| --- |
| license: apache-2.0 |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - image-to-video |
| tags: |
| - robotics |
| - world-models |
| --- |
| |
| # MultiWorld Dataset |
|
|
| ## Dataset Summary |
|
|
| **MultiWorld** is a large-scale multi-agent multi-view video dataset collected for training video world models. It contains two complementary sources of data: |
|
|
| 1. **It Takes Two Gameplay Dataset**: 100+ hours of real human gameplay from the cooperative action-adventure game *It Takes Two*, featuring dual-agent synchronized actions with distinct first-person viewpoints. |
| 2. **RoboFactory Manipulation Dataset**: Multi-robot manipulation trajectories spanning 4 tasks with 2-4 agents and variable camera viewpoints, including both success and failure episodes. |
|
|
| This dataset is the official release accompanying the paper [MultiWorld: Scalable Multi-Agent Multi-View Video World Models](https://huggingface.co/papers/2604.18564). |
|
|
| - **Homepage:** https://multi-world.github.io |
| - **Repository:** https://github.com/CIntellifusion/MultiWorld |
| - **Paper:** [arXiv:2604.18564](https://huggingface.co/papers/2604.18564) |
|
|
| --- |
|
|
| ## Sample Usage |
|
|
| ### Dataset Download |
|
|
| You can download the dataset using the Hugging Face CLI: |
|
|
| ```bash |
| hf auth login |
| hf download Haoyuwu/MultiWorldData --repo-type dataset \ |
| --local-dir ./data |
| bash preprocess/untar_chunks.sh |
| ``` |
|
|
| After running `preprocess/untar_chunks.sh`, the archives are extracted to: |
| - `data/ittakestwo_release/` — It Takes Two dataset |
| - `data/robots_release/` — Robotics dataset |
|
|
| --- |
|
|
| ## Dataset Details |
|
|
| ### It Takes Two Gameplay |
|
|
| | Property | Value | |
| |----------|-------| |
| | **Total Duration** | 100+ hours | |
| | **Frame Rate** | 60 FPS | |
| | **Resolution** | 480 × 960 | |
| | **Agents** | 2 players | |
| | **Viewpoints** | 2 distinct first-person views per episode | |
| | **Actions** | Synchronized keyboard and mouse actions per agent | |
| | **Modality** | RGB video + discrete/continuous action vectors | |
|
|
| The gameplay videos are captured from real human players cooperating in the game. Each frame is accompanied by per-agent action labels capturing keyboard presses and mouse movements. |
|
|
| ### RoboFactory Manipulation |
|
|
| | Property | Value | |
| |----------|-------| |
| | **Tasks** | 4 multi-robot manipulation tasks | |
| | **Agents** | 2–4 robots per task | |
| | **Viewpoints** | Variable camera configurations per task | |
| | **Resolution** | 256 × 320 | |
| | **Success Episodes** | 1,000 per task | |
| | **Failure Episodes** | 2,000 per task | |
| | **Modality** | RGB video + robot proprioception + actions | |
|
|
| Tasks include collaborative stacking, pushing, and pick-and-place scenarios. Both successful and failed trajectories are included to support learning robust world models and failure prediction. |
|
|
|
|
| --- |
|
|
| ### Possible Usage |
|
|
| The dataset is intended for research in: |
| - Video world models |
| - Multi-agent video generation |
| - Multi-view consistent video generation. |
|
|
| --- |
|
|
| ### Contact |
|
|
| For questions about the dataset, please open an issue on the [GitHub repository](https://github.com/CIntellifusion/MultiWorld) or contact the authors. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wu2025multiworld, |
| title={MultiWorld: Scalable Multi-Agent Multi-View Video World Models}, |
| author={Wu, Haoyu and Yu, Jiwen and Zou, Yingtian and Liu, Xihui}, |
| journal={arXiv preprint arXiv:2604.18564}, |
| year={2026} |
| } |
| ``` |
