The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
format: string
frame_count: int64
timestamps: string
hands_present: list<item: string>
child 0, item: string
coordinate_frame: string
camera_axes: string
translation_unit: string
camera: struct<width: int64, height: int64, fps_nominal: double, fx: double, fy: double, cx: double, cy: dou (... 50 chars omitted)
child 0, width: int64
child 1, height: int64
child 2, fps_nominal: double
child 3, fx: double
child 4, fy: double
child 5, cx: double
child 6, cy: double
child 7, dist_coeffs: list<item: int64>
child 0, item: int64
child 8, view: string
mano: struct<implementation: string, use_pca: bool, flat_hand_mean: bool, slots: string, hand_pose: string (... 106 chars omitted)
child 0, implementation: string
child 1, use_pca: bool
child 2, flat_hand_mean: bool
child 3, slots: string
child 4, hand_pose: string
child 5, global_orient: string
child 6, transl: string
child 7, betas: string
child 8, model_sha256: struct<left: string, right: string>
child 0, left: string
child 1, right: string
source: struct<recording: string, first_source_rgb_frame: int64, last_source_rgb_frame: int64, first_source_ (... 57 chars omitted)
child 0, recording: string
child 1, first_source_rgb_frame: int64
child 2, last_source_rgb_frame: int64
child 3, first_source_camera_time_s: double
child 4, last_source_camera_time_s: double
processing: struct<pose_resampling: string, global_rotation_resampling: string, translation_resampling: string, (... 12 chars omitted)
child 0, pose_resampling: string
child 1, global_rotation_resampling: string
child 2, translation_resampling: string
child 3, rgb: string
sha256: string
limitations: string
width: int64
generation: struct<model: string, checkpoint_sha256: string, text_prompt: string, anchor_frame: int64, tracking: (... 128 chars omitted)
child 0, model: string
child 1, checkpoint_sha256: string
child 2, text_prompt: string
child 3, anchor_frame: int64
child 4, tracking: string
child 5, anatomical_assignment: string
child 6, mask_geometry_postprocessing: string
child 7, inference_code_sha256: string
child 8, source_run: string
file: string
height: int64
arrays: struct<left_packed: string, right_packed: string, bitorder: string>
child 0, left_packed: string
child 1, right_packed: string
child 2, bitorder: string
to
{'format': Value('string'), 'file': Value('string'), 'sha256': Value('string'), 'frame_count': Value('int64'), 'width': Value('int64'), 'height': Value('int64'), 'coordinate_frame': Value('string'), 'timestamps': Value('string'), 'arrays': {'left_packed': Value('string'), 'right_packed': Value('string'), 'bitorder': Value('string')}, 'hands_present': {'left': Value('bool'), 'right': Value('bool')}, 'generation': {'model': Value('string'), 'checkpoint_sha256': Value('string'), 'text_prompt': Value('string'), 'anchor_frame': Value('int64'), 'tracking': Value('string'), 'anatomical_assignment': Value('string'), 'mask_geometry_postprocessing': Value('string'), 'inference_code_sha256': Value('string'), 'source_run': Value('string')}, 'limitations': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format: string
frame_count: int64
timestamps: string
hands_present: list<item: string>
child 0, item: string
coordinate_frame: string
camera_axes: string
translation_unit: string
camera: struct<width: int64, height: int64, fps_nominal: double, fx: double, fy: double, cx: double, cy: dou (... 50 chars omitted)
child 0, width: int64
child 1, height: int64
child 2, fps_nominal: double
child 3, fx: double
child 4, fy: double
child 5, cx: double
child 6, cy: double
child 7, dist_coeffs: list<item: int64>
child 0, item: int64
child 8, view: string
mano: struct<implementation: string, use_pca: bool, flat_hand_mean: bool, slots: string, hand_pose: string (... 106 chars omitted)
child 0, implementation: string
child 1, use_pca: bool
child 2, flat_hand_mean: bool
child 3, slots: string
child 4, hand_pose: string
child 5, global_orient: string
child 6, transl: string
child 7, betas: string
child 8, model_sha256: struct<left: string, right: string>
child 0, left: string
child 1, right: string
source: struct<recording: string, first_source_rgb_frame: int64, last_source_rgb_frame: int64, first_source_ (... 57 chars omitted)
child 0, recording: string
child 1, first_source_rgb_frame: int64
child 2, last_source_rgb_frame: int64
child 3, first_source_camera_time_s: double
child 4, last_source_camera_time_s: double
processing: struct<pose_resampling: string, global_rotation_resampling: string, translation_resampling: string, (... 12 chars omitted)
child 0, pose_resampling: string
child 1, global_rotation_resampling: string
child 2, translation_resampling: string
child 3, rgb: string
sha256: string
limitations: string
width: int64
generation: struct<model: string, checkpoint_sha256: string, text_prompt: string, anchor_frame: int64, tracking: (... 128 chars omitted)
child 0, model: string
child 1, checkpoint_sha256: string
child 2, text_prompt: string
child 3, anchor_frame: int64
child 4, tracking: string
child 5, anatomical_assignment: string
child 6, mask_geometry_postprocessing: string
child 7, inference_code_sha256: string
child 8, source_run: string
file: string
height: int64
arrays: struct<left_packed: string, right_packed: string, bitorder: string>
child 0, left_packed: string
child 1, right_packed: string
child 2, bitorder: string
to
{'format': Value('string'), 'file': Value('string'), 'sha256': Value('string'), 'frame_count': Value('int64'), 'width': Value('int64'), 'height': Value('int64'), 'coordinate_frame': Value('string'), 'timestamps': Value('string'), 'arrays': {'left_packed': Value('string'), 'right_packed': Value('string'), 'bitorder': Value('string')}, 'hands_present': {'left': Value('bool'), 'right': Value('bool')}, 'generation': {'model': Value('string'), 'checkpoint_sha256': Value('string'), 'text_prompt': Value('string'), 'anchor_frame': Value('int64'), 'tracking': Value('string'), 'anatomical_assignment': Value('string'), 'mask_geometry_postprocessing': Value('string'), 'inference_code_sha256': Value('string'), 'source_run': Value('string')}, 'limitations': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Ego Clinical Task
Research dataset containing synchronized egocentric RGB, camera-frame MANO hand
sequences, and automatically generated left/right glove masks. It contains 26
recordings (15,946 frames), grouped by activity in session_index.csv. The
recordings are glove demonstrations and object interactions; they do not claim
to contain clinical subjects or clinical outcomes.
quality_exceptions.csv is a sparse index of currently known frame-level
issues, inspired by HOT3D's independent quality masks. Frame ranges are
zero-based and inclusive. component identifies the affected modality;
status distinguishes a confirmed invalid annotation or image from a measured
timing warning or a region awaiting MANO review. An absent row means not
reviewed or not flagged, not a manual QA pass. Consumers can exclude only the
modalities and ranges relevant to their training task. These entries do not
alter the RGB, MANO, or mask files.
Each session contains:
rgb.mp4: rectified ZED left-camera video.mano.npz: camera-frame MANO parameters aligned one-to-one with RGB frames.metadata.json: camera model, coordinate convention, hands present, and source range.hand_masks.npz: lossless, bit-packed left/right glove masks aligned one-to-one with RGB frames.mask_metadata.json: mask format, hand presence, model, prompt, and generation method.
The compact format is compact_rgb_mano_v3. Coordinates use the rectified ZED
left-camera frame: +X right, +Y down, +Z forward. Translation is in meters.
Missing left or right hands are represented by empty arrays. There is no extra
project-specific scale parameter; hand shape is represented by the ten MANO
betas coefficients.
To decode a mask frame, use np.unpackbits and the stored image width:
import numpy as np
with np.load("hand_masks.npz", allow_pickle=False) as masks:
width = int(masks["width"])
left = np.unpackbits(masks["left_packed"], axis=2, count=width,
bitorder=str(masks["bitorder"])).astype(bool)
right = np.unpackbits(masks["right_packed"], axis=2, count=width,
bitorder=str(masks["bitorder"])).astype(bool)
# left[i] and right[i] align with frame i of rgb.mp4 and mano.npz.
For an absent hand, the corresponding packed array and decoded array have zero
frames. The masks were generated with SAM 3.1 using the text prompt black glove
and forward/backward video tracking. Projected MANO geometry assigns each mask
to the anatomical left or right hand and produces quality alerts; it does not
clip the predicted mask. There is no wrist-distance cutoff. The masks are
automatically generated research annotations, not pixel-accurate ground truth.
Occlusion and contact can still cause missing glove pixels or included object
pixels. Known reviewed exceptions are recorded in quality_exceptions.csv. Internal
validation reports and debug overlays are not part of this delivery.
The corrupted source RGB frame in session_041 frame 365 must be excluded
from training even if its mask were repaired. Session 040 has been withdrawn
from this release because its MANO tracking requires further investigation.
Recordings remain separate rather than being concatenated so that each video,
timestamp sequence, calibration record, and MANO sequence stays internally
aligned. session_index.csv is the authoritative, continuously updated list of
available sessions and activity groups.
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