| --- |
| pretty_name: WORL LingBot Training Data |
| license: cc-by-4.0 |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - worl |
| - lingbot |
| - world-model |
| - image-to-video |
| - camera-control |
| - synthetic |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: manifest.jsonl |
| --- |
| |
| # WORL LingBot Training Data |
|
|
| **WORL-Lingbot-Training-Data** is an OpenScene/revisit conditioning dataset for |
| training camera-controlled video world models. It contains text prompts, |
| initial scene images and camera trajectories in the portable WORL chain format. |
|
|
| Related model: [WORL-Lingbot](https://huggingface.co/Efficient-Large-Model/WORL-Lingbot). |
|
|
| ## Dataset contents |
|
|
| | Item | Count / format | |
| | --- | --- | |
| | Training records | 2,218 | |
| | First-frame images | 1,419 PNG images and 799 JPEG images | |
| | Camera arrays | 4,436 NumPy files: poses and intrinsics | |
| | Chain lengths | 912 two-clip chains; 601 three-clip chains; 705 four-clip chains | |
| | Clip geometry | 81 frames per clip, stride 80, camera timeline at 16 FPS | |
| | First-frame dimensions | 1,419 images at 832 × 464; 799 images at 1536 × 1024 | |
| | License | CC BY 4.0 | |
|
|
| The package contains conditioning inputs, not generated video clips or |
| preference labels. The first-frame images retain their source dimensions; |
| the training pipeline is responsible for any required resizing and consistent |
| adjustment of camera intrinsics. |
|
|
| ## Creation and scope |
|
|
| The WORL team created the OpenScene/revisit data and reports that the initial |
| scene imagery was produced using gpt-image. The portable bundle was prepared |
| from a 2,400-row source collection. It retains 2,218 records after excluding |
| 182 rows whose source stride was incompatible with the LingBot geometry. |
|
|
| The source labels cover OpenScene, revisiting, rotation and shared-context |
| variants. This release provides the team's LingBot conditioning bundle; it is |
| not the full NVRM video preference corpus. The available model export does not |
| record an exact checkpoint-to-dataset binding. |
|
|
| ## Download |
|
|
| ```bash |
| hf download Efficient-Large-Model/WORL-Lingbot-Training-Data \ |
| --repo-type dataset \ |
| --local-dir data/WORL-Lingbot-Training-Data |
| ``` |
|
|
| Pin a Hub commit with `--revision` when reproducing an experiment. |
|
|
| ## File layout |
|
|
| ```text |
| manifest.jsonl Training records in worl.chain.v1 format |
| assets/first_frames/ First-frame images |
| assets/camera/ Camera-to-world poses and camera intrinsics |
| assets.lock.json Size and SHA-256 for every referenced asset |
| dataset-card.md Short data card used by the WORL data preflight |
| PUBLICATION.json Source snapshot, data identities and validation summary |
| RELEASE_MANIFEST.json SHA-256 inventory of the public files |
| LICENSE.txt Creative Commons Attribution 4.0 International |
| README.md This dataset card |
| ``` |
|
|
| Each record contains a stable `id`, `sampling_index`, relative first-frame |
| path, camera metadata and ordered clip prompts with source frame windows. |
| `provenance.license` is `CC-BY-4.0`. Asset paths are relative to the dataset root. |
|
|
| | Camera field | Meaning | |
| | --- | --- | |
| | `coordinates` | `opencv_c2w`: camera-to-world convention | |
| | `poses_c2w` | Relative path to per-frame 4 × 4 pose matrices | |
| | `intrinsics` | Relative path to pixel-unit camera intrinsics | |
| | `fps` | Camera timeline frame rate, 16 FPS | |
|
|
| Example for reading one record locally: |
|
|
| ```python |
| import json |
| from pathlib import Path |
| import numpy as np |
| from PIL import Image |
| |
| root = Path("data/WORL-Lingbot-Training-Data") |
| with (root / "manifest.jsonl").open() as stream: |
| record = json.loads(next(stream)) |
| |
| image = Image.open(root / record["first_frame"]["uri"]).convert("RGB") |
| camera = record["conditions"]["camera"] |
| poses = np.load(root / camera["poses_c2w"], allow_pickle=False) |
| intrinsics = np.load(root / camera["intrinsics"], allow_pickle=False) |
| prompts = [clip["prompt"] for clip in record["clips"]] |
| ``` |
|
|
| ## Provenance and integrity |
|
|
| This public package derives from |
| [qyoo/lingbot-top2400-training-data at ee75eb9e711b](https://huggingface.co/datasets/qyoo/lingbot-top2400-training-data/tree/ee75eb9e711ba55ec9eac9356935a9dd21bf352f). |
| Images, camera arrays, prompts, record IDs, sampling order and clip windows are |
| preserved. Record provenance fields and documentation were updated for the |
| public release under CC BY 4.0. |
|
|
| The updated manifest and data card produce a new WORL dataset revision. |
| `PUBLICATION.json` records both the source identity and this release's identity; |
| the Hub commit is a separate version identifier. `assets.lock.json` covers all |
| 6,654 media assets. The public metadata is checked with WORL's CPU dataset |
| preflight for schema, asset hashes, image geometry, camera arrays and sampling |
| buckets. This check does not represent a new training run. |
|
|
| ## Intended use and limitations |
|
|
| The dataset supports research and development of camera-conditioned video |
| generation and world-model reinforcement learning. Synthetic imagery and |
| descriptions may contain visual errors, implausible geometry or bias. The |
| camera conditions do not establish physically accurate ground-truth scenes. |
| The bundle contains two- to four-clip chains and does not by itself reproduce |
| every experiment in the WORL manuscript. |
|
|
| ## License and attribution |
|
|
| The dataset is licensed under |
| [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). |
| The full terms are in [LICENSE.txt](LICENSE.txt). |
|
|
| When sharing this dataset or an adaptation, credit the WORL team, provide the |
| dataset and license links, and indicate changes. A suggested attribution is: |
|
|
| > WORL team, *WORL LingBot Training Data*, |
| > https://huggingface.co/datasets/Efficient-Large-Model/WORL-Lingbot-Training-Data, |
| > licensed under CC BY 4.0. |
|
|
| Maintainer: Yuyang Zhao, NVIDIA Research Singapore & Efficient AI. |
|
|
