| --- |
| pretty_name: MVISTA-4D |
| license: other |
| license_name: mixed-see-license-section |
| task_categories: |
| - robotics |
| language: |
| - en |
| tags: |
| - robotics |
| - robot-manipulation |
| - multi-view |
| - video |
| - depth |
| - point-cloud |
| - 4d |
| - world-model |
| - rlbench |
| - robotwin |
| - real-robot |
| viewer: false |
| --- |
| |
| # MVISTA-4D |
|
|
| Multi-view 4D robot-manipulation dataset spanning three sources — **RobotWin** (simulation), |
| **RLBench** (simulation), and a **real-robot** collection — used to train camera-controlled / |
| 4D generative world models. Each episode provides synchronized multi-view RGB + depth video plus |
| robot joint/action and camera parameters. |
|
|
| > **Heads-up:** the raw data is stored as **tar shards** (`raw_data.00–03.tar.part`, ~122 GB total), |
| > not as individually browsable files. Use the steps below to download, verify and reassemble. |
| |
| ## Contents |
| |
| ``` |
| raw_data.00.tar.part … raw_data.03.tar.part # 4 × 40G shards of the raw dataset |
| SHA256SUMS.raw_data_shards # checksums for the shards |
| dataset_tools/ # download / extract / preprocess / data-loading scripts |
| ├── README.md # full pipeline guide |
| ├── download_and_extract.sh # download → verify → merge → extract |
| ├── preprocessing/ # raw → training cache (Wan2.2 latents) |
| └── data_loading/ # TensorDataset classes that read the cache |
| ``` |
| |
| ## Quick start |
| |
| ```bash |
| pip install -U "huggingface_hub[hf_xet]" |
| |
| # Option A — use the helper script (recommended) |
| hf download ethenj/MVISTA-4D --repo-type dataset --include "dataset_tools/*" --local-dir . |
| bash dataset_tools/download_and_extract.sh # downloads shards, verifies, extracts |
| |
| # Option B — manual |
| hf download ethenj/MVISTA-4D --repo-type dataset \ |
| --include "raw_data.*.tar.part" "SHA256SUMS.raw_data_shards" --local-dir . |
| sha256sum -c SHA256SUMS.raw_data_shards |
| cat raw_data.*.tar.part | tar -x --strip-components=1 -f - |
| ``` |
| |
| After extraction you get three top-level directories: |
| |
| ``` |
| RLBench/ <task>/<variationN>/episodes/... cam_rgb_*.mp4, cam_depth_*.mp4, variation_descriptions.pkl, scene_data.npz |
| Robotwin/ multitask_small/<task>/ARX-X5+ARX-X5/demo_randomized/ |
| data/episodeN/ (48 surround_camera_* RGB/depth/segmentation mp4 streams) |
| data/episodeN.json, episodeN_joint_data.npz, instructions/*.json, _traj_data/*.pkl |
| multitask_dpt/ (2 extra tasks: rotate_qrcode + unprocessed/place_object_stand) |
| our_dataset/ real_robot/multiview_data_processed/task_XXXX/episode_XXXX/ |
| camera_*_color.mp4, camera_*_depth.mp4, metadata.npz, descriptions.json |
| ``` |
| |
| ## From raw data to training |
| |
| ``` |
| HF shards --download_and_extract.sh--> raw data (mp4 / npz / json / pkl) |
| --preprocessing/preprocess_*_{depth,xyz}.py--> cache (*.pth latents) # needs diffsynth + Wan2.2-TI2V-5B |
| --data_loading/ TensorDataset in train_*.py--> training |
| ``` |
| |
| See [`dataset_tools/README.md`](./dataset_tools/README.md) for full details on preprocessing and data loading. |
|
|
| ## Trained weights |
|
|
| Matching model checkpoints (13 main-model `.ckpt` + Action_VAE + i3d + TCN action models) are in the |
| companion model repo: **https://huggingface.co/ethenj/MVISTA-4D** |
| |
| ## License |
| |
| This release combines components with different terms — please respect each source's license: |
| |
| - **RLBench** subset — see the [RLBench](https://github.com/stepjam/RLBench) project license. |
| - **RobotWin** subset — see the [RobotWin](https://github.com/TianxingChen/RoboTwin) project license. |
| - **Real-robot** subset — released by the dataset authors. |
| - Tooling/code under `dataset_tools/` derives from the ReCamMaster codebase (MIT, Kuaishou Visual Generation and Interaction Center). |
|
|
| > Set a concrete top-level `license:` in the YAML header once you have confirmed the terms for redistribution. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the associated work. _(Add BibTeX here.)_ |
|
|
