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license: apache-2.0
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---
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license: apache-2.0
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size_categories:
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- 100K<n<1M
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---
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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.
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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
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- **Repository:** https://github.com/CIntellifusion/MultiWorld
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- **Paper:** [arXiv:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX)
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---
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## Dataset Details
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### It Takes Two Gameplay
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| Property | Value |
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|----------|-------|
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| **Total Duration** | 100+ hours |
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| **Frame Rate** | 60 FPS |
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| **Resolution** | 480 × 960 |
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| **Agents** | 2 players |
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| **Viewpoints** | 2 distinct first-person views per episode |
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| **Actions** | Synchronized keyboard and mouse actions per agent |
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| **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 |
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|----------|-------|
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| **Tasks** | 4 multi-robot manipulation tasks |
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| **Agents** | 2–4 robots per task |
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| **Viewpoints** | Variable camera configurations per task |
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| **Resolution** | 256 × 320 |
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| **Success Episodes** | 1,000 per task |
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| **Failure Episodes** | 2,000 per task |
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| **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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---
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### Possible Usage
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The dataset is intended for research in:
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- Video world models
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- Multi-agent video generation
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- Multi-view consistent video generation.
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---
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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.
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