maddigit / neuralhaircut

Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction

Paper | Project Page

This repository contains official inference code for Neural Haircut.

This code helps you to create strand-based hairstyle using multi-view images or monocular video.

Getting started

Clone the repository and install requirements:

git clone https://github.sec.samsung.net/v-sklyarova/NeuralHaircut.git
cd NeuralHaircut
conda env create -n neuralhaircut -f neural_haircut.yaml
conda activate neuralhaircut

Initialize submodules of k-diffusion, NeuS, MODNet, CDGNet, npbgpp. Download pretrained weights for CDGNet and MODNet.

git submodule update --init --recursive

cd npbgpp && python setup.py build develop
cd ..

Download the pretrained NeuralHaircut models:

gdown --folder https://drive.google.com/drive/folders/1TCdJ0CKR3Q6LviovndOkJaKm8S1T9F_8

Running

Fitting the FLAME coarse geometry using multiview images

More details could be find in multiview_optimization

Launching the first stage on H3ds dataset or custom monocular dataset:

python run_geometry_reconstruction.py --case CASE --conf ./configs/SCENE_TYPE/neural_strands.yaml --exp_name first_stage_SCENE_TYPE_CASE

where SCENE_TYPE = [h3ds|monocular].

• If you want to add camera fitting:

python run_geometry_reconstruction.py --case CASE --conf ./configs/SCENE_TYPE/neural_strands_w_camera_fitting.yaml --exp_name first_stage_SCENE_TYPE_CASE --train_cameras

At the end of first stage do the following steps.

• If you want to continue from checkpoint add flag --is_continue.

• If you want to obtain mesh in higher resolution add flags --is_continue --mode validate_mesh.

Launching the second stage on H3ds dataset or custom monocular dataset:

python run_strands_optimization.py --case CASE --scene_type SCENE_TYPE --conf ./configs/SCENE_TYPE/neural_strands.yaml  --hair_conf ./configs/hair_strands_textured.yaml --exp_name second_stage_SCENE_TYPE_CASE

• If during the first stage you also fitted the cameras, then use the following:

python run_strands_optimization.py --case CASE --scene_type SCENE_TYPE --conf ./configs/SCENE_TYPE/neural_strands_w_camera_fitted.yaml  --hair_conf ./configs/hair_strands_textured.yaml --exp_name second_stage_SCENE_TYPE_CASE

Train NeuralHaircut with your custom data

More information can be found in preprocess_custom_data..

You could run the scripts on our monocular scene for convenience.

License

This code and model are available for scientific research purposes as defined in the LICENSE.txt file. By downloading and using the project you agree to the terms in the LICENSE.txt.

Links

This work is based on the great project NeuS. Also we acknowledge additional projects that were essential and speed up the developement.

• NeuS for geometry reconstruction;

• npbgpp for rendering of soft rasterized features;

• k-diffusion for diffusion network;

• MODNet, CDGNet used to obtain silhouette and hair segmentations;

• PIXIE used to obtain initialization for shape and pose parameters;

Citation

Cite as below if you find this repository is helpful to your project:

@inproceedings{sklyarova2023neural_haircut,
title = {Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction},
author = {Sklyarova, Vanessa and Chelishev, Jenya and Dogaru, Andreea and Medvedev, Igor and Lempitsky, Victor and Zakharov, Egor},
booktitle = {Proceedings of IEEE International Conference on Computer Vision (ICCV)},
year = {2023}
}