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.

翻译：尽管从单视角图像生成三维内容已得到广泛研究，但从视频创建物理一致的三维动态场景仍处于早期阶段。我们提出一种新颖框架，利用生成式三维高斯泼溅（3DGS）模型，通过仿真方法从单视角视频中提取并重仿真三维动态流体对象。首先从单视角图像生成并优化由3DGS表示的流体几何结构，随后进行跨帧去噪、致密化与对齐处理。我们利用光流估计流体表面速度，并提出主流提取算法对其进行优化。三维体积速度场随后从流体封闭表面的速度推导得出。该速度场被转换为无散度的基于网格的表示形式，借助其跨帧可微性实现仿真参数的优化。此过程输出可直接用于仿真的流体资产，其物理动力学特性与源视频观测结果高度吻合。我们的方法适用于多种液态流体（包括无粘性与粘性流体），并允许用户无缝编辑输出几何结构或延长运动时长。这种从易于获取的网络单视角视频自动创建三维动态流体资产的方法，为低成本生成大规模三维流体资产展现出巨大潜力。