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GRAB is a dataset of full-body motions interacting and grasping 3D objects. It contains accurate finger and facial motions as well as the contact between the objects and body. It contains 5 male and 5 female participants and 4 different motion intents.

Eat - Banana	Talk - Phone	Drink- Mug	See - Binoculars

Check out the YouTube video below for more details.

Short Video	Long Video

• Description
• Getting Started
• Installation
• Examples
• Citation
• License
• Acknowledgments
• Contact

This repository Contains:

• Code to preprocess and prepare the GRAB data
• Tools to extract 3D vertices and meshes of the body, hands, and object
• Visualizing and rendering GRAB sequences

Inorder to use GRAB dataset please follow the below steps:

• Download the grab dataset from this website and put it in the following structure:

    GRAB
    ├── grab
    │   │
    │   ├── s1
    │   └── s2
    │   └── ...
    │   └── s9
    │   └── s10
    │  
    └── tools
    │    │
    │    ├── object_meshes
    │    └── object_settings
    │    └── subject_meshes
    │    └── subject_settings
    │    └── smplx_correspondence
    │  
    └── mocap (optional)

• Follow the instructions on the SMPL-X website to download SMPL-X and MANO models.
• Install this repo to process, visualize, and render the data.

To install the model please follow the next steps:

1. Clone this repository and install the requirements:

git clone https://github.com/otaheri/GRAB

2. Install the dependencies by the following command:

pip install -r requirements.txt

• Processing the data

  After installing the GRAB package and downloading the data and the models from smplx website, you should be able to run the grab_preprocessing.py

  python grab/grab_preprocessing.py --grab-path $GRAB_DATASET_PATH \
                                    --model-folder $SMPLX_MODEL_FOLDER \
                                    --out_path $PATH_TO_SAVE_DATA

• Get 3D vertices (or meshes) for GRAB

  In order to extract and save the vertices of the body, hands, and objects in the dataset, you can run the get_grab_vertices.py

  python grab/save_grab_vertices.py --grab-path $GRAB_DATASET_PATH \
                                   --model-folder $SMPLX_MODEL_FOLDER

• Visualizing and rendering 3D interactive meshes

  To visualize and interact with GRAB 3D meshes, run the examples/visualize_grab.py

  python examples/visualize_grab.py --grab-path $GRAB_DATASET_PATH \
                                    --model-folder $SMPLX_MODEL_FOLDER

  To render the meshes and save images in a folder please run the examples/render_grab.py

  python examples/render_grab.py --grab-path $GRAB_DATASET_PATH \
                                  --model-folder $SMPLX_MODEL_FOLDER \
                                  --render_path $PATH_TO_SAVE_RENDERINGS

@inproceedings{GRAB:2020,
  title = {{GRAB}: A Dataset of Whole-Body Human Grasping of Objects},
  author = {Taheri, Omid and Ghorbani, Nima and Black, Michael J. and Tzionas, Dimitrios},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2020},
  url = {https://grab.is.tue.mpg.de}
}

Software Copyright License for non-commercial scientific research purposes. Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use the GRAB data, model and software, (the "Data & Software"), including 3D meshes (body and objects), images, videos, textures, software, scripts, and animations. By downloading and/or using the Data & Software (including downloading, cloning, installing, and any other use of the corresponding github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Data & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.

Special thanks to Mason Landry for his invaluable help with this project.

We thank S. Polikovsky, M. Hoschle (MH) and M. Landry (ML) for the MoCap facility. We thank F. Mattioni, D. Hieber, and A. Valis for MoCap cleaning. We thank ML and T. Alexiadis for trial coordination, MH and F. Grimminger for 3D printing, V. Callaghan for voice recordings and J. Tesch for renderings. We thank Sai Kumar Dwivedi and Nikos Athanasiou for proofreading.

The code of this repository was implemented by Omid Taheri.

For questions, please contact [email protected].

For commercial licensing (and all related questions for business applications), please contact [email protected].