SeerWorld-Data is a research dataset for training generative video models to represent world evolution beyond the current field of view. Each sample contains structured subjects, events, camera movements, and causal reactions.

The dataset accompanies the paper “SeerWorld: Towards Out-of-Sight World Evolution in Generative Video Models.”

Repository structure

SeerWorld-Data/
├── data/
│   ├── train/
│   │   ├── metadata.jsonl
│   │   └── videos/000/ ... videos/011/
│   └── additional/
│       ├── metadata.jsonl
│       └── videos/011/ ... videos/012/
├── training_cache/ltx-2.5/      # optional precomputed inputs
├── THIRD_PARTY_NOTICES.md
└── LICENSE

Videos are grouped into 1,000-ID buckets to keep Hub directories manageable. Each file_name is relative to the metadata.jsonl file in the same split.

Metadata schema

Each line in metadata.jsonl is a JSON object with the following fields:

Field	Description
`id`	Stable public sample identifier.
`file_name`	Relative path to the MP4 video.
`split`	`train` or `additional`.
`duration_s`	Nominal generation duration in seconds: 10.0 or 15.0 (not an ffprobe measurement).
`fps`	Video frame rate used by the release metadata.
`segment_frames`	Temporal window length used for segment-level training supervision.
`segment_prompts`	Ordered prompts describing consecutive video segments.
`global_prompt`	A compact description of the complete video trajectory.
`subject_domain`	Broad subject domain: `human`, `animal`, or `game_character`.
`event_type`	Fine-grained event type, such as `rockfall`, `natural_flash_flood`, or `generic_monster_spawn`.
`scene_type`	Fine-grained scene type, such as `bedroom`, `mine`, or `space_colony`.
`reaction_type`	Fine-grained reaction type, such as `flee`, `take_cover_or_brace`, or `shield_or_barrier_defense`.

Example:

{
  "id": "seerworld_000000",
  "file_name": "videos/000/seerworld_000000.mp4",
  "split": "train",
  "duration_s": 10.0,
  "fps": 24,
  "segment_frames": 81,
  "segment_prompts": ["...", "...", "..."],
  "global_prompt": "...",
  "subject_domain": "human",
  "event_type": "rockfall",
  "scene_type": "hiking_trail",
  "reaction_type": "take_cover_or_brace"
}

Optional LTX-2.5 training cache

The repository also provides precomputed inputs for reproducing the released SeerWorld training recipes without re-encoding every video:

Component	Contents	Files	Approx. unpacked size
`core`	Full-video latents and entity-reference latents	24,236	53 GiB
`conditions`	Per-segment text features	42,059	494 GiB

The two components are independent downloads. The condition cache is optional; users may instead run the text encoder locally. See training_cache/ltx-2.5/README.md for checksums, extraction, paths, and tensor details.

Intended use

SeerWorld-Data is intended for research on video generation, video world models, segment-autoregressive generation, long-horizon consistency, event-conditioned generation, and out-of-sight world evolution. Eligibility is determined by use: academic, nonprofit, individual, and company researchers may use it for non-commercial research, but it may not be used to train, fine-tune, evaluate, improve, or deploy a commercial model.

Data construction and quality control

The videos were constructed with generative AI under structured scenario specifications. Samples were screened for scene structure, camera motion, event realization, revisit behavior, visual integrity, and harmful content. Segment-level and global captions were produced from the rendered videos and checked as part of the data pipeline.

_{Note: Automated and model-assisted screening cannot guarantee that every sample is artifact-free. Some videos may contain generation artifacts, imperfect event rendering, identity drift, or captions that describe an event near a segment boundary. These long-tail imperfections should be considered when training or analyzing models.}

License

SeerWorld-Data is released under the custom SeerWorld Research-Only License v1.0 (research-only-v1.0). The dataset, its precomputed cache, and models trained from it may be used only for non-commercial research. Commercial-model training, evaluation, improvement, and deployment are prohibited. See LICENSE for the complete terms.

The optional cache does not include model weights. Users must obtain required upstream models separately and comply with their terms. See THIRD_PARTY_NOTICES.md; the dataset license does not replace third-party licenses or grant rights the authors do not hold.

Datasets:

Tommy-Bie
/

SeerWorld-Data

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SeerWorld-Data