We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning the observable, Eulerian flow phenomena. Our differentiable vortex particles are coupled with a learnable, vortex-to-velocity dynamics mapping to effectively capture the complex flow features in a physically-constrained, low-dimensional space. This representation facilitates the learning of a fluid simulator tailored to the input video that can deliver robust, long-term future predictions. The value of our method is twofold: first, our learned simulator enables the inference of hidden physics quantities (e.g., velocity field) purely from visual observation; secondly, it also supports future prediction, constructing the input video's sequel along with its future dynamics evolution. We compare our method with a range of existing methods on both synthetic and real-world videos, demonstrating improved reconstruction quality, visual plausibility, and physical integrity.

翻译：我们提出了一种新颖的可微涡旋粒子（DVP）方法，用于从单个视频中推断和预测流体动力学。其核心是一个基于粒子的潜在空间，用于封装隐藏的、拉格朗日框架下的涡旋演化过程，这正是可观测欧拉流现象的基础。我们的可微涡旋粒子与可学习的涡旋-速度动力学映射相结合，能够在受物理约束的低维空间中有效捕捉复杂的流动特征。这种表示促进了对特定输入视频定制流体模拟器的学习，从而实现稳健的长期未来预测。该方法的价值体现在两方面：首先，习得的模拟器能够仅从视觉观测中推断隐藏的物理量（如速度场）；其次，它还支持未来预测，构建输入视频的后续内容及其动态演化过程。我们在合成视频与真实视频上将我们的方法与多种现有方法进行了比较，展示了在重建质量、视觉真实感和物理完整性方面的显著改善。