Grounding World Simulation Models in a Real-World Metropolis
Paper • 2603.15583 • Published • 154
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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 68, 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.
dojo-interp-v1 — DrivingDojo densified to 24 fps
10,068 five-second driving clips (1280×704, 24 fps, 73 or 121 frames) generated from DrivingDojo (Apache-2.0) 5 fps sequences with a view-interpolation video model. Every 5th generated frame is a real DrivingDojo frame; the frames in between are synthesised. Uniform keyframe spacing, guidance 2.
Layout
clips/dojo-NNN.tar tar shards, ~300 clips each, members <name>.mp4
poses/dojo-poses.tar <name>.npy (T,7) float32 per-frame relative camera
action [dx,dy,dz,droll,dpitch,dyaw,flag], CARLA
convention, estimated with VGGT-Omega on the
generated clip (unscaled: monocular)
ref_actions/dojo-ref-actions.tar <clip_id>.npy actions estimated on the original
5 fps DrivingDojo frames (reference for the clip)
manifest_s{0..3}.jsonl name, clip_id, frames, arc_m (null), caption, guidance
qc_scores.jsonl cheap motion/sharpness QC per clip (weak tag)
name is <clip_id>_w<k>; clip_id is the DrivingDojo sequence id.
Generation
Wan2.2-TI2V-5B with a rank-128 LoRA trained with the freeze-frame keyframe
recipe of the Seoul World Model (arXiv:2603.15583); 35 sampling steps, flow
shift 7. Code: https://github.com/kamwoh/miniworld
(scripts/generation/interpolate_batch.py).
Caveats
About 20% of DrivingDojo clips are near-stationary (stopped at lights); the QC
static flag marks them. Generated frames can morph on close-range pans.
License and attribution
Source frames: DrivingDojo (Apache-2.0). This derivative keeps Apache-2.0.
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