  Deep learning has shown great potential for modeling the physical dynamics of
complex particle systems such as fluids. Existing approaches, however, require
the supervision of consecutive particle properties, including positions and
velocities. In this paper, we consider a partially observable scenario known as
fluid dynamics grounding, that is, inferring the state transitions and
interactions within the fluid particle systems from sequential visual
observations of the fluid surface. We propose a differentiable two-stage
network named NeuroFluid. Our approach consists of (i) a particle-driven neural
renderer, which involves fluid physical properties into the volume rendering
function, and (ii) a particle transition model optimized to reduce the
differences between the rendered and the observed images. NeuroFluid provides
the first solution to unsupervised learning of particle-based fluid dynamics by
training these two models jointly. It is shown to reasonably estimate the
underlying physics of fluids with different initial shapes, viscosity, and
densities.
