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| # Matrix-Game: Interactive World Foundation Model | |
| <div style="display: flex; justify-content: center; gap: 10px;"> | |
| <a href="https://github.com/SkyworkAI/Matrix-Game"> | |
| <img src="https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white" alt="GitHub"> | |
| </a> | |
| <a href="#todo"> | |
| <img src="https://img.shields.io/badge/arXiv-Report-b31b1b?style=flat&logo=arxiv&logoColor=white" alt="arXiv"> | |
| </a> | |
| </div> | |
| ## π Overview | |
| **Matrix-Game** is a 17B-parameter Diffusion Transformer for generating high-resolution, physics-consistent videos in interactive game environments. Trained on large-scale data from Minecraft and Unreal Engine, it understands game physics like collisions, destruction, and item placement. Matrix-Game supports real-time, action-conditioned generation, adapting video content dynamically to user input. | |
| You can find more visualizations on our [website](#). | |
| ## π₯ Latest Updates | |
| * [2025-05] π Initial release of Matrix-Game | |
| ## π Performance Comparison | |
| ### GameWorld Score Benchmark Comparison | |
| | Model | Image Quality β | Aesthetic β | Temporal Cons. β | Motion Smooth. β | Keyboard Acc. β | Mouse Acc. β | 3D Cons. β | | |
| |-----------|------------------|-------------|-------------------|-------------------|------------------|---------------|-------------| | |
| | Oasis | 0.65 | 0.48 | 0.94 | **0.98** | 0.77 | 0.56 | 0.56 | | |
| | MineWorld | 0.69 | 0.47 | 0.95 | **0.98** | 0.86 | 0.64 | 0.51 | | |
| | **Ours** | **0.72** | **0.49** | **0.97** | **0.98** | **0.95** | **0.95** | **0.76** | | |
| **Metric Descriptions**: | |
| - **Image Quality** / **Aesthetic**: Visual fidelity and perceptual appeal of generated frames | |
| - **Temporal Cons.** / **Motion Smooth.**: Temporal coherence and smoothness between frames | |
| - **Keyboard Acc.** / **Mouse Acc.**: Accuracy in following user control signals | |
| - **3D Cons.**: Geometric stability and physical plausibility over time | |
| ### Human Evaluation | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Group</th> | |
| <th>Method</th> | |
| <th>Overall Quality (%)</th> | |
| <th>Controllability (%)</th> | |
| <th>Visual Quality (%)</th> | |
| <th>Temporal Consistency (%)</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td rowspan="3">Group A</td> | |
| <td>Oasis</td> | |
| <td>0.16</td> | |
| <td>0.33</td> | |
| <td>0.00</td> | |
| <td>0.16</td> | |
| </tr> | |
| <tr> | |
| <td>MineWorld</td> | |
| <td>3.78</td> | |
| <td>5.58</td> | |
| <td>1.32</td> | |
| <td>13.82</td> | |
| </tr> | |
| <tr> | |
| <td><strong>Ours</strong></td> | |
| <td><strong>96.05</strong></td> | |
| <td><strong>94.09</strong></td> | |
| <td><strong>98.68</strong></td> | |
| <td><strong>86.02</strong></td> | |
| </tr> | |
| <tr> | |
| <td rowspan="3">Group B</td> | |
| <td>Oasis</td> | |
| <td>0.66</td> | |
| <td>0.82</td> | |
| <td>0.75</td> | |
| <td>0.66</td> | |
| </tr> | |
| <tr> | |
| <td>MineWorld</td> | |
| <td>2.79</td> | |
| <td>5.76</td> | |
| <td>1.48</td> | |
| <td>6.25</td> | |
| </tr> | |
| <tr> | |
| <td><strong>Ours</strong></td> | |
| <td><strong>96.55</strong></td> | |
| <td><strong>93.42</strong></td> | |
| <td><strong>97.77</strong></td> | |
| <td><strong>93.09</strong></td> | |
| </tr> | |
| <tr> | |
| <td rowspan="3">Average</td> | |
| <td>Oasis</td> | |
| <td>0.41</td> | |
| <td>0.58</td> | |
| <td>0.38</td> | |
| <td>0.41</td> | |
| </tr> | |
| <tr> | |
| <td>MineWorld</td> | |
| <td>3.29</td> | |
| <td>5.67</td> | |
| <td>1.40</td> | |
| <td>10.04</td> | |
| </tr> | |
| <tr> | |
| <td><strong>Ours</strong></td> | |
| <td><strong>96.30</strong></td> | |
| <td><strong>93.76</strong></td> | |
| <td><strong>98.23</strong></td> | |
| <td><strong>89.56</strong></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| > Double-blind human evaluation by two independent groups across four key dimensions: **Overall Quality**, **Controllability**, **Visual Quality**, and **Temporal Consistency**. | |
| > Scores represent the percentage of pairwise comparisons in which each method was preferred. Matrix-Game consistently outperforms prior models across all metrics and both groups. | |
| ## π οΈ Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/SkyworkAI/Matrix-Game.git | |
| cd Matrix-Game | |
| ``` | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## π Quick Start | |
| ```bash | |
| bash run_inference.sh | |
| ``` | |
| ## π€ Contributing | |
| We welcome contributions! Please see our [contributing guidelines](CONTRIBUTING.md) for more details. | |
| ## β Acknowledgements | |
| We would like to express our gratitude to: | |
| - [Diffusers](https://github.com/huggingface/diffusers) for their excellent diffusion model framework | |
| - [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) for their strong base model | |
| We are grateful to the broader research community for their open exploration and contributions to the field of interactive world generation. | |
| ## π License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |