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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              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/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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2026-07-28 04:50:34.858: axis_1,d,-0.518
2026-07-28 04:50:34.858: axis_2,d,-0.07
2026-07-28 04:50:34.858: axis_3,d,-0.039
2026-07-28 04:50:34.891: axis_0,d,0.261
2026-07-28 04:50:34.891: axis_1,d,-0.538
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2026-07-28 04:50:35.391: axis_3,d,-0.005
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2026-07-28 04:50:35.425: axis_1,d,-0.675
2026-07-28 04:50:35.425: axis_2,d,-0.066
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2026-07-28 04:50:35.458: axis_2,d,-0.065
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2026-07-28 04:50:35.491: axis_2,d,-0.063
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2026-07-28 04:50:35.525: axis_0,d,0.276
2026-07-28 04:50:35.525: axis_1,d,-0.681
End of preview.

WorldRover-styles

The same camera path rendered under six different treatments. 200 first-person trajectories across four scenes, each re-rendered as up to six variants — so the geometry, motion and action labels are held fixed while lighting, weather and material appearance change. 1000 clips, ~48 h of video at 30 fps, ~818 GB.

Variant Scenes Clips What changes Per clip
first_person all 4 200 the reference render rgb + depth + pose + actions
white_model all 4 200 untextured white shading — pure geometry rgb only
goldenhour 3 150 low warm sun rgb only
night 3 150 night lighting rgb only
snow 3 150 snow cover + overcast rgb only
storm 3 150 storm sky + wet surfaces rgb only

goldenhour / night / snow / storm cover med_village, paris and venice; art_nouveau is an interior set and ships first_person + white_model only.

Scene Base trajectories Median clip Range Video per variant
med_village 50 3 min 29 s 41 s – 6 min 45 s 3.01 h
venice 50 2 min 54 s 1 min 07 s – 6 min 43 s 2.66 h
paris 50 1 min 54 s 49 s – 5 min 15 s 1.80 h
art_nouveau 50 1 min 32 s 14 s – 5 min 00 s 1.39 h

Layout

<scene>/first_person/<clip_id>/     pinhole 1280x720, 30 fps
    rgb.mp4  depth/depth.mkv  depth/depth.meta.json
    camera_trajectory.csv  description.json  gamepad_format/  trajectory.png
<scene>/<variant>/<clip_id>/rgb.mp4  same path, same frame count, different look
style_grids/batch_grid_<scene>_<variant>.mp4   contact-sheet previews (64 videos)

A variant clip shares its <clip_id> with the first_person clip, so the pose, depth and action labels from first_person/<clip_id> apply to every variant of it unchanged.

Conventions

  • Read the depth pixel format from each clip's depth/depth.meta.json — this batch is not uniform. 197 of the 200 clips are FFV1 / gbrp16le with the code in the R channel (G = B = 0); three art_nouveau clips — an725_navmesh_000005, an_react_000022, an_react_000037 — are single-plane FFV1 / gray16le instead. Every clip's depth.meta.json states its own format correctly (verified against the files), so read it rather than assuming. WorldRover-6scenes is uniformly gray16le. The quantization is identical either way: depth_m = exp(code/65535 * (log 200 − log 0.1) + log 0.1), radial distance.
  • camera_trajectory.csv has one row more than the video has frames; the authoritative count is depth/depth.meta.json.
  • Poses are Unreal-style: left-handed, centimetres, X-forward / Y-right / Z-up.

Tools

git clone https://github.com/AlayaLab/WorldRover
pip install -r WorldRover/tools/requirements.txt   # numpy, opencv-python; ffmpeg/ffprobe on PATH
cd WorldRover/tools                                # the package is not pip-installable yet
from worldrover import Clip

clip    = Clip("/data/WorldRover-styles/venice/first_person/fix_venice_000000")
rgb     = clip.rgb_frame(100)     # uint8 (H, W, 3), sRGB
depth_m = clip.depth_frame(100)   # planar depth in metres
pts     = clip.points_world(100)  # world points, centimetres
poses   = clip.poses              # per-frame pose + intrinsics

Clip takes a filesystem path to a clip directory. The reader takes the depth pixel format from that clip's own depth/depth.meta.json, so it handles both encodings used across these repositories, and it converts radial depth and the off-by-one trajectory row for you.

The style variants hold only rgb.mp4; point Clip at the matching first_person clip for that trajectory's depth, pose and action labels.

Related

License

Rendered video, depth, camera pose and action labels are released for research use. The underlying 3D environments are third-party commercial assets, are not redistributed here, and each clip's description.json records its asset pack and licence. Tools are MIT.

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