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eth-ait / GaussianHaircut

Gaussian Haircut: Human Hair Reconstruction with Strand-Aligned 3D Gaussians
https://eth-ait.github.io/GaussianHaircut/
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3d-gaussian-splatting 3d-reconstruction computer-graphics computer-vision digital-humans hair

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Gaussian Haircut: Human Hair Reconstruction with Strand-Aligned 3D Gaussians

Paper | Project Page

This repository contains an official implementation of Gaussian Haircut, a strand-based hair reconstruction approach for monocular videos.

Getting started

  1. Install CUDA 11.8

    Follow the instructions on https://developer.nvidia.com/cuda-11-8-0-download-archive.

    Make sure that

    • PATH includes /bin
    • LD_LIBRARY_PATH includes /lib64

    The environment was tested only with this CUDA version.

  2. Install Blender 3.6 in order to create strand visualizations

    Follow instructions on https://www.blender.org/download/lts/3-6.

  3. Close the repo and run the install script

    git clone git@github.com:eth-ait/GaussianHaircut.git
cd GaussianHaircut
chmod +x ./install.sh
./install.sh

Reconstruction

  1. Record a monocular video

    Use examples on the project page as references and introduce as little motion blur as possible.

  2. Setup a directory for the reconstructed scene

    Put the video file in it and rename it to raw.mp4

  3. Run the script

    export PROJECT_DIR="[/path/to/]GaussianHaircut"
export BLENDER_DIR="[/path/to/blender/folder/]blender"
DATA_PATH="[path/to/scene/folder]" ./run.sh

    The script performs data pre-processing, reconstruction, and final visualization using Blender. Use Tensorboard to see intermediate visualizations.

License

This code is based on the 3D Gaussian Splatting project. For terms and conditions, please refer to LICENSE_3DGS. The rest of the code is distributed under CC BY-NC-SA 4.0.

If this code is helpful in your project, cite the papers below.

Citation

@inproceedings{zakharov2024gh,
   title = {Human Hair Reconstruction with Strand-Aligned 3D Gaussians},
   author = {Zakharov, Egor and Sklyarova, Vanessa and Black, Michael J and Nam, Giljoo and Thies, Justus and Hilliges, Otmar},
   booktitle = {European Conference of Computer Vision (ECCV)},
   year = {2024}
}

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