  In this work, we tackle the problem of real-world fluid animation from a
still image. The key of our system is a surface-based layered representation
deriving from video decomposition, where the scene is decoupled into a surface
fluid layer and an impervious background layer with corresponding
transparencies to characterize the composition of the two layers. The animated
video can be produced by warping only the surface fluid layer according to the
estimation of fluid motions and recombining it with the background. In
addition, we introduce surface-only fluid simulation, a $2.5D$ fluid
calculation version, as a replacement for motion estimation. Specifically, we
leverage the triangular mesh based on a monocular depth estimator to represent
the fluid surface layer and simulate the motion in the physics-based framework
with the inspiration of the classic theory of the hybrid Lagrangian-Eulerian
method, along with a learnable network so as to adapt to complex real-world
image textures. We demonstrate the effectiveness of the proposed system through
comparison with existing methods in both standard objective metrics and
subjective ranking scores. Extensive experiments not only indicate our method's
competitive performance for common fluid scenes but also better robustness and
reasonability under complex transparent fluid scenarios. Moreover, as the
proposed surface-based layer representation and surface-only fluid simulation
naturally disentangle the scene, interactive editing such as adding objects to
the river and texture replacing could be easily achieved with realistic
results.
