3D Dynamic Fluid Assets from Single-View Videos with Generative Gaussian Splatting

Abstract

While the generation of 3D content from single-view images has been extensively studied, the creation of physically consistent 3D dynamic scenes from videos remains in its early stages. We propose a novel framework leveraging generative 3D Gaussian Splatting (3DGS) models to extract and re-simulate 3D dynamic fluid objects from single-view videos using simulation methods. The fluid geometry represented by 3DGS is initially generated and optimized from single-view images, then denoised, densified, and aligned across frames. We estimate the fluid surface velocity using optical flow, propose a mainstream extraction algorithm to refine it. The 3D volumetric velocity field is then derived from the velocity of the fluid's enclosed surface. The velocity field is therewith converted into a divergence-free, grid-based representation, enabling the optimization of simulation parameters through its differentiability across frames. This process outputs simulation-ready fluid assets with physical dynamics closely matching those observed in the source video. Our approach is applicable to various liquid fluids, including inviscid and viscous types, and allows users to edit the output geometry or extend movement durations seamlessly. This automatic method for creating 3D dynamic fluid assets from single-view videos, easily obtainable from the internet, shows great potential for generating large-scale 3D fluid assets at a low cost.