Dimitrije Antić   Garvita Tiwari   Batuhan Ozcomlekci   Riccardo Marin   Gerard Pons-Moll

The code was tested under Ubuntu 22.04, Python 3.9, CUDA 11.6, Pytorch 1.13.0 Use the following command to create a conda environment with all the required dependencies:

git clone --recursive https://github.com/anticdimi/CloSe.git
cd CloSe
conda env create -f env.yml
conda activate close

To build the custom Open3D extension needed to run the CloSeT, see the instructions in docs/CloSeT.md.

If the environment setup fails, please follow instructions on how to install Pytorch3D here, and install PyTorch from here.

The steps for downloading the dataset are described in docs/dataset.md.

The pretrained models can be downloaded from this link in the folder CloSeNet/. After downloading, place the models in the ./pretrained folder.

After setting up the environment and downloading the pretrained models, you can run the inference on the provided example scans using the following command:

python demo.py --render

And the results will be saved in the ./out folder.

See the prep_scan.py script to see how the data is prepared for inference.

For training CloSeNet model, you can use the following command:

python train_closenet.py cfg/closenet.yaml

See config file for more detail abot the training setup.

The steps for installing and using the interactive tool is described in docs/CloSeT.md.

If you find this work useful, please consider citing:

@inproceedings{antic2024close,
    title = {CloSe: A 3D Clothing Segmentation Dataset and Model},
    author = {Antic, Dimitrije and Tiwari, Garvita and Ozcomlekci, Batuhan  and Marin, Riccardo  and Pons-Moll, Gerard},
    booktitle = {International Conference on 3D Vision (3DV)},
    month = {March},
    year = {2024},
}