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| license: apache-2.0 |
| size_categories: |
| - 100K<n<1M |
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| # MultiWorld Dataset |
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| ## Dataset Summary |
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| **MultiWorld** is a large-scale multi-agent multi-view video dataset collected for training video world models. It contains two complementary sources of data: |
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| 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. |
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| This dataset is the official release accompanying the paper *"MultiWorld: Scalable Multi-Agent Multi-View Video World Models"*. |
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| - **Homepage:** https://multi-world.github.io |
| - **Repository:** https://github.com/CIntellifusion/MultiWorld |
| - **Paper:** [arXiv:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX) |
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| ## Dataset Details |
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| ### It Takes Two Gameplay |
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| | 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 | |
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| 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. |
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| ### RoboFactory Manipulation |
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| | 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 | |
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| 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. |
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| ### Possible Usage |
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| The dataset is intended for research in: |
| - Video world models |
| - Multi-agent video generation |
| - Multi-view consistent video generation. |
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| ### Contact |
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| For questions about the dataset, please open an issue on the [GitHub repository](https://github.com/CIntellifusion/MultiWorld) or contact the authors. |
