  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.
