Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
source_video_id: string
source_split: string
boundaries: list<item: int64>
child 0, item: int64
discontinuities: list<item: null>
child 0, item: null
all_source_frames_accounted_for: bool
original_frames: int64
job: string
dataset: string
existing_training_assignment: string
training_admission: string
source_geometry_receipt_sha: string
native_split: string
source_revision: string
source_metadata_sha: string
source_metadata: struct<dataset: string, id: string, video_id: string, split: string, object_ids: list<item: int64>, (... 9430 chars omitted)
child 0, dataset: string
child 1, id: string
child 2, video_id: string
child 3, split: string
child 4, object_ids: list<item: int64>
child 0, item: int64
child 5, frame_ids: list<item: string>
child 0, item: string
child 6, frame_indices: list<item: int64>
child 0, item: int64
child 7, source_frame_ids: list<item: string>
child 0, item: string
child 8, timestamps: list<item: double>
child 0, item: double
child 9, fps: int64
child 10, fps_policy: string
child 11, boxes: list<item: list<item: struct<object_id: int64, xyxy: list<item: int64>>>>
child 0, item: list<item: struct<object_id: int64, xyxy: list<item: int64>>>
child 0, item: struct<object_id: int64, xyxy: list<item: int64>>
child 0, object_id: int64
child 1, xyxy: list<item: int64>
child 0, item: int64
child 12, annotation_available: list<item: bool>
...
8, reference_provenance: string
child 19, codec: struct<name: string, crf: int64, pixel_format: string, note: string>
child 0, name: string
child 1, crf: int64
child 2, pixel_format: string
child 3, note: string
child 20, ready_for_reference_condition: bool
child 21, ready_for_text_condition: bool
child 22, caption_provenance: string
child 21, dynamic: bool
child 22, source_target_pair: null
child 23, annotation_frame_count: int64
child 24, full_raw_video: bool
child 25, background_exclusion: string
child 26, stage: string
child 27, schema: string
child 28, producer: string
child 29, frames: int64
child 30, native_hw: list<item: int64>
child 0, item: int64
video_id: string
units: list<item: struct<unit: string, frame_range: list<item: int64>, frames: int64, calibration_pass: boo (... 187 chars omitted)
child 0, item: struct<unit: string, frame_range: list<item: int64>, frames: int64, calibration_pass: bool, backgrou (... 175 chars omitted)
child 0, unit: string
child 1, frame_range: list<item: int64>
child 0, item: int64
child 2, frames: int64
child 3, calibration_pass: bool
child 4, background_photometric_pass: bool
child 5, background_temporal_pass: bool
child 6, tracking_usable: bool
child 7, fg_available: bool
child 8, model_ready: bool
child 9, training_admission: string
child 10, accepted_background: bool
to
{'job': Value('string'), 'dataset': Value('string'), 'video_id': Value('string'), 'native_split': Value('string'), 'existing_training_assignment': Value('string'), 'source_metadata': {'dataset': Value('string'), 'id': Value('string'), 'video_id': Value('string'), 'split': Value('string'), 'object_ids': List(Value('int64')), 'frame_ids': List(Value('string')), 'frame_indices': List(Value('int64')), 'source_frame_ids': List(Value('string')), 'timestamps': List(Value('float64')), 'fps': Value('int64'), 'fps_policy': Value('string'), 'boxes': List(List({'object_id': Value('int64'), 'xyxy': List(Value('int64'))})), 'annotation_available': List(Value('bool')), 'text': Value('string'), 'reference_mode': Value('string'), 'full_clip': Value('bool'), 'original_frame_count': Value('int64'), 'camera_provenance': Value('string'), 'mask_provenance': Value('string'), 'input_hashes': {'answer/original_frames/train/00000.jpg': Value('string'), 'answer/original_frames/train/00001.jpg': Value('string'), 'answer/original_frames/train/00002.jpg': Value('string'), 'answer/original_frames/train/00003.jpg': Value('string'), 'answer/original_frames/train/00004.jpg': Value('string'), 'answer/original_frames/train/00005.jpg': Value('string'), 'answer/original_frames/train/00006.jpg': Value('string'), 'answer/original_frames/train/00007.jpg': Value('string'), 'answer/original_frames/train/00008.jpg': Value('string'), 'answer/original_frames/train/00009.jpg': Value('string'), 'answer/original_frames/trai
...
'reference_image': Value('string'), 'masked_video': Value('string')}, 'object_ids': List(Value('int64')), 'track_id': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'fps': Value('int64'), 'fps_policy': Value('string'), 'reference_provenance': Value('string'), 'codec': {'name': Value('string'), 'crf': Value('int64'), 'pixel_format': Value('string'), 'note': Value('string')}, 'ready_for_reference_condition': Value('bool'), 'ready_for_text_condition': Value('bool'), 'caption_provenance': Value('string')}, 'dynamic': Value('bool'), 'source_target_pair': Value('null'), 'annotation_frame_count': Value('int64'), 'full_raw_video': Value('bool'), 'background_exclusion': Value('string'), 'stage': Value('string'), 'schema': Value('string'), 'producer': Value('string'), 'frames': Value('int64'), 'native_hw': List(Value('int64'))}, 'original_frames': Value('int64'), 'units': List({'unit': Value('string'), 'frame_range': List(Value('int64')), 'frames': Value('int64'), 'calibration_pass': Value('bool'), 'background_photometric_pass': Value('bool'), 'background_temporal_pass': Value('bool'), 'tracking_usable': Value('bool'), 'fg_available': Value('bool'), 'model_ready': Value('bool'), 'training_admission': Value('string'), 'accepted_background': Value('bool')}), 'source_revision': Value('string'), 'source_metadata_sha': Value('string'), 'source_geometry_receipt_sha': Value('string'), 'all_source_frames_accounted_for': Value('bool'), 'training_admission': Value('string')}
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
source_video_id: string
source_split: string
boundaries: list<item: int64>
child 0, item: int64
discontinuities: list<item: null>
child 0, item: null
all_source_frames_accounted_for: bool
original_frames: int64
job: string
dataset: string
existing_training_assignment: string
training_admission: string
source_geometry_receipt_sha: string
native_split: string
source_revision: string
source_metadata_sha: string
source_metadata: struct<dataset: string, id: string, video_id: string, split: string, object_ids: list<item: int64>, (... 9430 chars omitted)
child 0, dataset: string
child 1, id: string
child 2, video_id: string
child 3, split: string
child 4, object_ids: list<item: int64>
child 0, item: int64
child 5, frame_ids: list<item: string>
child 0, item: string
child 6, frame_indices: list<item: int64>
child 0, item: int64
child 7, source_frame_ids: list<item: string>
child 0, item: string
child 8, timestamps: list<item: double>
child 0, item: double
child 9, fps: int64
child 10, fps_policy: string
child 11, boxes: list<item: list<item: struct<object_id: int64, xyxy: list<item: int64>>>>
child 0, item: list<item: struct<object_id: int64, xyxy: list<item: int64>>>
child 0, item: struct<object_id: int64, xyxy: list<item: int64>>
child 0, object_id: int64
child 1, xyxy: list<item: int64>
child 0, item: int64
child 12, annotation_available: list<item: bool>
...
8, reference_provenance: string
child 19, codec: struct<name: string, crf: int64, pixel_format: string, note: string>
child 0, name: string
child 1, crf: int64
child 2, pixel_format: string
child 3, note: string
child 20, ready_for_reference_condition: bool
child 21, ready_for_text_condition: bool
child 22, caption_provenance: string
child 21, dynamic: bool
child 22, source_target_pair: null
child 23, annotation_frame_count: int64
child 24, full_raw_video: bool
child 25, background_exclusion: string
child 26, stage: string
child 27, schema: string
child 28, producer: string
child 29, frames: int64
child 30, native_hw: list<item: int64>
child 0, item: int64
video_id: string
units: list<item: struct<unit: string, frame_range: list<item: int64>, frames: int64, calibration_pass: boo (... 187 chars omitted)
child 0, item: struct<unit: string, frame_range: list<item: int64>, frames: int64, calibration_pass: bool, backgrou (... 175 chars omitted)
child 0, unit: string
child 1, frame_range: list<item: int64>
child 0, item: int64
child 2, frames: int64
child 3, calibration_pass: bool
child 4, background_photometric_pass: bool
child 5, background_temporal_pass: bool
child 6, tracking_usable: bool
child 7, fg_available: bool
child 8, model_ready: bool
child 9, training_admission: string
child 10, accepted_background: bool
to
{'job': Value('string'), 'dataset': Value('string'), 'video_id': Value('string'), 'native_split': Value('string'), 'existing_training_assignment': Value('string'), 'source_metadata': {'dataset': Value('string'), 'id': Value('string'), 'video_id': Value('string'), 'split': Value('string'), 'object_ids': List(Value('int64')), 'frame_ids': List(Value('string')), 'frame_indices': List(Value('int64')), 'source_frame_ids': List(Value('string')), 'timestamps': List(Value('float64')), 'fps': Value('int64'), 'fps_policy': Value('string'), 'boxes': List(List({'object_id': Value('int64'), 'xyxy': List(Value('int64'))})), 'annotation_available': List(Value('bool')), 'text': Value('string'), 'reference_mode': Value('string'), 'full_clip': Value('bool'), 'original_frame_count': Value('int64'), 'camera_provenance': Value('string'), 'mask_provenance': Value('string'), 'input_hashes': {'answer/original_frames/train/00000.jpg': Value('string'), 'answer/original_frames/train/00001.jpg': Value('string'), 'answer/original_frames/train/00002.jpg': Value('string'), 'answer/original_frames/train/00003.jpg': Value('string'), 'answer/original_frames/train/00004.jpg': Value('string'), 'answer/original_frames/train/00005.jpg': Value('string'), 'answer/original_frames/train/00006.jpg': Value('string'), 'answer/original_frames/train/00007.jpg': Value('string'), 'answer/original_frames/train/00008.jpg': Value('string'), 'answer/original_frames/train/00009.jpg': Value('string'), 'answer/original_frames/trai
...
'reference_image': Value('string'), 'masked_video': Value('string')}, 'object_ids': List(Value('int64')), 'track_id': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'fps': Value('int64'), 'fps_policy': Value('string'), 'reference_provenance': Value('string'), 'codec': {'name': Value('string'), 'crf': Value('int64'), 'pixel_format': Value('string'), 'note': Value('string')}, 'ready_for_reference_condition': Value('bool'), 'ready_for_text_condition': Value('bool'), 'caption_provenance': Value('string')}, 'dynamic': Value('bool'), 'source_target_pair': Value('null'), 'annotation_frame_count': Value('int64'), 'full_raw_video': Value('bool'), 'background_exclusion': Value('string'), 'stage': Value('string'), 'schema': Value('string'), 'producer': Value('string'), 'frames': Value('int64'), 'native_hw': List(Value('int64'))}, 'original_frames': Value('int64'), 'units': List({'unit': Value('string'), 'frame_range': List(Value('int64')), 'frames': Value('int64'), 'calibration_pass': Value('bool'), 'background_photometric_pass': Value('bool'), 'background_temporal_pass': Value('bool'), 'tracking_usable': Value('bool'), 'fg_available': Value('bool'), 'model_ready': Value('bool'), 'training_admission': Value('string'), 'accepted_background': Value('bool')}), 'source_revision': Value('string'), 'source_metadata_sha': Value('string'), 'source_geometry_receipt_sha': Value('string'), 'all_source_frames_accounted_for': Value('bool'), 'training_admission': 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.
VACE Object
2026-11-07: single-video geometry quality review / training policy
The current training continuation uses MultiCam and DL3DV only. DAVIS/HC-STVG are excluded from new training/evaluation batches after residual unified-background instability was confirmed. This does not delete or invalidate the original object-reference archives.
The v49 adapter enlarged PC4 framing and added a temporal reprojection gate; it did not reconstruct camera/depth/background GS for retained samples. Background reconstruction and FG temporal-consistency improvements remain proposed work, not completed preprocessing. See the detailed preprocessing review and change specification, including measurements, implemented changes, limitations, and re-admission requirements.
Derived object-reference and rectangular video-inpainting preparation of four source datasets. This is not an official VACE release. Initial upload completed. See upload_complete.json for the archive counts and size verification results.
| Source subset | Videos | Samples | Clean reference images |
|---|---|---|---|
| DAVIS 2017 train/val | 90 | 205 | 205 |
| MeViS v2 train subset | 200 | 1,449 | 497 |
| HC-STVG 2.0 train subset | 300 | 300 | 300 |
| Ref-YouTube-VOS train | 3,471 | 12,913 | 6,459 |
| Total | 4,061 | 14,867 | 7,461 |
Files and extraction
Download with huggingface_hub.snapshot_download(repo_id="calisolo/vace-object", repo_type="dataset"). Extract every .tar in archives/ into the same destination directory. Extract all shards before reading samples: some entries are relative symbolic links to shared tracks or frames within the dataset. The archives preserve the VACE_<dataset>/ layout. These are TAR archives, not Arrow/Parquet configurations; use the JSONL manifests rather than load_dataset().
Each source dataset includes manifest.jsonl, dataset_info.json, input/bbox, input/text_prompt, input/reference_image, input/masked_video, answer/original_video, original frame sequences where available, and native annotations referenced by the manifests. Combined catalog.jsonl rows add dataset and dataset_root; remaining file paths are relative to that dataset root. No model weights, credentials, environment files, download archives, or operational logs are included.
Reference files:
object_<id>.png: original bounding-box crop.object_<id>_cleanreference.jpg: original foreground composited onto white, JPEG quality 100/4:4:4. JPEG encoding is lossy.object_<id>_cleanreference_mask.png: aligned binary foreground mask (255 foreground, 0 background).reference_metadata.json: object ID, source frame index/ID, crop xyxy, and selection provenance.
Boxes are xyxy pixel coordinates, zero-based frame indices, right/bottom exclusive. Input videos have rectangular black masks, not silhouette masks. References were selected from the answer sequence using maximum bounding-box area and are not independent external references; account for this in evaluation.
Processing and limitations
DAVIS, MeViS, and Ref-YouTube-VOS clean references use native object segmentation. HC-STVG clean-reference masks are SAM3 estimates from existing bounding boxes, and most have not been manually inspected. Body-part omissions, holes, and incorrect instances may remain. Processing completion does not mean full quality validation.
HC-STVG 120_TM5MPJIq1Is has a visually corrected English caption, text-plus-box resegmentation, and manually repaired belt/trousers regions; previous outputs and correction provenance are included. Original source annotation is preserved. DAVIS captions were VLM generated and may contain target-description errors. Referring expressions may mention multiple entities; target IDs define the selected instance. MeViS can have multiple object references per track. Expression aliases share the same underlying track artifacts.
Videos were CPU encoded with libx264 CRF18/yuv444p without resizing. Dataset-specific sequence FPS conventions are recorded in dataset_info.json and manifests and are not necessarily original-video FPS.
Attribution and terms
Original dataset/video rights remain applicable. Public access does not imply unrestricted commercial use. DAVIS has CC BY-NC 4.0 terms and MeViS CC BY-NC-SA 4.0/non-commercial research terms. No new blanket license is granted for third-party videos. See upstream sources and terms.
HC-STVG full raw source video supplement (2026-11-06)
The original HC-STVG archive contains annotation-only answer clips. Full raw source videos for all 300 selected videos are now available in archives/VACE_HC_STVG/raw-videos-1106. Download the tar, SHA256SUMS and manifest there, then overlay-extract into the same data root. The archive contains VACE_HC_STVG/raw/videos/ and per-file provenance; source lengths vary and target annotation intervals may be shorter. Raw archive revision: 4ef247cbf36656c0347795d3cf0e95033716c8be.
Experimental static-background repair implementation (2026-11-07)
An isolated implementation now recalibrates static-background cameras/depth scales, splits disconnected shots, rebuilds Gaussian geometry, and regenerates dependent FG/PC4/rays/tracks. Two representative units passed preprocessing validation; full DAVIS205 + HC-STVG300 track reprocessing is ongoing locally, not complete. See implementation, fixed gates, aggregate before/after validation and source code. The current MC/DL-only training policy is unchanged. New VAE/model validation and training re-admission are not claimed. This documentation publication does not imply derived payload upload completion.
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