles. To this end, we present a physics-inspired fine-grained wrinkle model. Different from existing methods, we recognize the importance of the interplay between internal friction and plasticity during wrinkle formation. Furthermore, we model their time dependence for persistent wrinkles. Our model is capable of not only simulating realistic wrinkle patterns, but also their time-dependent changes according to how long the deformation is maintained. Through extensive experiments, we show that our model is effective in simulating realistic spatial and temporal varying wrinkles, versatile in simulating different materials, and capable of generating more fine-grained wrinkles than the state of the art.

Cloth Animation with Time-dependent Persistent Wrinkles

Tomoyo Kikuchi, Takashi Kanai

We present a unified, primitive-first framework with DCD for collision response in physics-based simulations. Previous methods do not provide sufficient solutions on a framework that resolves edge-triangle and edge-edge collisions when handling self-collisions and inter-object collisions in a unified manner. We define a scalar function and its gradient, representing the distance between two triangles and the movement direction for collision response, respectively. The resulting method offers an effective solution for collisions with minor computational overhead and robustness for any type of deformable object, such as solids or cloth. The algorithm is conceptually simple and easy to implement. When using PBD/XPBD, it is straightforward to incorporate our method into a collision constraint.

A Unified Discrete Collision Framework for Triangle Primitives

Yuhan Wu, Nobuyuki Umetani

We propose BlendSim, a novel framework for editable simulation, and its lightweight storage using spacetime optimization. Traditional spacetime control methods suffer from a high computational complexity, which limits their use in interactive animation. The proposed approach effectively reduces the dimensionality of the problem by representing the motion trajectories of each vertex using continuous parametric Bézier splines with variable keyframe times. Because this mesh animation representation is continuous and fully differentiable, it can be optimized such that it follows the laws of physics under various constraints. The proposed method also integrates constraints, such as collisions and cyclic motion, making it suitable for real-world applications where seamless looping and physical interactions are required. Leveraging projective dynamics, we further enhance the computational efficiency by decoupling the optimization into local parallelizable and global quadratic steps, enabling a fast and stable simulation.

BlendSim: Simulation on Parametric Blendshapes using Spacetime Projective Dynamics

Xiaowei He, Shusen Liu, Yuzhong Guo, Jian Shi, Ying Qiao

In simulating fluids using position-based dynamics, the accuracy and robustness depend on numerous numerical parameters, including the time step size, iteration count, and particle size, among others. This complexity can lead to unpredictable control of simulation behaviors. In this paper, we first reformulate the problem of enforcing fluid compressibility/incompressibility into an nonlinear optimization problem, and then introduce a semi-implicit successive substitution method (SISSM) to solve the nonlinear optimization problem by adjusting particle positions in parallel. In contrast to calculating an intermediate variable, such as pressure, to enforce fluid incompressibility within the position-based dynamics (PBD) framework, the proposed semi- implicit approach eliminates the necessity of such calculations. Instead, it directly employs successive substitution of particle positions to correct density errors. This method exhibits reduced dependency to numerical parameters, such as particle size and time step variations, and improves consistency and stability in simulating fluids that range from highly compressible to nearly incompressible. We validates the effectiveness of applying a variety of different techniques in accelerating the convergence rate.

A Semi-Implicit SPH Method for Compressible and Incompressible
Flows with Improved Convergence

Joel Wretborn, Alexey Stomakhin, Christopher Batty

We introduce a novel unified mixture-based method for simulating underwater bubbles across a range of bubble scales. Our approach represents bubbles as a set of Lagrangian particles that are coupled with the surrounding Eulerian water volume. When bubble particles are sparsely distributed, each particle, typically smaller than the liquid grid voxel size, corresponds to an individual spherical bubble. As the sub-grid particles increase in local density our model smoothly aggregates them, ultimately forming connected, fully aerated volumetric regions that are properly resolved by the Eulerian grid. We complement our scheme with a continuous surface tension model, defined via the gradient of the bubbles’ local volume fractions, which works seamlessly across this scale transition. Our unified representation allows us to capture a wide range of effects across different scales—from tiny dispersed sub-grid air pockets to fully Eulerian two-phase interfacial flows.

A unified multi-scale method for simulating immersed bubbles

Yuchen Sun, Linglai Chen, Weiyuan Zeng, Tao Du, Shiying Xiong, Bo Zhu

This paper introduces a two-phase interfacial fluid model based on the impulse variable to capture complex vorticity-interface interactions. Our key idea is to leverage bidirectional flow map theory to enhance the transport accuracy of both vorticity and interfaces simultaneously and address their coupling within a unified Eulerian framework. At the heart of our framework is an impulse ghost fluid method to solve the two-phase incompressible fluid characterized by its interfacial dynamics. To deal with the history-dependent jump of gauge variables across a dynamic interface, we develop a novel path integral formula empowered by spatiotemporal buffers to convert the history-dependent jump condition into a geometry-dependent jump condition when projecting impulse to velocity. We demonstrate the efficacy of our approach in simulating and visualizing several interface-vorticity interaction problems with cross-phase vortical evolution, including interfacial whirlpool, vortex ring reflection, and leapfrogging bubble rings.

An Impulse Ghost Fluid Method for Simulating Two-Phase Flows

• A Unified Multi-scale Method for Simulating Immersed Bubbles
• A Semi-Implicit SPH Method for Compressible and Incompressible Flows with Improved Convergence
• A Hybrid Lagrangian-Eulerian Formulation of Thin-shell Fracture
• Rest Shape Optimization for Sag-Free Discrete Elastic Rods
• BlendSim: Simulation on Parametric Blendshapes using Spacetime Projective Dynamics
• A Unified Discrete Collision Framework for Triangle Primitives
• Cloth Animation with Time-dependent Persistent Wrinkles
• Corotational Hinge-based Thin Plates/Shells
• Eigenvalue Blending for Projected Newton