# Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images

arXiv: https://arxiv.org/abs/2303.08639 · HTML: https://mlsanity.com/paper/2303.08639/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Latent Image Animator: Learning to Animate Images via Latent Space Navigation](https://mlsanity.com/paper/2203.09043/) (2022)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation](https://mlsanity.com/paper/2109.09881/) (2021)
- [DeepWrinkles: Accurate and Realistic Clothing Modeling](https://mlsanity.com/paper/1808.03417/) (2018)
- [Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks](https://mlsanity.com/paper/1709.07592/) (2017)
- [Flow-Grounded Spatial-Temporal Video Prediction from Still Images](https://mlsanity.com/paper/1807.09755/) (2018)
- [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)
- [Stochastic Image-to-Video Synthesis using cINNs](https://mlsanity.com/paper/2105.04551/) (2021)
- [Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks](https://mlsanity.com/paper/1607.02586/) (2016)
- [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021)
- [iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis](https://mlsanity.com/paper/2107.02790/) (2021)
- [Understanding Object Dynamics for Interactive Image-to-Video Synthesis](https://mlsanity.com/paper/2106.11303/) (2021)
- [Deep Detail Enhancement for Any Garment](https://mlsanity.com/paper/2008.04367/) (2020)
- [DeepLandscape: Adversarial Modeling of Landscape Video](https://mlsanity.com/paper/2008.09655/) (2020)
- [Animating Landscape: Self-Supervised Learning of Decoupled Motion and Appearance for Single-Image Video Synthesis](https://mlsanity.com/paper/1910.07192/) (2019)
- [Simulating Fluids in Real-World Still Images](https://mlsanity.com/paper/2204.11335/) (2022)
- [Endless Loops: Detecting and Animating Periodic Patterns in Still Images](https://mlsanity.com/paper/2105.09374/) (2021)

## Built on by (most cited first, 14 of 14)

- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [Generative Image Dynamics](https://mlsanity.com/paper/2309.07906/) (2023)
- [MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model](https://mlsanity.com/paper/2405.20222/) (2024)
- [AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance](https://mlsanity.com/paper/2311.12886/) (2023)
- [Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts](https://mlsanity.com/paper/2403.08268/) (2024)
- [PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://mlsanity.com/paper/2312.13964/) (2023)
- [Text-Guided Synthesis of Eulerian Cinemagraphs](https://mlsanity.com/paper/2307.03190/) (2023)
- [Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models](https://mlsanity.com/paper/2407.15642/) (2024)
- [Motion Modes: What Could Happen Next?](https://mlsanity.com/paper/2412.00148/) (2024)
- [StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN](https://mlsanity.com/paper/2403.14186/) (2024)
- [Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers](https://mlsanity.com/paper/2508.07246/) (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)
- [DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum](https://mlsanity.com/paper/2510.22213/) (2025)

## Abstract (arXiv)

Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images.