Simulating Fluids in Real-World Still Images

Siming Fan, Jingtan Piao, Chen Qian, Hongsheng Li, Kwan-Yee Lin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 15922-15931

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, 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, as a replacement for motion estimation. Specifically, we leverage triangular mesh based on a monocular depth estimator to represent fluid surface layer and simulate the motion with the inspiration of classic physics theory of hybrid Lagrangian-Eulerian method, along with a learnable network so as to adapt to complex real-world image textures.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 proposed surface-based layer representation and surface-only fluid simulation naturally disentangle the scene, interactive editing such as adding objects and texture replacing could be easily achieved with realistic results.