MANUS-Grasps Dataset

More detailed information coming!!

Full dataset can be used using these commands. raw_videos are the original captured videos from the BRICS capture system. annotationsV1 are the 1st version of annotations for the raw videos. Further annotation version would include improved annotations.

As of now, we are hosting dataset in the AWS. To downlaod the dataset, please use following commands.

aws s3 cp s3://manus-data/raw_videos/ <path_to_destination> --recursive --no-sign-request
aws s3 cp s3://manus-data/annotationsV1/ <path_to_destination> --recursive --no-sign-request

Dataset Info

Raw Videos

There are raw videos for 4 subjects. We showed results for first three subjects, however we release the 4th subject complimentary. Each subject contains the grasp videos from multi-views.

Annotations

There are no annotations for 4th subject as pose detection failed terribly with the gloved hand.

├── {SUBJECT}
    ├── actions_hdf5/ 
    ├── grasps/ 
    ├── evals/ 
    ├── objects/ 
    ├── calib.actions/ 
    ├── calib.evals/ 
    ├── calib.grasps/ (Don't use for grasps. Instead use calib.object for grasps)
    ├── calib.object/ 
    ├── mano_rest.pkl
    ├── mano_rest.ply
    ├── mano_shape.npz

• To optimize object module, we just requires multi-view images and camera parameters. Camera parameters can be found in calib.object folder. optim_params.txt file contains the camera parameters. And images can be found inside object folder.
• To optimize hand module, we use multi-view sequences of different hand poses. It can be found in actions_hdf5 folder.
• evals contain the ground truth contact annotations.
• mano_rest.pkl and mano_rest.ply are the mano parameters fitted to each subject's canonical pose. (Not needed as such.)

How to use MANO?

• Please check scripts/dataset_helpers/load_videos.py file on how to use MANO parameters with raw RGB data.

Pose Estimation

• Please check preprocess/README.md for the pose estimation part of the data.