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Requirements

Hardware Requirements

  • CUDA-ready GPU with Compute Capability 7.0+
  • 11 GB VRAM (we used RTX 2080Ti)

Software Requirements

  • Conda (recommended for easy setup)
  • C++ Compiler for PyTorch extensions (we used Visual Studio for Windows, GCC for Linux)
  • CUDA SDK for PyTorch extensions, install after Visual Studio or GCC
  • C++ Compiler and CUDA SDK must be compatible
  • FFMPEG to create result videos

Additional python packages

  • RoMa (for rotation representations)
  • DearPyGUI (for viewer interface)
  • NVDiffRast (for mesh rendering in viewer)

Tested Platforms

PyTorch Version CUDA version Linux Windows (VS2022) Windows (VS2019)
2.0.1 11.7.1 Pass Fail to compile Pass
2.2.0 12.1.1 Pass Pass Pass

Installation

Our default installation method is based on Conda package and environment management:

1. Create conda environment and install CUDA

git clone https://github.com/ShenhanQian/GaussianAvatars.git --recursive
cd GaussianAvatars

conda create --name gaussian-avatars -y python=3.10
conda activate gaussian-avatars

# Install CUDA and ninja for compilation
conda install -c "nvidia/label/cuda-11.7.1" cuda-toolkit ninja  # use the right CUDA version

2. Setup paths

For Linux

ln -s "$CONDA_PREFIX/lib" "$CONDA_PREFIX/lib64"  # to avoid error "/usr/bin/ld: cannot find -lcudart"
conda env config vars set CUDA_HOME=$CONDA_PREFIX  # for compilation

For Windows with PowerShell

conda env config vars set CUDA_PATH="$env:CONDA_PREFIX"  

## Visual Studio 2022 (modify the version number `14.39.33519` accordingly)
conda env config vars set PATH="$env:CONDA_PREFIX\Script;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.39.33519\bin\Hostx64\x64;$env:PATH"
## or Visual Studio 2019 (modify the version number `14.29.30133` accordingly)
conda env config vars set PATH="$env:CONDA_PREFIX\Script;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\HostX86\x86;$env:PATH" 

# re-activate the environment to make the above eonvironment variables effective
conda deactivate
conda activate gaussian-avatars

For Windows with Command Prompt

conda env config vars set CUDA_PATH=%CONDA_PREFIX%

## Visual Studio 2022 (modify the version number `14.39.33519` accordingly)
conda env config vars set PATH="%CONDA_PREFIX%\Script;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.39.33519\bin\Hostx64\x64;%PATH%"
## or Visual Studio 2019 (modify the version number `14.29.30133` accordingly)
conda env config vars set PATH="%CONDA_PREFIX%\Script;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\HostX86\x86;%PATH%"

# re-activate the environment to make the above eonvironment variables effective
conda deactivate
conda activate gaussian-avatars

3. Install PyTorch and other packages

# Install PyTorch (make sure that the CUDA version matches with "Step 1")
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu117
# or
conda install pytorch torchvision pytorch-cuda=11.7 -c pytorch -c nvidia
# make sure torch.cuda.is_available() returns True

# Install the rest packages (can take a while to compile diff-gaussian-rasterization, simple-knn, and nvdiffrast)
pip install -r requirements.txt