# Simulating Fluids in Real-World Still Images arXiv: https://arxiv.org/abs/2204.11335 ยท HTML: https://mlsanity.com/paper/2204.11335/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 45 references that are in the map. - First posted on arXiv 24 Apr 2022. ## Builds on (most cited first, 22 of 22) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016) - [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014) - [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019) - [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017) - [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016) - [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018) - [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017) - [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018) - [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019) - [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020) - [Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image](https://mlsanity.com/paper/2012.09855/) (2020) - [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016) - [SelFlow: Self-Supervised Learning of Optical Flow](https://mlsanity.com/paper/1904.09117/) (2019) - [Animating Pictures with Eulerian Motion Fields](https://mlsanity.com/paper/2011.15128/) (2020) - [Video Generation from Single Semantic Label Map](https://mlsanity.com/paper/1903.04480/) (2019) - [Omnimatte: Associating Objects and Their Effects in Video](https://mlsanity.com/paper/2105.06993/) (2021) - [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021) - [Dense Optical Flow Prediction from a Static Image](https://mlsanity.com/paper/1505.00295/) (2015) - [Self-Supervised Learning via Conditional Motion Propagation](https://mlsanity.com/paper/1903.11412/) (2019) - [Ships, Splashes, and Waves on a Vast Ocean](https://mlsanity.com/paper/2108.05481/) (2021) ## Built on by (most cited first, 15 of 15) - [Generative Image Dynamics](https://mlsanity.com/paper/2309.07906/) (2023) - [MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation](https://mlsanity.com/paper/2502.04299/) (2025) - [TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models](https://mlsanity.com/paper/2403.17005/) (2024) - [3D Cinemagraphy from a Single Image](https://mlsanity.com/paper/2303.05724/) (2023) - [Text-Guided Synthesis of Eulerian Cinemagraphs](https://mlsanity.com/paper/2307.03190/) (2023) - [VividDream: Generating 3D Scene with Ambient Dynamics](https://mlsanity.com/paper/2405.20334/) (2024) - [Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image](https://mlsanity.com/paper/2308.10257/) (2023) - [Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images](https://mlsanity.com/paper/2303.08639/) (2023) - [3D Video Loops from Asynchronous Input](https://mlsanity.com/paper/2303.05312/) (2023) - [Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images](https://mlsanity.com/paper/2504.05458/) (2025) - [Learning an Implicit Physics Model for Image-based Fluid Simulation](https://mlsanity.com/paper/2508.08254/) (2025) - [DreamLoop: Controllable Cinemagraph Generation from a Single Photograph](https://mlsanity.com/paper/2601.02646/) (2026) - [Controllable Longer Image Animation with Diffusion Models](https://mlsanity.com/paper/2405.17306/) (2024) - [CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations](https://mlsanity.com/paper/2605.10586/) (2026) - [Eulerian Motion Reconstruction for Water Scenery](https://mlsanity.com/paper/2609.38622/) (2026) ## Abstract (arXiv) 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.