Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
pipeline_name: string
pipeline_version: string
pipeline_version_index: string
pipeline_release_id: string
pipeline_display_name: string
pipeline_contract_sha256: string
dataset_release: string
rendering_base: string
schema: string
dataset: string
sequence_key: string
camera_variant_index: int64
solver_version: string
rig: string
mode: string
mount: string
anchor_source: string
aim_policy: string
head_orientation_used_for_aim: bool
controls: struct<candidate_index: int64, translation_seed_hex: string, rotation_seed_hex: string, translation_ (... 539 chars omitted)
child 0, candidate_index: int64
child 1, translation_seed_hex: string
child 2, rotation_seed_hex: string
child 3, translation_radius_m: double
child 4, translation_vertical_radius_m: double
child 5, translation_period_seconds: double
child 6, translation_phase_rad: double
child 7, translation_drift_phase_rad: double
child 8, rotation_yaw_radius_deg: double
child 9, rotation_pitch_radius_deg: double
child 10, rotation_period_seconds: double
child 11, rotation_phase_rad: double
child 12, rotation_drift_phase_rad: double
child 13, rotation_yaw_center_deg: double
child 14, rotation_pitch_center_deg: double
child 15, roll_deg: double
child 16, mount: string
child 17, mount_forward_m: double
child 18, mount_up_m: double
child 19, sampled_lens_mm: double
child 20, lens_mm: double
child 21, lens_resolve_scale: double
resolution: list<item: int64>
child 0, item: int64
sensor_width
...
ha256: string
child 1, camera_parameters.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 2, mano_parameters.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 3, masks_visible_2bit.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 4, scene_randomization.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 5, camera_plan.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 6, sensor_look.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 7, render_audit.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 8, depth_gt_mm.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 9, depth_sensor_u16.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 10, verified: bool
uploaded_bytes: int64
completed: int64
training_complete: bool
updated_at: double
release: string
to
{'schema': Value('string'), 'dataset': Value('string'), 'release': Value('string'), 'completed': Value('int64'), 'expected': Value('int64'), 'uploaded_bytes': Value('int64'), 'source_frames': Value('int64'), 'training_complete': Value('bool'), 'complete': List({'sequence': Value('string'), 'frames': Value('int64'), 'bytes': Value('int64'), 'commit': Value('string'), 'missing_channels': List(Value('null')), 'paired_depth': Value('bool'), 'worker_seconds': Value('float64'), 'upload_seconds': Value('float64'), 'source_metadata_sha256': Value('string'), 'files': {'rgb.mp4': {'bytes': Value('int64'), 'sha256': Value('string')}, 'camera_parameters.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'mano_parameters.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'masks_visible_2bit.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'scene_randomization.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'camera_plan.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'sensor_look.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'render_audit.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'depth_gt_mm.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'depth_sensor_u16.npz': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'verified': Value('bool')}), 'updated_at': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
pipeline_name: string
pipeline_version: string
pipeline_version_index: string
pipeline_release_id: string
pipeline_display_name: string
pipeline_contract_sha256: string
dataset_release: string
rendering_base: string
schema: string
dataset: string
sequence_key: string
camera_variant_index: int64
solver_version: string
rig: string
mode: string
mount: string
anchor_source: string
aim_policy: string
head_orientation_used_for_aim: bool
controls: struct<candidate_index: int64, translation_seed_hex: string, rotation_seed_hex: string, translation_ (... 539 chars omitted)
child 0, candidate_index: int64
child 1, translation_seed_hex: string
child 2, rotation_seed_hex: string
child 3, translation_radius_m: double
child 4, translation_vertical_radius_m: double
child 5, translation_period_seconds: double
child 6, translation_phase_rad: double
child 7, translation_drift_phase_rad: double
child 8, rotation_yaw_radius_deg: double
child 9, rotation_pitch_radius_deg: double
child 10, rotation_period_seconds: double
child 11, rotation_phase_rad: double
child 12, rotation_drift_phase_rad: double
child 13, rotation_yaw_center_deg: double
child 14, rotation_pitch_center_deg: double
child 15, roll_deg: double
child 16, mount: string
child 17, mount_forward_m: double
child 18, mount_up_m: double
child 19, sampled_lens_mm: double
child 20, lens_mm: double
child 21, lens_resolve_scale: double
resolution: list<item: int64>
child 0, item: int64
sensor_width
...
ha256: string
child 1, camera_parameters.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 2, mano_parameters.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 3, masks_visible_2bit.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 4, scene_randomization.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 5, camera_plan.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 6, sensor_look.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 7, render_audit.json: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 8, depth_gt_mm.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 9, depth_sensor_u16.npz: struct<bytes: int64, sha256: string>
child 0, bytes: int64
child 1, sha256: string
child 10, verified: bool
uploaded_bytes: int64
completed: int64
training_complete: bool
updated_at: double
release: string
to
{'schema': Value('string'), 'dataset': Value('string'), 'release': Value('string'), 'completed': Value('int64'), 'expected': Value('int64'), 'uploaded_bytes': Value('int64'), 'source_frames': Value('int64'), 'training_complete': Value('bool'), 'complete': List({'sequence': Value('string'), 'frames': Value('int64'), 'bytes': Value('int64'), 'commit': Value('string'), 'missing_channels': List(Value('null')), 'paired_depth': Value('bool'), 'worker_seconds': Value('float64'), 'upload_seconds': Value('float64'), 'source_metadata_sha256': Value('string'), 'files': {'rgb.mp4': {'bytes': Value('int64'), 'sha256': Value('string')}, 'camera_parameters.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'mano_parameters.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'masks_visible_2bit.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'scene_randomization.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'camera_plan.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'sensor_look.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'render_audit.json': {'bytes': Value('int64'), 'sha256': Value('string')}, 'depth_gt_mm.npz': {'bytes': Value('int64'), 'sha256': Value('string')}, 'depth_sensor_u16.npz': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'verified': Value('bool')}), 'updated_at': Value('float64')}
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.
EgoRenderer V006 ARCTIC
Upload in progress. Use INDEX.json for verified uploaded sequences. A
directory or a planned sequence count does not mean that all files are present.
This derived research release contains randomized synthetic views of the301 public ARCTIC source motions, covering218,273original source frames at30fps. Each source has one production. Two historical extra variants and the separate hard-case campaign are not part of this upload.
Channels
samples/<sequence>/rgb.mp4
samples/<sequence>/depth_gt_mm.npz
samples/<sequence>/depth_sensor_u16.npz
samples/<sequence>/masks_visible_2bit.npz
samples/<sequence>/camera_parameters.npz
samples/<sequence>/mano_parameters.npz
samples/<sequence>/metadata.json
Generated RGB is1536x864,30fps,Cycles16SPP/OIDN,DLSS5 and temporal postprocessing. Scene appearance,lighting,materials,props and camera sampling are randomized. Source clocks and native hand trajectories are retained. Rendering uses original dataset-native hands,not fitted MANO/SOMA auxiliary labels.
Clean GT and simulated active-stereo depth are separate,compressed at fast level1 and quantized to millimeters. The reader returns meters. GT may use uint16,uint32 or uint64 per frame to preserve far/background values; it is never silently clipped to the sensor's uint16 range. Invalid measurements remain invalid. No GT is used to fill simulated sensor holes.
The initial historical GT gaps are arctic_s01_capsulemachine_grab_01 and
arctic_s09_laptop_use_03. Their metadata explicitly identifies missing GT.
Eight other historical unregistered GT channels are being exported from retained
clean render passes,without new rendering. Never use noisy depth as clean GT.
Reading
Download egorender_reader.py and the files for a sequence. NumPy is sufficient.
from egorender_reader import DepthReader, depth_pair
gt_path, sensor_path = depth_pair("samples/arctic_s10_waffleiron_use_02")
with DepthReader(gt_path) as reader:
depth_m, valid, source_frame = reader.frame(0)
Use the package's exact source_frame_indices and camera arrays. Split by the
original source identity,not individual frames. The official hidden test GT is
not fabricated or included. Auxiliary fitted MANO/SOMA labels are not included
in this export. Generated status does not imply visual/quality acceptance or
complete training qualification; training_complete remains false.
Provenance And Rights
The repository owner has confirmed public-release authorization for these derived results. ARCTIC's original research license still governs upstream materials. Do not interpret this repository as an unrestricted commercial license or permission to redistribute original model files. Contact the publisher for the scope of additional release authorization.
Numeric native hand annotations are included under that confirmed release authorization. Original dataset images,SMPL-X/MANO model files,private scene assets,body/clothing meshes,credentials and internal host paths are not uploaded. Public metadata is a sanitized derivative;original on-server receipts remain unchanged. See source metadata hashes and per-file hashes in each package.
Please cite the upstream dataset:
@inproceedings{fan2023arctic,
title={ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation},
author={Fan, Zicong and Taheri, Omid and Tzionas, Dimitrios and Kocabas, Muhammed and Kaufmann, Manuel and Black, Michael J. and Hilliges, Otmar},
booktitle={CVPR},
year={2023}
}
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