PyTorch implementation of the paper "Neural-ABC: Neural Parametric Models for Articulated Body with Clothes". This repository contains the training and inference code, data.
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We proposed Neural-ABC, a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose.
Neural-ABC is a neural implicit parametric model with latent spaces of human identity, clothing, shape and pose. It can generate various human identities and different clothes. The clothed human body can deform into different body shapes and poses.
This code has been tested on Tesla V100.
Environment:
- Ubuntu 20.04
- python 3.8.17
Our default, provided install method is:
conda env create -f environment.yml
conda activate NeuralABC
pip install -r requirements.txt
If you have problems when installing pytorch3d, please follow their instructions.
Build and install meshudf:
cd meshudf
source setup.sh
If you have problems when installing meshudf, please follow their instructions.
Download the female SMPL model from http://smplify.is.tue.mpg.de/ and place basicModel_f_lbs_10_207_0_v1.0.0.pkl in the folder of ./smpl_pytorch.
Download the trained model from here and place it in the folder of ./checkpoints.
Neural-ABC can generate clothed human bodies with decoupled attributes:
cd script
python generate.py
Identity, clothing, body shape, and actions can all be independently modified. Since the parameter space of Neural-ABC is continuous, the modified attributes can be continuous. The -type option includes id, cloth, shape, and pose.
python interpolate.py
If you find our paper useful for your work please cite:
@article{Chen2024NeuralABC,
title={Neural-ABC: Neural Parametric Models for Articulated Body with Clothes},
author={Honghu Chen, Yuxin Yao, and Juyong Zhang},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024}，
publisher={IEEE}
}
For more questions, please contact honghuc@mail.ustc.edu.cn
Our data is processed with the help of StereoPIFu, DrapeNet and MeshUDF:
@inproceedings{yang2021stereopifu,
author = {Yang Hong and Juyong Zhang and Boyi Jiang and Yudong Guo and Ligang Liu and Hujun Bao},
title = {StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision},
booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}
@inproceedings{de2023drapenet,
author = {De Luigi, Luca and Li, Ren and Guillard, Benoit and Salzmann, Mathieu and Fua, Pascal},
title = {{DrapeNet: Garment Generation and Self-Supervised Draping}},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2023}
}
@inproceedings{guillard2022udf,
author = {Guillard, Benoit and Stella, Federico and Fua, Pascal},
title = {MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks},
booktitle = {European Conference on Computer Vision},
year = {2022}
}