# Animating Pictures with Eulerian Motion Fields

arXiv: https://arxiv.org/abs/2011.15128 · HTML: https://mlsanity.com/paper/2011.15128/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 30 of 55)

- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling](https://mlsanity.com/paper/2401.15977/) (2024)
- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [DisCo: Disentangled Control for Realistic Human Dance Generation](https://mlsanity.com/paper/2307.00040/) (2023)
- [Generative Image Dynamics](https://mlsanity.com/paper/2309.07906/) (2023)
- [Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos](https://mlsanity.com/paper/2304.04452/) (2023)
- [AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance](https://mlsanity.com/paper/2311.12886/) (2023)
- [PhysGen3D: Crafting a Miniature Interactive World from a Single Image](https://mlsanity.com/paper/2503.20746/) (2025)
- [MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation](https://mlsanity.com/paper/2502.04299/) (2025)
- [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021)
- [LivePhoto: Real Image Animation with Text-guided Motion Control](https://mlsanity.com/paper/2312.02928/) (2023)
- [FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis](https://mlsanity.com/paper/2502.08244/) (2025)
- [TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models](https://mlsanity.com/paper/2403.17005/) (2024)
- [StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration](https://mlsanity.com/paper/2411.04925/) (2024)
- [PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://mlsanity.com/paper/2312.13964/) (2023)
- [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)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image](https://mlsanity.com/paper/2308.10257/) (2023)
- [DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata](https://mlsanity.com/paper/2211.11417/) (2022)
- [3D-Aware Video Generation](https://mlsanity.com/paper/2206.14797/) (2022)
- [Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance](https://mlsanity.com/paper/2207.10123/) (2022)
- [Simulating Fluids in Real-World Still Images](https://mlsanity.com/paper/2204.11335/) (2022)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://mlsanity.com/paper/2104.02687/) (2021)
- [Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images](https://mlsanity.com/paper/2303.08639/) (2023)
- [Diverse Generation from a Single Video Made Possible](https://mlsanity.com/paper/2109.08591/) (2021)
- [Automatic Animation of Hair Blowing in Still Portrait Photos](https://mlsanity.com/paper/2309.14207/) (2023)

## Abstract (arXiv)

In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.