GraspXL: Generating Grasping Motions for Diverse Objects at Scale

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This is a repository for the visualization of GraspXL Dataset. The repository is based on arctic-digit, which is used for the visualization of ARCTIC dataset.

Our dataset contains diverse grasping motions of 500k+ objects with different dexterous hands:

Getting started

Clone the GraspXL_visualization repository:

$ git clone https://github.com/zdchan/GraspXL_visualization.git
$ cd GraspXL_visualization

Install the dependencies listed in environment.yaml

$ conda env create -f environment.yaml
$ conda activate graspxl_viewer

Download MANO pickle data-structures

• Visit MANO website
• Create an account by clicking Sign Up and provide your information
• Download Models and Code (the downloaded file should have the format mano_v*_*.zip). Note that all code and data from this download falls under the MANO license.
• unzip and copy the contents in mano_v*_*/models/ to the data/body_models/mano folder
• Your data/body_models/mano folder structure should look like this:

data/body_models/mano
   ├── info.txt
   ├── LICENSE.txt
   ├── MANO_LEFT.pkl
   ├── MANO_RIGHT.pkl
   ├── SMPLH_female.pkl
   └── SMPLH_male.pkl

You can now run the grasping visualization scripts for MANO, Allegro, or Shadow Hand in the ./scripts folder. For example, if you want to visualize a MANO grasping sequence, run

$ python ./scripts/visualizer_mano.py

We use a wine glass as an example. If you want to visualize another object or another sequence, put the object mesh (.obj file) in ./data/GraspXL/object_mesh/ and the sequence in ./data/GraspXL/recorded/, and run

$ python ./scripts/visualizer_mano.py --seq_name <sequence name> --obj_name <object name>

Citation

@inProceedings{zhang2024graspxl,
  title={{GraspXL}: Generating Grasping Motions for Diverse Objects at Scale},
  author={Zhang, Hui and Christen, Sammy and Fan, Zicong and Hilliges, Otmar and Song, Jie},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024}
}

Our paper benefits a lot from aitviewer. If you find our viewer useful, to appreciate their hard work, consider citing:

@software{kaufmann_vechev_aitviewer_2022,
  author = {Kaufmann, Manuel and Vechev, Velko and Mylonopoulos, Dario},
  doi = {10.5281/zenodo.1234},
  month = {7},
  title = {{aitviewer}},
  url = {https://github.com/eth-ait/aitviewer},
  year = {2022}
}