The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
dexgarmentlab-folding-lifting-meshes
Simulated garment-manipulation demonstrations recorded with DexGarmentLab assets, for training cloth state-estimation and dynamics models on variable-vertex meshes.
Contents
dexgarmentlab_folding_lifting_meshes.h5 (~1.4 GB):
- 239 garments, mixed categories (tops, jackets, pants, dresses, shirts, suits, vests, sweaters), each with its own topology: 1,637–2,048 vertices, ≤6,051 unidirectional edges (≤12,102 bidirectional), ≤4,000 faces.
- 894 trajectories / 28,673 steps (12–59 steps each), two task types: short in-place folding-style manipulations (
54%) and lift-and-place motions (46%). - Single gripper, single camera with 2,048-point clouds per step.
- Metric scale, z-up, cloth resting on a table plane at z≈0.
Layout
training/<garment>/
rest_positions (V, 3) float32
edges (E, 2) int32 unidirectional
faces (F, 3) int32
trajectory_<N>/
actuated_vertices (V, 1) float64 binary grasp mask
step_<XXXX>/
positions (V, 3) float32 ground-truth mesh state
gripper_pos (3,) float32
pointclouds/cam_0 (2048, 3) float32
Only a training split is present; downstream loaders partition trajectories into train/val.
Usage
Consumed by the UniClothDiff training stack (ClothStateEstVariableDataset, ClothDynamicsVariableDataset) with max_num_nodes: 2048, max_num_edges: 12288. Configs train_state_est_gps_dexgarment.yaml and train_dynamics_gps_dexgarment.yaml auto-download this file via src/hub.py when it is not found locally.
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