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-# NeuralHaircut
+# Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction
+
+
+
+[**Paper**](https://arxiv.org/abs/2306.05872) | [**Project Page**](https://samsunglabs.github.io/NeuralHaircut/)
+
+
+
+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: 
+
+```bash
+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](https://github.com/crowsonkb/k-diffusion), [NeuS](https://github.com/Totoro97/NeuS), [MODNet](https://github.com/ZHKKKe/MODNet), [CDGNet](https://github.com/tjpulkl/CDGNet), [npbgpp](https://github.com/rakhimovv/npbgpp). Download pretrained weights for [CDGNet](https://github.com/tjpulkl/CDGNet) and [MODNet](https://github.com/ZHKKKe/MODNet).
+
+```bash
+git submodule update --init --recursive
+```
+
+
+```bash
+cd npbgpp && python setup.py build develop
+cd ..
+```
+
+
+Download the pretrained NeuralHaircut models:
+
+```bash
+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](./src/multiview_optimization)
+
+
+### Launching the first stage on [H3ds dataset](https://github.com/CrisalixSA/h3ds) or custom monocular dataset:
+
+
+
+```bash
+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: 
+
+```bash
+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](./preprocess_custom_data).
+
+
+- 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](https://github.com/CrisalixSA/h3ds) or custom monocular dataset:
+
+
+```bash
+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:
+
+```bash
+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.](./preprocess_custom_data).
+
+You could run the scripts on our [monocular scene](./example) 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](https://github.com/Totoro97/NeuS). Also we acknowledge additional projects that were essential and speed up the developement.
+
+- [NeuS](https://github.com/Totoro97/NeuS) for geometry reconstruction;
+
+- [npbgpp](https://github.com/rakhimovv/npbgpp) for rendering of soft rasterized features;
+
+- [k-diffusion](https://github.com/crowsonkb/k-diffusion) for diffusion network;
+
+- [MODNet](https://github.com/ZHKKKe/MODNet), [CDGNet](https://github.com/tjpulkl/CDGNet) used to obtain silhouette and hair segmentations;
+
+- [PIXIE](https://github.com/yfeng95/PIXIE) used to obtain initialization for shape and pose parameters;
+
+## Citation
+
+
+
+
+
+