TADA! Text to Animatable Digital Avatars
Tingting Liao*
路
Hongwei Yi*
路
Yuliang Xiu
路
Jiaxiang Tang
路
Yangyi Huang
路
Justus Thies
路
Michael J. Black
* Equal Contribution
3DV 2024
TADA takes text as input and produce holistic animatable 3D avatars with high-quality geometry and texture. It enables creation of large-scale digital character assets that are ready for animation and rendering, while also being easily editable through natural language.
NEWS (2023.9.24):
- Using Omnidata normal prediction model to improve the normal&image consistency.
with_normal_supervision.mp4
wo_normal_supervision_ep0150.mp4
Install
- System requirement: Unbuntu 20.04
- Tested GPUs: RTX4090, A100, V100
- Compiler: gcc-7.5 / g++-7.5
- Python=3.9, CUDA=11.5, Pytorch=1.12.1
git clone [email protected]:TingtingLiao/TADA.git
cd TADA
conda env create --file environment.yml
conda activate tada
pip install -r requirements.txt
cd smplx
python setup.py install
# download omnidata normal and depth prediction model
mkdir data/omnidata
cd data/omnidata
gdown '1Jrh-bRnJEjyMCS7f-WsaFlccfPjJPPHI&confirm=t' # omnidata_dpt_depth_v2.ckpt
gdown '1wNxVO4vVbDEMEpnAi_jwQObf2MFodcBR&confirm=t' # omnidata_dpt_normal_v2.ckpt
Data
- SMPL-X Model (Need to register, download the SMPLX_NEUTRAL_2020.npz and put it into ./data/smplx/)
- TADA Extra Data (Required) Unzip it as directory ./data
- TADA 100 Characters (Optional)
- Optional Motion Data
Please consider cite AIST, AIST++, TalkSHOW, MotionDiffusion if they also help on your project
@inproceedings{aist-dance-db,
author = {Shuhei Tsuchida and Satoru Fukayama and Masahiro Hamasaki and Masataka Goto},
title = {AIST Dance Video Database: Multi-genre, Multi-dancer, and Multi-camera Database for Dance Information Processing},
booktitle = {Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR) },
year = {2019},
month = {Nov}
}
@inproceedings{li2021learn,
title={AI Choreographer: Music Conditioned 3D Dance Generation with AIST++},
author={Ruilong Li and Shan Yang and David A. Ross and Angjoo Kanazawa},
year={2021},
booktitle={ICCV}
}
@inproceedings{yi2023generating,
title={Generating Holistic 3D Human Motion from Speech},
author={Yi, Hongwei and Liang, Hualin and Liu, Yifei and Cao, Qiong and Wen, Yandong and Bolkart, Timo and Tao, Dacheng and Black Michael J},
booktitle={CVPR},
pages={469-480},
month={June},
year={2023}
}
@inproceedings{tevet2023human,
title={Human Motion Diffusion Model},
author={Guy Tevet and Sigal Raab and Brian Gordon and Yoni Shafir and Daniel Cohen-or and Amit Haim Bermano},
booktitle={ICLR},
year={2023},
url={https://openreview.net/forum?id=SJ1kSyO2jwu}
}
Usage
Training
The results will be saved in $workspace. Please change it in the config/*.yaml files.
# single prompt training
python -m apps.run --config configs/configs/tada_w_dpt.yaml.yaml --text "Aladdin in Aladdin"
# multiple prompts training
bash scripts/run.sh data/prompt/fictional.txt 1 10 configs/tada.yaml
Animation
- Download AIST or generate motions from TalkShow and MotionDiffusion.
python -m apps.anime --subject "Abraham Lincoln" --res_dir your_result_path
Tips
- Using an appropriate learning rate for SMPL-X shape is important to learn accurate shape.
- Omnidata normal supervision can effectively enhance the overall geometry and texture consistency; however, it demands more time for optimization.
Other Interesting Works
- HumanNorm: multiple stage SDS loss and perceptual loss can help generate the lifelike texture.
Citation
@inproceedings{liao2024tada,
title={{TADA! Text to Animatable Digital Avatars}},
author={Liao, Tingting and Yi, Hongwei and Xiu, Yuliang and Tang, Jiaxiang and Huang, Yangyi and Thies, Justus and Black, Michael J.},
booktitle={International Conference on 3D Vision (3DV)},
year={2024}
}
License
This code and model are available for non-commercial scientific research purposes as defined in the LICENSE (i.e., MIT LICENSE). Note that, using TADA, you have to register SMPL-X and agree with the LICENSE of it, and it's not MIT LICENSE, you can check the LICENSE of SMPL-X from https://github.com/vchoutas/smplx/blob/main/LICENSE; Enjoy your journey of exploring more beautiful avatars in your own application.