README.md

metadata

license: openmdw-1.1
language:
  - en
tags:
  - video
  - autonomous-driving
  - simulation
  - multiview
configs:
  - config_name: default
    drop_labels: true
    data_files:
      - split: test
        path:
          - wsm/*/*.mp4
          - metadata.jsonl

Cosmos3 Driving Simulation Evaluation

Dataset Summary

This dataset contains 91 driving scenes with 11 camera views per scene: 1,001 World Scenario Map (WSM) control videos and 1,001 matching timestamped caption JSON files.

Dataset Description

The dataset provides structured scene controls and text prompts for multiview driving generation and evaluation. The WSM controls are derived from real-world driving data collected by NVIDIA and represent lane lines, object bounding boxes, and traffic-light states.

Included: WSM 4.0.1 rendered control videos, per-camera captions, scene/view manifests, measured video properties, and file checksums.

Not included: source RGB camera images or videos, LiDAR data, calibration, raw scene annotations, or model-generated outputs. WSM videos are abstract renderings of scene structure, not camera recordings. Original or restyled first-frame images are not included.

The released manifest identifies no separate standard or long-rollout subsets; control videos are 11.7–21.9 seconds long.

Dataset Owner(s)

NVIDIA Corporation

Dataset Creation Date

2026-10-20

Versioning

V1.0

No previous version.

License/Terms of Use

This dataset is released under the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).

Intended Usage

This dataset is intended for autonomous vehicle-related use cases only, including both commercial and non-commercial use in accordance with the applicable license terms. Its size and distribution across camera views support synthetic driving scene evaluation.

Public text-to-video evaluation of Cosmos driving simulation using WSM controls and accompanying text prompts. Specifically:

• evaluating Cosmos3 driving simulation conditioned on WSM controls.

This dataset is not intended for training perception or driving models.

Dataset Characterization

Data Collection Method: Automatic/Sensors.

Sensor-collected driving data is processed into WSM controls.

Labeling Method: Automated.

Human reviewers provide quality assurance for the automated annotations. Processing steps:

1. NVIDIA in-house models annotate lane lines and object bounding boxes on the source footage.
2. Human reviewers check the annotations for quality.
3. A script converts the reviewed annotations into WSM controls.
4. An internal NVIDIA caption model generates a text caption per camera view, used as a text prompt for evaluation.
5. All camera imagery is discarded. Only the WSM controls and text prompts are released.

Dataset Format

Modality: Video and Text.

WSM control videos in mp4 format, at 30fps. Each video shows the scene layout over time: lane lines, object bounding boxes, and traffic-light states.

Text captions are provided as 11 separate JSON files per scene, one per camera view.

Dataset Quantification

Record count: 91 multiview sequences (11 camera views per sequence).

Size bin: n<1K, where each record is one sequence.

Video length: 351–657 frames (11.7–21.9 seconds) at 30fps.

Resolution:

• Seven standard cameras: 1924 × 1084 for 53 sequences, or 1920 × 1080 for 38 sequences.
• Four fisheye cameras: 1720 × 1080 for 53 sequences, or 1350 × 1080 for 38 sequences.

Total storage size: approximately 2.59 GB (decimal).

Limitations

• Small sample. 91 scenes cover a limited range of road types, regions, traffic densities, and weather. Results on this evaluation subset may not generalize to other driving scenarios.
• One conditioning format. Results apply to WSM conditioning and may not transfer to other conditioning formats, such as depth, segmentation, or edge maps.
• Annotation error. Lane lines and bounding boxes come from automated models with human QA. Some missed or misplaced objects may remain.
• Layout only. WSM encodes scene geometry and traffic-light state. It does not encode appearance, lighting, weather, or road-surface detail.

Reference(s)

• WSM representation example. The top half shows WSM controls; the bottom half shows Cosmos-generated video. The released dataset contains WSM controls and text prompts, without generated video.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Privacy: The dataset contains no camera images, faces, license plates, or legible text. Road agents appear only as bounding boxes.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Files

wsm/scene-001/camera_front_wide_120fov.mp4
captions/scene-001/camera_front_wide_120fov.json
...
manifest.jsonl
metadata.jsonl
checksums.json

• manifest.jsonl: one record per scene, grouping all 11 views by camera_key, with repository-relative wsm_path and caption_path values.
• metadata.jsonl: one record per camera view, linking its control video to the complete caption text and measured video properties for the dataset viewer.
• checksums.json: SHA-256 and byte size for every other release payload file.
• scene-001 through scene-091 are release-local identifiers.

The viewer has 1,001 rows, not 1,001 independent scenes. Group rows by sample_id to recover the 91 multiview scenes. The test split contains the entire evaluation collection; no training split is supplied.

Browse the dataset

Use the built-in Dataset Viewer / Data Studio on this dataset page, with configuration default and split test. Each row contains one playable WSM video and its matching timestamped caption.

• Search for a scene ID such as scene-001 to find its 11 camera views.
• Use camera_key to identify the view, for example camera_front_wide_120fov.
• Read caption for the complete prompt. caption_path points to the matching structured JSON in the Files tab.
• Check width, height, fps, num_frames, and duration_seconds for the native control-video properties. caption_start_frame and caption_end_frame describe the caption window, not the full video duration.
• Download a complete scene using the command in the Download section below.

The built-in viewer displays camera videos individually; it is not a synchronized surround player. It shows only WSM controls and captions, not RGB recordings or LiDAR. Hosted browsing requires the data to be on the dataset's default branch and processed by Hugging Face; files proposed only in a pull request are not a verified live viewer. Restricted repositories require access.

Camera names

Each scene contains these camera keys. Match files by camera key, not directory listing order.

camera_front_wide_120fov
camera_cross_left_120fov
camera_cross_right_120fov
camera_rear_left_70fov
camera_rear_right_70fov
camera_rear_tele_30fov
camera_front_tele_30fov
camera_front_fisheye_200fov
camera_left_fisheye_200fov
camera_rear_fisheye_200fov
camera_right_fisheye_200fov

Captions and timing

Each JSON preserves the structured caption format:

{
  "caption_structured": {
    "chunk_0_300": {
      "caption": "[0.0s-2.0s]: ...",
      "start_frame": 0,
      "end_frame": 300
    }
  }
}

This is a schema illustration, not an additional sample. Use each file's actual chunk keys, frame bounds and timestamped text.

The captions are the timestamped Tier1 version, with a privacy cleanup of identifying text. They are not a new environmental prompt rewrite. The cleanup removes transcribed identifying values, including license-plate values, while retaining generic descriptions such as a vehicle having a visible plate. Timestamp strings, frame bounds and other JSON fields are preserved.

Control videos retain their original bytes, frame rate, dimensions and duration. A control video may be longer than its caption window. Use the caption's frame bounds when selecting a generation interval; do not assume that a caption describes the entire MP4. metadata.jsonl records measured control dimensions, frame count, frame rate and duration. The upload does not crop, retime, resize or re-encode the controls.

Captions are machine-generated descriptions and may contain errors. Privacy cleanup is not a correction of all scene descriptions or an independent verification of physical camera calibration.

Download

Download the files directly; there is no ZIP extraction step:

hf download nvidia/Cosmos3-Driving-Simulation-Evaluation \
  --repo-type dataset --local-dir driving-simulation-evaluation

For one scene, download its two folders plus the manifests:

hf download nvidia/Cosmos3-Driving-Simulation-Evaluation \
  --repo-type dataset --local-dir driving-simulation-evaluation \
  --include 'wsm/scene-001/*' 'captions/scene-001/*' \
            'manifest.jsonl' 'metadata.jsonl' 'README.md' 'checksums.json'

Authentication is required while repository access is restricted. Downloaded metadata uses relative paths and contains no internal storage locations.