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VSI-Super-Wild

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Visual Question Answering
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Tags:
spatial-supersensing
video-understanding
long-video
spatial-reasoning
benchmark
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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:    ValueError
Message:      Invalid string class label VSI-Super-Wild@7d1cf5cdeccd20e7acdfb1440411d8b9ddfcc4d9
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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label VSI-Super-Wild@7d1cf5cdeccd20e7acdfb1440411d8b9ddfcc4d9

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[ECCV 2026] Towards Spatial Supersensing in the Wild

Tsinghua University, NVIDIA, Stanford University

Project Page Paper Code Dataset

Humans make sense of continuous sensory streams by maintaining implicit world states that support spatial reasoning and prediction. VSI-Super-Wild studies whether multimodal models can develop similar spatial supersensing capabilities in genuinely long-form, in-the-wild videos.

The benchmark moves beyond short indoor clips and object-centric settings. It evaluates world modeling over the agent-object-environment triad with human-verified question-answer pairs grounded in real-world video streams.

VSI-Super-Wild teaser

Highlights

  • In-the-wild long-video benchmark. VSI-Super-Wild contains 442 view-level videos across 8 scene categories, totaling 284.52 hours, with videos up to 261.08 minutes long.
  • Human-verified spatial QA. The release includes 6,980 question-answer pairs covering motion, place ordering, object ordering, and continuous object counting.
  • Multi-anchor task suite. Four tasks probe agent, environment, and object states over long temporal horizons.
  • Ready for evaluation. The video archives and metadata are released on this Hugging Face dataset page, while evaluation code is maintained in the GitHub repository.

Motivation

Existing spatial supersensing benchmarks are an important step toward testing implicit world modeling, but they often remain synthetic, household-centered, or object-centric. VSI-Super-Wild targets natural long-video streams and broader world-state coverage across agent, object, and environment anchors.

Motivation

Task Suite

VSI-Super-Wild evaluates four task families over the agent-object-environment triad:

Task Name Anchor Answer Format Q&A Count
VMR Motion Orientation Recall Agent Multiple choice 1,215
VPO Place Temporal Ordering Environment Multiple choice 1,302
VOO Object Temporal Ordering Object Multiple choice 3,350
VOC Continuous Object Counting Object Integer 1,113

VOO contains two balanced subtypes: 1,675 first-appearance recall questions and 1,675 last-appearance recall questions.

Task suite

Dataset Construction

The benchmark is built through a semi-automatic pipeline with human-in-the-loop verification. We collect and filter in-the-wild panoramic videos, project panoramas into perspective views, derive temporal and spatial metadata, synthesize rule-based QA, and verify the resulting samples with rollback when metadata or QA needs refinement.

Data construction

Benchmark Statistics

The released metadata file contains 6,980 human-verified Q&A rows associated with 442 view-level videos.

Benchmark statistics

Files on Hugging Face

The Hugging Face repository contains the QA metadata file and video archives grouped by video duration bucket:

VSI-Super-Wild/
|-- qa.jsonl
|-- videos_0_10_part_000.zip
|-- videos_10_30_part_000.zip
|-- videos_10_30_part_001.zip
|-- videos_30_60_part_000.zip
|-- ...
|-- videos_60_120_part_001.zip
|-- videos_120_plus_part_000.zip
|-- ...
`-- videos_120_plus_part_008.zip
Duration Bucket Archive Pattern Files Approx. Size
0-10 min videos_0_10_part_*.zip 1 49.53 GB
10-30 min videos_10_30_part_*.zip 2 267.49 GB
30-60 min videos_30_60_part_*.zip 6 833.35 GB
60-120 min videos_60_120_part_*.zip 2 265.15 GB
120+ min videos_120_plus_part_*.zip 9 1249.97 GB
Total video archives - 20 2665.49 GB

The video_name field in qa.jsonl refers to the corresponding video file used by each question.

This dataset page hosts the released videos and QA metadata. For evaluation, please use the official GitHub codebase, which maintains the task definitions, video resolver, metrics, and evaluator scripts.

Citation 

@inproceedings{VSI_Super_Wild,
  title={Towards Spatial Supersensing in the Wild},
  author={Tianjun Gu and Tianyu Xin and Kuan Zhang and Bowen Yang and Kok-Chung Chua and Peize Li and Xinran Zhang and Yupeng Chen and Qiyue Zhao and Qinlei Xie and Jianhang Liu and Yucheng Lu and Yinan Han and Marco Pavone and Yiming Li},
  booktitle={The Nineteenth European Conference on Computer Vision},
  year={2026}
}
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Paper for THUSI-Lab/VSI-Super-Wild