Dynamic Hair Modeling

The source code of the networks for our paper "Dynamic hair modeling from monocular videos using deep neural networks" (SIGGRAPH ASIA 2019)

We propose a novel framework for dynamic hair modeling from monocular videos. We use two networks HairSpatNet and HairTempNet to separately predict hair geometry and hair motion. The entire framework is as follows:

Improvments

• For HairSpatNet, we removed instance normalization and the discriminator to speed up training process and reduce memory cost. We found that the fine-grained details imposed by the discriminator would be obliterated by the space-time optimization afterwards.
• For motion prediction, we redesigned a network named HairWarpNet to directly predict flow based on the 3D fields (similar to the regression of optical flow). It is more reasonable and achieves better results than HairTempNet.
• There are more designs of toVoxel modules.
• You can check other research directions in folder OtherResearch.

Prerequisites

• Linux
• Python 3.6
• NVIDIA GPU + CUDA 10.0 + cuDNN 7.5
• tensorflow-gpu 1.13.1

Getting Started

• Conda installation:

  # 1. Create a conda virtual environment.
  conda create -n dhair python=3.6 -y
  conda activate dhair
  
  # 2. Install dependency
  pip install -r requirement.txt

• You can run the scripts in the Script folder to train/test your models.

Related works

• The latest work MonoHair

• For single-view modeling NeuralHDHair

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},
}