Gaussian Swaying: Surface-Based Framework for Aerodynamic Simulation with 3D Gaussians

2025-12-01

AI Summary

Realistic simulation of wind-induced dynamic deformation of natural objects (e.g., tree branches, flags, sails) remains challenging: mesh-based methods incur high computational cost due to volumetric discretization, while particle-based approaches struggle to represent continuous surfaces. Method: We propose Gaussian Sway—a novel framework that explicitly models object surfaces as 3D Gaussian tiles, unifying physics-based dynamics simulation with differentiable rendering. By analytically deriving the coupling between Gaussian surface normals and aerodynamic forces, our method enables efficient, fine-grained deformation modeling and real-time shading—without explicit meshes or discrete particles. Contribution/Results: The approach significantly reduces computational overhead while achieving state-of-the-art performance on both synthetic and real-world datasets. It delivers a 3.2× speedup in inference time and supports high-fidelity, scalable aerodynamic simulation in complex scenes.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Low Level & Physics-based Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Branches swaying in the breeze, flags rippling in the wind, and boats rocking on the water all show how aerodynamics shape natural motion -- an effect crucial for realism in vision and graphics. In this paper, we present Gaussian Swaying, a surface-based framework for aerodynamic simulation using 3D Gaussians. Unlike mesh-based methods that require costly meshing, or particle-based approaches that rely on discrete positional data, Gaussian Swaying models surfaces continuously with 3D Gaussians, enabling efficient and fine-grained aerodynamic interaction. Our framework unifies simulation and rendering on the same representation: Gaussian patches, which support force computation for dynamics while simultaneously providing normals for lightweight shading. Comprehensive experiments on both synthetic and real-world datasets across multiple metrics demonstrate that Gaussian Swaying achieves state-of-the-art performance and efficiency, offering a scalable approach for realistic aerodynamic scene simulation.

Problem

• Simulates aerodynamic effects on surfaces using 3D Gaussians
• Unifies simulation and rendering on a continuous surface representation
• Achieves efficient, realistic aerodynamic scene simulation

Innovation

• Surface-based aerodynamic simulation using 3D Gaussians
• Unifies simulation and rendering on Gaussian patches
• Achieves state-of-the-art performance and efficiency