wildavatar / WildAvatar_Toolbox

[ArXiv 2024] WildAvatar: Web-scale In-the-wild Video Dataset for 3D Avatar Creation

WildAvatar: Web-scale In-the-wild Video Dataset for 3D Avatar Creation

Zihao Huang¹  Shoukang Hu²  Guangcong Wang³  Tianqi Liu¹
Yuhang Zang⁴  Zhiguo Cao¹  Wei Li²  Ziwei Liu²

¹Huazhong University of Science and Technology  ²S-Lab, Nanyang Technological University
³Great Bay University  ⁴Shanghai AI Laboratory

TL;DR: WildAvatar is a large-scale dataset from YouTube with 10,000+ human subjects, designed to address the limitations of existing laboratory datasets for avatar creation.

🔨 Environments

conda create -n wildavatar python=3.9
conda activate wildavatar
pip install -r requirements.txt
pip install pyopengl==3.1.4

📦 Prepare Dataset

1. Download WildAvatar.zip
2. Put the WildAvatar.zip under ./data/WildAvatar/.
3. Unzip WildAvatar.zip
4. Install yt-dlp
5. Download and Extract images from YouTube, by running

python prepare_data.py --ytdl ${PATH_TO_YT-DLP}$

📊 Visualization

1. Put the SMPL_NEUTRAL.pkl under ./assets/.
2. Run the following script to visualize the smpl overlay of the human subject of ${youtube_ID}

python vis_smpl.py --subject "${youtube_ID}"

3. The SMPL mask and overlay visualization can be found in data/WildAvatar/${youtube_ID}/smpl and data/WildAvatar/${youtube_ID}/smpl_masks

For example, if you run

python vis_smpl.py --subject "__-ChmS-8m8"

The SMPL mask and overlay visualization can be found in data/WildAvatar/__-ChmS-8m8/smpl and data/WildAvatar/__-ChmS-8m8/smpl_masks

🎯 Using WildAvatar

For training and testing on WildAvatar, we currently provide the adapted code for HumanNeRF and GauHuman.

📝 Citation

If you find our work useful for your research, please cite our paper.

    @article{huang2024wildavatar,
    title={WildAvatar: Web-scale In-the-wild Video Dataset for 3D Avatar Creation},
    author={Huang, Zihao and Hu, ShouKang and Wang, Guangcong and Liu, Tianqi and Zang, Yuhang and Cao, Zhiguo and Li, Wei and Liu, Ziwei},
    journal={arXiv preprint arXiv:2407.02165},
    year={2024}
    }

😃 Acknowledgement

This project is built on source codes shared by GauHuman, HumanNeRF, and CLIFF. Many thanks for their excellent contributions!

📧 Contact

If you have any questions, please feel free to contact Zihao Huang (zihaohuang at hust.edu.cn).