Physics-Based Animation

Xuan Li, Chang Yu, Wenxin Du, Ying Jiang, Tianyi Xie, Yunuo Chen, Yin Yang, Chenfanfu Jiang

Recent advances in large models have significantly advanced image-to-3D reconstruction. However, the generated models are often fused into a single piece, limiting their applicability in downstream tasks. This paper focuses on 3D garment generation, a key area for applications like virtual try-on with dynamic garment animations, which require garments to be separable and simulation-ready. We introduce Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image. Starting with the image, our approach combines a pre-trained image-to-sewing pattern generation model for creating coarse sewing patterns with a pre-trained multi-view diffusion model to produce multi-view images. The sewing pattern is further refined using a differentiable garment simulator based on the generated multi-view images. Versatile experiments demonstrate that our optimization approach substantially enhances the geometric alignment of the reconstructed 3D garments and humans with the input image. Furthermore, by integrating a texture generation module and a human motion generation module, we produce customized physics-plausible and realistic dynamic garment demonstrations.

Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with
Diffusion Prior and Differentiable Physics

Fumiya Narita, Nimiko Ochiai, Takashi Kanai, Ryoichi Ando

This paper introduces a novel grid structure that extends tall cell methods for efficient deep water simulation. Unlike previous tall cell methods, which are designed to capture all the fine details around liquid surfaces, our approach subdivides tall cells horizontally, allowing for more aggressive adaptivity and a significant reduction in the number of cells. The foundation of our method lies in a new variational formulation of Poisson’s equations for pressure solve tailored for tall-cell grids, which naturally handles the transition of variable-sized cells. This variational view not only permits the use of the efficacy-proven conjugate gradient method but also facilitates monolithic two-way coupled rigid bodies. The key distinction between our method and previous general adaptive approaches, such as tetrahedral or octree grids, is the simplification of adaptive grid construction. Our method performs grid subdivision in a quadtree fashion, rather than an octree. These 2D cells are then simply extended vertically to complete the tall cell population. We demonstrate that this novel form of adaptivity, which we refer to as quadtree tall cells, delivers superior performance compared to traditional uniform tall cells.

Quadtree Tall Cells for Eulerian Liquid Simulation

Zhanyu Yang, Aryamaan Jain, Guillaume Cordonnier, Marie-Paule Cani, Zhaopeng Wang, Bedrich Benes

We introduce Arenite, a novel physics-based approach for modeling sandstone structures. The key insight of our work is that simulating a combination of stress and multi-factor erosion enables the generation of a wide variety of sandstone structures observed in nature. We isolate the key shape-forming phenomena: multi-physics fabric interlocking, wind and fluvial erosion, and particle-based deposition processes. Complex 3D structures such as arches, alcoves, hoodoos, or buttes can be achieved by creating simple 3D structures with user-painted erodable areas and vegetation and running the simulation. We demonstrate the algorithm on a wide variety of structures, and our GPU-based implementation achieves the simulation in less than 5 minutes on a desktop computer for our most complex example.

Arenite: A Physics-based Sandstone Simulator

Behrooz Zarebavani, Danny Kaufman, David I W Levin, Maryam Mehri Dehnavi

We introduce Parth, a fill-reducing ordering method for sparse Cholesky solvers with dynamic sparsity patterns (e.g., in physics simulations with contact or geometry processing with local remeshing). Parth facilitates the selective reuse of fill-reducing orderings when sparsity patterns exhibit temporal coherence, avoiding full symbolic analysis by localizing the effect of dynamic sparsity changes on the ordering vector. We evaluated Parth on over 175,000 linear systems collected from both physics simulations and geometry processing applications, and show that for some of the most challenging physics simulations, it achieves up to 14x reordering runtime speedup, resulting in a 2x speedup in Cholesky solve time—even on top of well-optimized solvers such as Apple Accelerate and Intel MKL.

Adaptive Algebraic Reuse of Reordering in Cholesky Factorizations with Dynamic Sparsity Patterns

Anka Chen, Jerry Hsu, Ziheng Liu, Miles Macklin, Yin Yang, Cem Yuksel

We present a novel contact model, termed Offset Geometric Contact (OGC), for guaranteed penetration-free simulation of codimensional objects with minimal computational overhead. Our method is based on constructing a volumetric shape by offsetting each face along its normal direction, ensuring orthogonal contact forces, thus allows large contact radius without artifacts. We compute vertex-specific displacement bounds to guarantee penetration-free simulation, which improves convergence and avoids the need for expensive continuous collision detection. Our method relies solely on massively parallel local operations, avoiding global synchronization and enabling efficient GPU implementation. Experiments demonstrate real-time, large-scale simulations with performance more than two orders of magnitude faster than prior methods while maintaining consistent computational budgets.

Offset Geometric Contact

Jingrui Xing, Bin Wang, Mengyu Chu, Baoquan Chen

We present a grid-free fluid solver featuring a novel Gaussian representation. Drawing inspiration from the expressive capabilities of 3D Gaussian Splatting in multi-view image reconstruction, we model the continuous flow velocity as a weighted sum of multiple Gaussian functions. This representation is continuously differentiable, which enables us to derive spatial differentials directly and solve the time-dependent PDE via a custom first‑order optimization tailored to fluid dynamics. Compared to traditional discretizations, which typically adopt Eulerian, Lagrangian, or hybrid perspectives, our approach is inherently memory-efficient and spatially adaptive, enabling it to preserve fine-scale structures and vortices with high fidelity. While these advantages are also sought by implicit neural representations, GSR offers enhanced robustness, accuracy, and generality across diverse fluid phenomena, with improved computational efficiency during temporal evolution. Though our first‑order solver does not yet match the speed of fluid solvers using explicit representations, its continuous nature substantially reduces spatial discretization error and opens a new avenue for high‑fidelity simulation. We evaluate the proposed solver across a broad range of 2D and 3D fluid phenomena, demonstrating its ability to preserve intricate vortex dynamics, accurately capture boundary-induced effects such as Kármán vortex streets, and remain robust across long time horizons—all without additional parameter tuning. Our results suggest that GSR offers a compelling direction for future research in fluid simulation.

Gaussian Fluids: A Grid-Free Fluid Solver based on Gaussian Spatial Representation

Yijie Liu, Taiyuan Zhang, Xiaoxiao Yan, Han Yan, Nuoming Liu, Bo Ren

The freezing of thin films is a mesmerizing natural phenomenon, inspiring photographers to capture its beauty through their lenses and digital artists to recreate its allure using effects tools. In this paper, we present a novel method for physically simulating the intricate freezing dynamics on thin films. By accounting for the influence of phase and temperature changes on surface tension, our method reproduces Marangoni freezing and the “Snow-Globe Effect”, characterized by swirling ice dendrites on the film. We introduce a novel Phase Map method on top of the state-of-the-art Moving Eulerian-Lagrangian Particles (MELP) meshless framework, enabling dendritic crystal simulation on mobile particles and offering precise control over freezing patterns. We demonstrate that our method is able to capture a wide range of dynamic freezing processes of soap bubbles and is stable for complex boundaries in our experiments.

Controllable Complex Freezing Dynamics Simulation on Thin Films

Yuchen Sun, Junlin Li, Ruicheng Wang, Sinan Wang, Zhiqi Li, Bart G. van Bloemen Waanders, Bo Zhu

We propose Leapfrog Flow Maps (LFM) to simulate incompressible fluids with rich vortical flows in real time. Our key idea is to use a hybrid velocity-impulse scheme enhanced with leapfrog method to reduce the computational workload of impulse-based flow map methods, while possessing strong ability to preserve vortical structures and fluid details. In order to accelerate the impulse-to-velocity projection, we develop a fast matrix-free Algebraic Multigrid Preconditioned Conjugate Gradient (AMGPCG) solver with customized GPU optimization, which makes projection comparable with impulse evolution in terms of time cost. We demonstrate the performance of our method and its efficacy in a wide range of examples and experiments, such as real-time simulated burning fire ball and delta wingtip vortices.

Leapfrog Flow Maps for Real-Time Fluid Simulation

Duowen Chen^*, Zhiqi Li^*, Taiyuan Zhang, Jinjin He, Junwei Zhou, Bart G van Bloemen Waanders, Bo Zhu (* Joint First Authors)

This paper presents a unified compressible flow map framework designed to accommodate diverse compressible flow systems, including high-Mach-number flows (e.g., shock waves and supersonic aircraft), weakly compressible systems (e.g., smoke plumes and ink diffusion), and incompressible systems evolving through compressible acoustic quantities (e.g., free-surface shallow water). At the core of our approach is a theoretical foundation for compressible flow maps based on Lagrangian path integrals, a novel advection scheme for the conservative transport of density and energy, and a unified numerical framework for solving compressible flows with varying pressure treatments. We validate our method across three representative compressible flow systems, characterized by varying fluid morphologies, governing equations, and compressibility levels, demonstrating its ability to preserve and evolve spatiotemporal features such as vortical structures and wave interactions governed by different flow physics. Our results highlight a wide range of novel phenomena, from ink torus breakup to delta wing tail vortices and vortex shedding on free surfaces, significantly expanding the range of fluid systems that flow-map methods can handle.

Fluid Simulation on Compressible Flow Maps

Zhiqi Li, Candong Lin, Duowen Chen^, Xinyi Zhou, Shiying Xiong, Bo Zhu

We propose a novel gauge fluid solver that evolves Clebsch wave functions on particle flow maps (PFMs). The key insight underlying our work is that particle flow maps exhibit superior performance in transporting point elements—such as Clebsch components—compared to line and surface elements, which were the focus of previous methods relying on impulse and vortex gauge variables for flow maps. Our Clebsch PFM method incorporates three main contributions: a novel gauge transformation enabling accurate transport of wave functions on particle flow maps, an enhanced velocity reconstruction method for coarse grids, and a PFM-based simulation framework designed to better preserve fine-scale flow structures. We validate the Clebsch PFM method through a wide range of benchmark tests and simulation examples, ranging from leapfrogging vortex rings and vortex reconnections to Kelvin–Helmholtz instabilities, demonstrating that our method outperforms its impulse- or vortex-based counterparts on particle flow maps, particularly in preserving and evolving small-scale features.

Clebsch Gauge Fluid on Particle Flow Maps