README.md

metadata

title: Matrix
emoji: 🐟
colorFrom: blue
colorTo: blue
sdk: docker
app_file: server.py
pinned: true
short_description: AI Gaming server
app_port: 8080
disable_embedding: false

Matrix-Game: Interactive World Foundation Model

[🤗 Huggingface] [📖 Technical Report] [🚀 Project Website]

📝 Overview

Matrix-Game is a 17B-parameter interactive world foundation model for controllable game world generation.

✨ Key Features

• 🎯 Feature 1: Interactive Generation. A diffusion-based image-to-world model that generates high-quality videos conditioned on keyboard and mouse inputs, enabling fine-grained control and dynamic scene evolution.
• 🚀 Feature 2: GameWorld Score. A comprehensive benchmark for evaluating Minecraft world models across four key dimensions, including visual quality, temporal quality, action controllability, and physical rule understanding.
• 💡 Feature 3: Matrix-Game Dataset A large-scale Minecraft dataset with fine-grained action annotations, supporting scalable training for interactive and physically grounded world modeling.

🔥 Latest Updates

• [2025-05] 🎉 Initial release of Matrix-Game Model

🚀 Performance Comparison

GameWorld Score Benchmark Comparison

Model	Image Quality ↑	Aesthetic Quality ↑	Temporal Cons. ↑	Motion Smooth. ↑	Keyboard Acc. ↑	Mouse Acc. ↑	3D Cons. ↑
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 Consistency / Motion Smoothness: Temporal coherence and smoothness between frames

• Keyboard Accuracy / Mouse Accuracy: Accuracy in following user control signals

• 3D Consistency: Geometric stability and physical plausibility over time

  Please check our GameWorld benchmark for detailed implementation.

Human Evaluation

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.

🚀 Quick Start

# clone the repository:
git clone https://github.com/SkyworkAI/Matrix-Game.git
cd Matrix-Game

# install dependencies:
pip install -r requirements.txt

# install apex and FlashAttention-3
# Our project also depends on [apex](https://github.com/NVIDIA/apex) and [FlashAttention-3](https://github.com/Dao-AILab/flash-attention)

# inference
bash run_inference.sh

🔧 Hardware Requirements

• GPU:
  • NVIDIA A100/H100
• VRAM:
  • Requires ≥80GB of GPU memory for a single 65-frame video inference.

⭐ Acknowledgements

We would like to express our gratitude to:

• Diffusers for their excellent diffusion model framework
• HunyuanVideo for their strong base model
• MineDojo for their Minecraft video dataset
• MineRL for their excellent gym framework
• Video-Pre-Training for their accurate Inverse Dynamics Model
• GameFactory for their idea of action control module

We are grateful to the broader research community for their open exploration and contributions to the field of interactive world generation.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📎 Citation

If you find this project useful, please cite our paper:

@article{zhang2025matrixgame,
  title     = {Matrix-Game: Interactive World Foundation Model},
  author    = {Yifan Zhang and Chunli Peng and Boyang Wang and Puyi Wang and Qingcheng Zhu and Zedong Gao and Eric Li and Yang Liu and Yahui Zhou},
  journal   = {arXiv},
  year      = {2025}
}