HaoZhang990127 / TECA

Offical code of TECA: Text-Guided Generation and Editing of Compositional 3D Avatars

TECA: Text-Guided Generation and Editing of Compositional 3D Avatars

Hao Zhang* · Yao Feng* · Peter Kulits · Yandong Wen · Justus Thies · Michael J. Black
* Equal Contribution

3DV 2024

TECA takes text as input combining mesh and NeRF representation methods to generate realistic editable and animtable avatars.

Install

• Tested GPUs: RTX A5000, A100, V100
• Python=3.9, CUDA=11.3, Pytorch=1.12.1

git clone https://github.com/HaoZhang990127/TECA.git
cd TECA

conda env create --file environment.yaml
conda activate teca
# install pytorch3d
pip install git+https://github.com/facebookresearch/pytorch3d.git@v0.7.3
# install cubvh
pip install git+https://github.com/ashawkey/cubvh
# install kaolin
pip install git+https://github.com/NVIDIAGameWorks/kaolin
pip install -r requirements.txt

If you have problems when installing pytorch3d cubvh kaolin, please follow their instructions.

Data

• TECA Data (Required) Unzip it as directory ./data
• Note that, using TECA, you have to register SMPL-X and agree with the LICENSE of it, you can check the LICENSE of SMPL-X from https://github.com/vchoutas/smplx/blob/main/LICENSE.

Usage

Training

# train in the coarse stage
python -m scripts.run_teca --config_path=configs/a_fat_European_woman_with_bob_cut_hairstyle.yaml
# train in the fine-tuning stage, the fine-tuning stage needs a large cuda memory and can set small resolution and query points in *refine.yaml
python -m scripts.run_teca --config_path=configs/a_fat_European_woman_with_bob_cut_hairstyle_refine.yaml

Virtual try-on

# inference for virtual try-on
python -m scripts.run_teca_skinning --config_path=configs/skinning_try_on.yaml

Animation

# inference for animation
python -m scripts.run_teca_animation --config_path=configs/skinning_animation.yaml

Citation

@inproceedings{zhang2024teca,
  title={{TECA: Text-Guided Generation and Editing of Compositional 3D Avatars}},
  author={Zhang, Hao and Feng, Yao and Kulits, Peter and Wen, Yandong, and Thies, Justus and Black, Michael J.},
  booktitle={International Conference on 3D Vision (3DV)},
  year={2024}
}

Related Works

• SCRAF: uses a hybrid method combining mesh and NeRF to reconstruct an animatable avatar from monocular video.
• DreamFusion: enables zero-shot text-driven general 3D object generation using SDS loss.
• Latent-NeRF: enables zero-shot text-driven general 3D object generation using SDS loss and geometric prior in latent space.
• TEXTure: enables zero-shot text-driven 3D object texture generation using Stable Diffusion Inpainting and Stable Diffusion Depth.

License

This code and model are available for non-commercial scientific research purposes as defined in the LICENSE file. By downloading and using the code and model you agree to the terms in the LICENSE.

Disclosure

MJB has received research gift funds from Adobe, Intel, Nvidia, Meta/Facebook, and Amazon. MJB has financial interests in Amazon, Datagen Technologies, and Meshcapade GmbH. While MJB is a part-time employee of Meshcapade, his research was performed solely at, and funded solely by, the Max Planck Society. While TB is part-time employee of Amazon, this research was performed solely at, and funded solely by, MPI.

Contact

For more questions, please contact [e-mail] For commercial licensing, please contact [e-mail]