GRAIL Installation Guide

GRAIL Installation Guide#

GRAIL has been developed and tested on Ubuntu 22.04+ with NVIDIA GPUs (A6000, RTX 4090, RTX 5090, RTX 6000 Ada).

The repo ships three conda envs:

grail (Python 3.10) — 2D generation, 4D reconstruction, optimization
hunyuan (Python 3.10) — Hunyuan3D-2.1 asset generation
sonic (Python 3.11, optional) — retargeting + task-general tracking training (Isaac Lab + Isaac Sim)

Docker (Recommended)#

The Docker image ships pre-installed system dependencies, CUDA 12.8, and the grail + hunyuan conda envs (Python deps, PyTorch, PyTorch3D, detectron2, CUDA extensions). You only need to bind-mount your repo and run the lightweight setup script to download Blender:

docker pull docker.io/nvgrail/grail:latest
docker run --gpus all -it --shm-size=16g \
    -v /path/to/grail:/workspace/grail \
    docker.io/nvgrail/grail:latest

# Inside the container
cd /workspace/grail
bash scripts/setup/install_env_docker.sh   # downloads Blender (~6.4GB)
bash scripts/setup/download_checkpoints.sh # model checkpoints
bash scripts/setup/download_comasset.sh    # datasets (optional)
conda activate grail

The image does not ship the sonic env. If you need retargeting or training, run bash scripts/setup/install_env_sonic.sh inside the container to install it on top (~20 GB extra).

Install from Scratch#

Quick Start#

# 1. Clone with submodules
git clone --recursive https://github.com/NVlabs/GRAIL.git
cd GRAIL

# If already cloned without --recursive:
git submodule update --init --recursive

# 2. Install everything
bash scripts/setup/install_env_scratch.sh                 # grail + hunyuan
bash scripts/setup/install_env_scratch.sh --install-sonic # + sonic (Isaac Sim/Lab, ~30 GB / ~30 min)

# 3. Activate the environment
#    (install_env_scratch.sh runs `conda init bash`, so start a new shell or source bashrc)
source ~/.bashrc
conda activate grail

# 4. Download checkpoints
bash scripts/setup/download_checkpoints.sh

# 5. Download ComAsset dataset
bash scripts/setup/download_comasset.sh

Environment Setup (Manual)#

Prerequisites#

OS: Ubuntu 20.04+
GPU: NVIDIA GPU with CUDA support (tested: A6000, RTX 4090, RTX 5090, RTX 6000 Ada)
CUDA: toolkit installed at /usr/local/cuda-<version>/ (the scripts default to cu121; use the matching NVIDIA runfile if your system only has apt-installed nvidia-cuda-toolkit, since that layout doesn’t satisfy $CUDA_HOME/{bin,include,lib64}).
Conda: miniconda or miniforge

System Dependencies#

sudo apt-get update
sudo apt-get install -y \
    build-essential cmake g++ gcc git git-lfs wget curl unzip ffmpeg \
    libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev \
    libglfw3-dev libglvnd-dev freeglut3-dev \
    libxrender1 libxi6 libxkbcommon-x11-0 libsm6 libxext6 \
    libosmesa6-dev mesa-utils-extra pkg-config

Boost, Eigen, and gcc-12 ship via conda-forge into the grail / hunyuan envs; no system packages needed.

Python Environment#

conda create -y -n grail python=3.10
conda activate grail
pip install --upgrade pip setuptools wheel

# PyTorch 2.5.1 + CUDA 12.1 (script default; cu128 + torch 2.7.1 also works)
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121

# Build-env C++ toolchain compatible with CUDA 12.1 nvcc (gcc>12 is rejected).
# libboost 1.84 because 1.91 made boost::system header-only, breaking
# FoundationPose's find_package(Boost COMPONENTS system).
conda install -y -c conda-forge \
    'gcc_linux-64=12' 'gxx_linux-64=12' \
    'libboost-devel=1.84' 'libboost=1.84' eigen

# PyTorch3D (build from source — ~5-10 min)
pip install fvcore iopath
FORCE_CUDA=1 pip install "git+https://github.com/facebookresearch/pytorch3d.git" --no-build-isolation

# detectron2 (build from source)
pip install cloudpickle pycocotools
pip install "git+https://github.com/facebookresearch/detectron2.git" --no-build-isolation

# Core dependencies
pip install numpy scipy opencv-python Pillow imageio ffmpeg-python trimesh fast_simplification open3d \
    pyyaml omegaconf einops tqdm scikit-image matplotlib joblib ninja pybind11

# ML dependencies
pip install 'transformers==4.46.0' accelerate safetensors huggingface_hub sentencepiece \
    smplx roma timm kornia wandb tensorboardX lightning hydra-core hydra-zen rich

# Visualization & utilities
pip install scenepic pyrender PyOpenGL PyOpenGL_accelerate av 'moviepy==1.0.3' \
    ultralytics colorama gdown openai rerun-sdk configer natsort \
    wis3d 'sam2==0.4.0' seaborn transformations ruamel.yaml

# Reconstruction-specific deps
pip install chumpy --no-build-isolation  # then patch for numpy compat:
# sed -i 's/from numpy import bool, int, float.*/from numpy import nan, inf/' \
#   $(python -c "import chumpy; print(chumpy.__file__)")
pip install "git+https://github.com/warmshao/WiLoR-mini.git" --no-deps
pip install "git+https://github.com/google/aistplusplus_api.git"

Submodule Setup#

FoundationPose#

# Python deps
pip install pysdf warp-lang
pip install "git+https://github.com/NVlabs/nvdiffrast.git" --no-build-isolation

# Build C++ extension (requires Eigen3, Boost, pybind11 — all in the grail env from above)
cd imports/FoundationPose/mycpp && rm -rf build && mkdir build && cd build
CMAKE_PREFIX_PATH=$(python -c "import pybind11; print(pybind11.get_cmake_dir())") \
    cmake .. -DPYTHON_EXECUTABLE=$(which python) && make -j$(nproc)
cd ../../../..

# Patch and build CUDA extension (required for PyTorch 2.7+)
cd imports/FoundationPose/bundlesdf/mycuda
sed -i 's/std=c++14/std=c++17/g' setup.py
sed -i 's/\.type()/\.scalar_type()/g' common.cu
CPATH=$CONDA_PREFIX/include:$CONDA_PREFIX/include/eigen3 \
    pip install -e . --no-build-isolation
cd ../../../..

GEM-SMPL#

pip install -e imports/GEM-SMPL/ --no-deps
pip install cython_bbox lapx wis3d

# Build DROID-SLAM CUDA extensions
cd imports/GEM-SMPL/third-party/DROID-SLAM
export CUDA_HOME=/usr/local/cuda-12.1
pip install -e . --no-build-isolation
cd ../../../..

GEM-SOMA#

cd imports/GEM-SOMA
git submodule update --init third_party/soma third_party/sam-3d-body
pip install -e third_party/soma --no-deps
cd third_party/soma && git lfs pull && cd ../..
pip install -e . --no-deps
pip install cloudpickle fvcore iopath pycocotools braceexpand 'setuptools<75'
cd ../..

MoGe#

pip install "git+https://github.com/EasternJournalist/utils3d.git@3fab839f0be9931dac7c8488eb0e1600c236e183"
pip install "git+https://github.com/EasternJournalist/pipeline.git@866f059d2a05cde05e4a52211ec5051fd5f276d6"
pip install -e imports/MoGe/ --no-deps

Hunyuan3D-2.1 (Separate Environment)#

Hunyuan3D has conflicting dependency versions and runs in a separate conda environment:

conda create -y -n hunyuan python=3.10
conda activate hunyuan
pip install --upgrade pip 'setuptools<81' wheel   # lightning_fabric needs pkg_resources
conda install -y -c conda-forge 'gcc_linux-64=12' 'gxx_linux-64=12'
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install -r imports/Hunyuan3D-2.1/requirements.txt

# Build extensions
cd imports/Hunyuan3D-2.1/hy3dpaint/custom_rasterizer
pip install --no-build-isolation -e .
cd ../DifferentiableRenderer
bash compile_mesh_painter.sh
cd ../../../..

# Download RealESRGAN weights
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth \
    -P imports/Hunyuan3D-2.1/hy3dpaint/ckpt/

conda activate grail  # return to main env

Sonic (Isaac Lab + Isaac Sim)#

Required only for retargeting and task-general tracking training. The one-shot path:

bash scripts/setup/install_env_sonic.sh      # creates conda env 'sonic' (Python 3.11)

That installs Isaac Sim 5.1.0 + Isaac Lab v2.3.2 + retargeting deps, applies the GRAIL GMR overlay, and pulls imports/SONIC LFS assets. See scripts/setup/sonic_install.md for the step-by-step breakdown if you want to DIY. Time: ~30 min. Disk: ~20 GB. EULA: auto-accepted via OMNI_KIT_ACCEPT_EULA=Yes.

GRAIL Package#

pip install -e .

Prepare Datasets#

We use ComAsset as our main dataset:

bash scripts/setup/download_comasset.sh

Rate-limited for anonymous IPs — if you hit a limit, huggingface-cli login with a read token and retry.

Expected structure:

data/
└── ComAsset/
    ├── accordion/
    │   └── <object_id>/
    │       ├── images/
    │       ├── model.obj
    │       └── model.mtl
    ├── axe/
    └── ...

Other supported datasets: BEHAVE, InterCap, FullBodyManip, SAPIEN.

Download Checkpoints#

bash scripts/setup/download_checkpoints.sh

Downloads GEM-SMPL (~14 GB), GEM-SOMA (~6.4 GB), FoundationPose (~250 MB), RealESRGAN, and SMPL-X body models. SMPL-X lands at imports/GEM-SMPL/inputs/checkpoints/body_models/ — retargeting’s --smplx_model_path default points there directly.

Environment Variables#

# Required
export CUDA_HOME=/usr/local/cuda-12.1
export PYOPENGL_PLATFORM=egl                  # headless rendering
export LD_LIBRARY_PATH=$(python -c "import torch; print(torch.__path__[0])")/lib:$LD_LIBRARY_PATH

# Sonic env only
export OMNI_KIT_ACCEPT_EULA=Yes               # Isaac Sim EULA
export DISPLAY=:1                             # GMR mujoco viewer

# For 2D generation pipeline
export OPENAI_API_KEY=<your-key>
export KLING_ACCESS_KEY=<your-key>
export KLING_SECRET_KEY=<your-key>

# Optional cache paths
export HF_HOME=/path/to/cache/huggingface
export TORCH_HOME=/path/to/cache/torch