# Automatic Animation of Hair Blowing in Still Portrait Photos

arXiv: https://arxiv.org/abs/2309.14207 · HTML: https://mlsanity.com/paper/2309.14207/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 99 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [OneFormer: One Transformer to Rule Universal Image Segmentation](https://mlsanity.com/paper/2211.06220/) (2022)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [Improved Conditional VRNNs for Video Prediction](https://mlsanity.com/paper/1904.12165/) (2019)
- [Video Self-Stitching Graph Network for Temporal Action Localization](https://mlsanity.com/paper/2011.14598/) (2020)
- [Blind Video Temporal Consistency via Deep Video Prior](https://mlsanity.com/paper/2010.11838/) (2020)
- [Photo Wake-Up: 3D Character Animation from a Single Photo](https://mlsanity.com/paper/1812.02246/) (2018)
- [Animating Pictures with Eulerian Motion Fields](https://mlsanity.com/paper/2011.15128/) (2020)
- [A Dynamic Multi-Scale Voxel Flow Network for Video Prediction](https://mlsanity.com/paper/2303.09875/) (2023)
- [ISTR: End-to-End Instance Segmentation with Transformers](https://mlsanity.com/paper/2105.00637/) (2021)
- [Stochastic Image-to-Video Synthesis using cINNs](https://mlsanity.com/paper/2105.04551/) (2021)
- [Controllable Animation of Fluid Elements in Still Images](https://mlsanity.com/paper/2112.03051/) (2021)
- [Region-Aware Face Swapping](https://mlsanity.com/paper/2203.04564/) (2022)
- [iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis](https://mlsanity.com/paper/2107.02790/) (2021)
- [Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images](https://mlsanity.com/paper/2207.14067/) (2022)
- [Face Parsing with RoI Tanh-Warping](https://mlsanity.com/paper/1906.01342/) (2019)
- [NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations](https://mlsanity.com/paper/2205.04175/) (2022)
- [NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation](https://mlsanity.com/paper/2212.00613/) (2022)

## Built on by (most cited first, 13 of 13)

- [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)
- [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)
- [Vivid-ZOO: Multi-View Video Generation with Diffusion Model](https://mlsanity.com/paper/2406.08659/) (2024)
- [PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://mlsanity.com/paper/2312.13964/) (2023)
- [MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation](https://mlsanity.com/paper/2412.05848/) (2024)
- [Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models](https://mlsanity.com/paper/2407.15642/) (2024)
- [Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers](https://mlsanity.com/paper/2508.07246/) (2025)
- [Focal Guidance: Unlocking Controllability from Semantic-Weak Layers in Video Diffusion Models](https://mlsanity.com/paper/2601.07287/) (2026)
- [Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models](https://mlsanity.com/paper/2512.20000/) (2025)
- [Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands](https://mlsanity.com/paper/2607.09811/) (2026)

## Abstract (arXiv)

We propose a novel approach to animate human hair in a still portrait photo. Existing work has largely studied the animation of fluid elements such as water and fire. However, hair animation for a real image remains underexplored, which is a challenging problem, due to the high complexity of hair structure and dynamics. Considering the complexity of hair structure, we innovatively treat hair wisp extraction as an instance segmentation problem, where a hair wisp is referred to as an instance. With advanced instance segmentation networks, our method extracts meaningful and natural hair wisps. Furthermore, we propose a wisp-aware animation module that animates hair wisps with pleasing motions without noticeable artifacts. The extensive experiments show the superiority of our method. Our method provides the most pleasing and compelling viewing experience in the qualitative experiments and outperforms state-of-the-art still-image animation methods by a large margin in the quantitative evaluation. Project url: urlhttps://nevergiveu.github.io/AutomaticHairBlowing/