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：我们提出一种新型可微涡旋粒子方法，用于从单段视频中推断并预测流体动力学。其核心在于构建基于粒子的隐空间，以封装隐藏在可观测欧拉流现象背后的拉格朗日涡旋演化过程。可微涡旋粒子与可学习的涡旋-速度动力学映射相耦合，在物理约束的低维空间中有效捕获复杂流场特征。该表征方式便于学习针对输入视频定制的流体模拟器，实现鲁棒的长期未来预测。本方法的价值体现在两方面：第一，所学模拟器能够仅凭视觉观测推断隐藏物理量（如速度场）；第二，支持未来预测，可重构输入视频的后续片段及其动力学演化。我们在合成视频与真实视频上对比多种现有方法，验证了本方法在重建质量、视觉真实感及物理一致性方面的提升。