NeuralHDHair

The source code of the networks for our paper "NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations" (CVPR2022)

Pipeline

Prerequisites

• Linux
• Python 3.8
• Pytorch 1.8.1
• NVIDIA GPU + CUDA 11.1

Train

1.Train Coarse module

python main.py --name=yourname --model_name=HairSpatNet --blur_ori --no_use_depth --no_use_L --gpu_ids=0 --batch_size=1

2.Train Global module

python main.py --name=yourname --model_name=HairModelingHD --blur_ori --no_use_depth  --gpu_ids=0 --batch_size=1 --pretrain_path=pretrain_model_path

3.Train GrowingNet

python main.py --name=yourname --model_name=GrowingNet  --batch_size=1 --sd_per_batch=800 --pt_per_strand 72

Tips

1.Data, some data processing and rendering code do not have permissions and cannot open source temporarily.

2.Use high-quality images as much as possible, the quality of reconstruction depends largely on the quality of generated orientation map.

3.The hair should be aligned with the bust as much as possible, you may need the face alignment algorithm to calculate the affine transformation.(data/Train_input/DB1 contains standard input samples)

Related works

The latest work MonoHair

For Dynamic hair modeling DynamicHair

Citation

If you find this useful for your research, please cite the following papers.

@inproceedings{wu2022neuralhdhair,
title={NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations},
author={Wu, Keyu and Ye, Yifan and Yang, Lingchen and Fu, Hongbo and Zhou, Kun and Zheng, Youyi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={1526--1535},
year={2022}
}

@article{yang2019dynamic,
  title={Dynamic hair modeling from monocular videos using deep neural networks},
  author={Yang, Lingchen and Shi, Zefeng and Zheng, Youyi and Zhou, Kun},
  journal={ACM Transactions on Graphics (TOG)},
  volume={38},
  number={6},
  pages={1--12},
  year={2019},
}