[PDF] | [OpenReview] | [arXiv] | [Project Page]

This is the codebase for our NeurIPS 2021 paper Garment4D: Garment Reconstruction from Point Cloud Sequences.

For further information, please contact Fangzhou Hong.

• 2021-12 Code release!
• 2021-09 Garment4D is accepted to NeurIPS 2021.

The code has been tested with Python 3.7.9, PyTorch 1.5.0, CUDA 10.1. Other required packages include:

• tqdm
• loguru
• yaml
• easydict
• numpy
• scipy
• chamferdist
• openmesh
• pytorch_scatter

Other than the above packages, please also compile the cuda kernels required by PointNet++ by executing python setup.py install under modules/pointnet2/pointnet2.

Please register and download SMPL model files here. Then please put the model files at smplx/model. The folder structure should be like

./
├── 
├── ...
└── smplx/
    ├── models/
        ├── smpl/
            ├── SMPL_MALE.pkl
            ├── SMPL_FEMALE.pkl
            └── SMPL_NEUTRAL.pkl
    ├── ...

Please download the processed CLOTH3D dataset using the following links. Due the size of the whole dataset is big (~160 GB), we split the archieve into several 10 GB chunks. You could combine them by cat CLOTH3D.z* > merged_CLOTH3D.zip and unzip it and put it under dataset.

The folder structure should look like

./
├── 
├── ...
└── dataset/
    ├──CLOTH3D/
        ├── CLOTH3D/
        ├── CLOTH3D_template/
        ├── list/
        ├── Skirt_reg/
        ├── Trousers_reg/
        └── Tshirt_reg/

Please download the pre-train models using the following links and put them under pretrain.

The folder structure should look like

./
├── 
├── ...
└── pretrain/
    ├── skirt.ckpt
    ├── trousers.ckpt
    └── tshirt.ckpt

Please checkout the scripts/test folder for the testing scripts. One should be able to run the corresponding testing scripts with the pre-train models and dataset setup correctly.

Please checkout the scripts/train folder for the training scripts. We currently support three types of garments i.e. skirts, Tshirts and trousers. Take skirts training as an example, please run the train_skirt_canonical.sh first for the canonical garment reconstruction and then run the train_skirt_posed.sh for the posed garment reconstruction.

Distributed under the MIT License. See LICENSE for more information.

If you find our work useful in your research, please consider citing the following papers:

@inproceedings{
    hong2021garmentd,
    title={Garment4D: Garment Reconstruction from Point Cloud Sequences},
    author={Fangzhou Hong and Liang Pan and Zhongang Cai and Ziwei Liu},
    booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
    year={2021},
    url={https://openreview.net/forum?id=aF60hOEwHP}
}

In our implementation, we refer to the following open-source databases:

• PointNet2.PyTorch
• pygcn
• smplx