Datasets:

Efficient-Large-Model
/

WORL-Lingbot-Training-Data

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.

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

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

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:

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. 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. The full terms are in 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.