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--- |
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license: mit |
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task_categories: |
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- image-to-video |
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language: |
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- en |
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tags: |
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- diffusion |
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- generation |
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- human |
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- animation |
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size_categories: |
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- 10K<n<100K |
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--- |
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## Data for Human Image Animation |
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<img src="https://github.com/user-attachments/assets/01174ec4-c076-4947-966c-01d511d0383e"> |
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## 🎏 Introduction |
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<b>TL; DR: With the rapid developments in generative models, including the diffusion-based or the flow-based models, the human-centric tasks, like pose-driven human image animation, audio-driven action generation, diffusion-based pose estimation, human optical estimation, etc., have attracted a lot of attention from lots of works. |
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We pay attention to the quality of the training data of human data for these tasks. However, due to the lack of high-quality datasets, especially for the human image animation, we find it is hard to collect videos from existing public datasets, while these videos have these characteristics: |
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1. High-resolution: the resolution of the vertical video is larger than 1080 * 576. |
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2. High-dynamic: the video is vivid and suitable to learn human motions. |
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3. Dancing-style: In this stage, we focus on the human animation task and mainly collect videos like TikTok styles. |
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## ⚔️ What we do |
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We collect a large number of videos from the internet. After filtering low-quality, limited motion, and bad frames, we get 25,000 videos in this repo. Now we provide a visualization to these data and the corresponding pose data, you can check each training video in our work. |
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Notice: we do not allow any commercial usage of these videos and you must delete them within 24 hours after downloading. |
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Tips: If you find that your data is being infringed upon, please contact us immediately to request its removal. |
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## 📍 Citation |
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If you find this guidance helpful, please consider citing: |
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``` |
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@article{zhao2025dynamictrl, |
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title={DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation}, |
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author={Haoyu, Zhao and Zhongang, Qi and Cong, Wang and Qingping, Zheng and Guansong, Lu and Fei, Chen and Hang, Xu and Zuxuan, Wu}, |
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year={2025}, |
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journal={arXiv:2503.21246}, |
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} |
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``` |
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