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Read the Dataset Access Agreement: https://huggingface.co/datasets/Drexubery/WorldCrafter/blob/main/LICENSE. By submitting, you accept its academic-research-only terms. Please consider starring https://github.com/TencentARC/WorldCrafter to support our open-source work; this is optional.
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WorldCrafter dataset
High-quality videos with captions and camera annotations for camera-controlled video generation and WorldCrafter fine-tuning.
If you find this release useful, please star WorldCrafter on GitHub to support our open-source work. A star is appreciated but is not required for access.
Access agreement
This dataset is available for non-commercial academic research only. Read the Dataset Access Agreement, then sign in to Hugging Face, provide your institution and research purpose, and accept the agreement using the access form on this page. Download access is granted automatically to your Hugging Face account upon submission.
Contents
| 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
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 originalcutandcrop: alignment metadata for reproducing preprocessing.
Download
Sign in to Hugging Face, complete the access form on this page, and accept the agreement to receive download access automatically. Use your own account and 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.
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