Only one application is needed: complete and submit the Google Form linked below. Access is processed automatically; no separate Hugging Face access request is required.

👉 OPEN GOOGLE APPLICATION FORM

Complete the Google Form: enter your exact HF username and academic details, upload your GitHub Star screenshot, and accept the dataset agreements.
Submit the form to trigger automatic access processing.
Wait a few minutes, then refresh this page while signed in to the HF account you specified.

You do not need to click the “Request access” button below. If you already clicked it and see “awaiting a review”, simply complete the Google Form above.

For non-commercial academic research only. Application details and screenshots are kept privately for the dataset maintainer. No manual screenshot review is required. If access is still unavailable after 30 minutes, check your HF username or contact the maintainer.

WorldCrafter dataset

High-quality videos with captions and camera annotations for camera-controlled video generation and WorldCrafter fine-tuning.

Project code · Project page

Preview

Most videos in the WorldCrafter dataset feature subject-following camera motion, helping models learn camera control for tracking and following subjects.

WorldCrafter Benchmark

We also release WorldCrafter Benchmark: 144 image–prompt pairs for evaluating first-person scene exploration and third-person subject following.

Group	Images and prompts
First person — scenes, indoor and outdoor environments	61
Third person — agents and subject following	83

The benchmark brings together images from Pexels, Project Genie, and Happy Oyster. All image copyrights remain with their original creators and respective rights holders. Original images, prompt text, and per-image source records are preserved.

After completing the dataset access agreement, download only the benchmark (approximately 76 MB):

pip install huggingface_hub
hf auth login
hf download Drexubery/WorldCrafter benchmark/download_benchmark.py --repo-type dataset --local-dir .
python benchmark/download_benchmark.py --local-dir WorldCrafter-benchmark

Use --split firstperson or --split thirdperson to download one group. See the benchmark README for the layout, source credits, and usage.

Access agreement

This dataset is available for non-commercial academic research only.

Complete the application and upload your GitHub Star screenshot. Enter your own exact Hugging Face username, provide your academic details, and accept the Dataset Access Agreement. Google sign-in is required to upload the image.
Return to this dataset page using the HF account you specified and refresh after a few minutes. Access is granted automatically after successful processing; no manual screenshot review is required.

No separate Hugging Face access request is required. You do not need to click Request access; submitting the Google Form triggers automatic processing. Academic details and screenshots are kept privately for the dataset maintainer. If access remains unavailable after 30 minutes, check your username or contact the dataset maintainer.

Item	Count
Unique source MP4 videos	19,972
Camera annotation JSON records	20,434
Valid target chunk entries	77,642

Videos are deduplicated by their source-file path. Multiple sample records can refer to different segments of one source video. The release preserves complete source MP4 files and complete camera histories; the valid index selects target chunks, rather than trimming away history required by the training sampler.

The dataset includes the existing video captions. Precomputed latent features and depth maps are not included.

Layout

benchmark/                       # evaluation images, prompts, preview, and downloader
archives/videos-00000.tar          # bounded video shards
archives/cameras-00000.tar.gz      # portable camera annotations
metadata/samples.jsonl             # sample-to-video mapping, existing caption, crop/cut
metadata/videos.jsonl              # unique videos, byte sizes and SHA-256
metadata/cameras.jsonl             # camera record sizes and SHA-256
metadata/valid_chunk_indices.json  # unchanged selected feature-name -> chunk IDs
metadata/shards.json               # archive sizes, members and SHA-256
scripts/download_dataset.py        # authenticated download, verification and extraction
release.json                       # dataset version and provenance

Extract all archives into the same dataset directory. Video members live under videos/ and annotation members under cameras/. Public paths are relative and do not depend on internal storage mounts.

Camera and sampling conventions

Camera poses and intrinsics are estimated using Depth Anything 3 (Nested Giant–Large, version 1.1), a model for reconstructing scene geometry from multiple images. We sample 9 anchor frames from each 33-frame processed chunk at offsets [0, 4, 8, 12, 16, 20, 24, 28, 32] and estimate them jointly with the video's reference frame. The predicted poses are converted to camera-to-world transforms and expressed relative to the video reference camera and to each chunk's first anchor. These annotations are model estimates at the sampled anchors, rather than measured ground truth or dense per-frame poses.

camera_pose_rel_video_c2w: [chunks, 9, 4, 4], camera-to-world poses relative to the video's reference camera.
camera_pose_rel_chunk_c2w: [chunks, 9, 4, 4], poses relative to the first anchor of each chunk.
camera_K_norm: [chunks, 9, 4], normalized [fx, fy, cx, cy] using the model-processed image dimensions.
camera_valid_mask: [chunks, 9], validity flags.
camera_anchor_frame_idx: source-video frame indices.
camera_anchor_processed_frame_idx: indices after temporal selection.
selected_start_frame, selected_end_frame, stride, fps, and the sample's original cut and crop: alignment metadata for reproducing preprocessing.

Download

Complete the Google application form and wait for access to be processed. Then sign in to the same Hugging Face account locally. Do not share access tokens:

pip install huggingface_hub
hf auth login
hf download Drexubery/WorldCrafter --repo-type dataset --local-dir WorldCrafter-data
python WorldCrafter-data/scripts/download_dataset.py --root WorldCrafter-data --verify --extract

Keep approximately 400 GB free if retaining both downloaded archives and extracted videos. Exact archive sizes and checksums are recorded in metadata/shards.json.

Downloads last month: 158

Total file size:

182 GB

Datasets:

Drexubery
/

WorldCrafter

Get access through the Google Form