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},
}