metadata
license: mit
pretty_name: garment-tracking
Dataset Card for VR-Folding Dataset
Table of Contents
Dataset Description
- Homepage: https://garment-tracking.robotflow.ai
- Repository: GitHub
- Paper: GarmentTracking: Category-Level Garment Pose Tracking
- Point of Contact:
Dataset Summary
This is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking.
This dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity.
To download the dataset, use the following shell snippet:
git lfs install
git clone https://huggingface.co/datasets/robotflow/garment-tracking
# if you want to clone without large files β just their pointers
# prepend your git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
# merge multiple .zip files (e.g. folding) into one .zip file
cd data/folding
cat folding_dataset.z* > folding_dataset.zip
# unzip
unzip folding_dataset.zip
All the data are stored in zarr format.
Dataset Structure
Here is the detailed stucture of a data example (zarr format) of one frame:
00068_Tshirt_000000_000000
βββ grip_vertex_id
β βββ left_grip_vertex_id (1,) int32
β βββ right_grip_vertex_id (1,) int32
βββ hand_pose
β βββ left_hand_euler (25, 3) float32
β βββ left_hand_pos (25, 3) float32
β βββ right_hand_euler (25, 3) float32
β βββ right_hand_pos (25, 3) float32
βββ marching_cube_mesh
β βββ is_vertex_on_surface (6410,) bool
β βββ marching_cube_faces (12816, 3) int32
β βββ marching_cube_verts (6410, 3) float32
βββ mesh
β βββ cloth_faces_tri (8312, 3) int32
β βββ cloth_nocs_verts (4434, 3) float32
β βββ cloth_verts (4434, 3) float32
βββ point_cloud
βββ cls (30000,) uint8
βββ nocs (30000, 3) float16
βββ point (30000, 3) float16
βββ rgb (30000, 3) uint8
βββ sizes (4,) int64
Specifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here grip_vertex_id
is the vertex index list of the grasped points of the mesh.
Dataset Example
Please see example for example data and visualization scripts.
Here are two video examples for flattening and folding task.