This is the repo for ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns.
Download checkpoints from here, and put *.pth at ./checkpoints.
Download and extract the SMPL model from http://smplify.is.tue.mpg.de/, and place basicModel_f_lbs_10_207_0_v1.0.0.pkl and basicModel_neutral_lbs_10_207_0_v1.0.0.pkl in the folder of ./smpl_pytorch.
The code is implemented with python 3.8, torch 2.0.1 and cuda 11.8 (other versions usually work as well).
Other dependencies include trimesh, pytorch3D, scipy.
For garment generation:
python infer_isp.py --which tee/pants/skirt --save_path tmp --save_name skirt --res 256 --idx_G 0
For layering inference:
python infer_layering.py
For fitting ISP to 3D garment mesh in rest pose:
python fitting_3D_mesh.py --which tee/pants/skirt --save_path tmp --save_name skirt-fit --res 256
For fitting ISP to images:
python fitting_image.py
The example files are under ./extra-data/fitting-sample/, including the segmentation mask mask.png and the SMPL parameters mocap.pkl. We use Self-Correction-Human-Parsing to produce garment masks, and frankmocap to estimate SMPL parameters.
If you find our work useful, please cite it as:
@inproceedings{Li2023isp,
author = {Li, Ren and Guillard, Benoit and Fua, Pascal},
title = {{ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns}},
booktitle = {Advances in Neural Information Processing Systems},
year = {2023}
}