Skywork / Matrix-3D

Matrix3D: Omnidirectional Explorable
3D World Generation

🌟 Introduction

Matrix-3D utilizes panoramic representation for wide-coverage omnidirectional explorable 3D world generation that combines conditional video generation and panoramic 3D reconstruction.

• Large-Scale Scene Generation : Compared to existing scene generation approaches, Matrix-3D supports the generation of broader, more expansive scenes that allow for complete 360-degree free exploration.
• High Controllability : Matrix-3D supports both text and image inputs, with customizable trajectories and infinite extensibility.
• Strong Generalization Capability : Built upon self-developed 3D data and video model priors, Matrix-3D enables the generation of diverse and high-quality 3D scenes.
• Speed-Quality Balance : Two types of panoramic 3D reconstruction methods are proposed to achieve rapid and detailed 3D reconstruction respectively.

📦 Installation

Clone the repo and create the environment:

# Clone the repository 
git clone --recursive https://github.com/SkyworkAI/Matrix-3D.git
cd Matrix-3D

# Create a new conda environment
conda create -n matrix3d python=3.10
conda activate matrix3d

# Install torch and torchvision (with GPU support, we use CUDA 12.4 Version)
pip3 install torch==2.7.1 torchvision==0.22.1

#Run installation script
chmod +x install.sh
./install.sh

💫 Pretrained Models

🎮 Usage

• 🔧 Checkpoint Download

python scripts/download_checkpoints.py

• 🔥 One-command 3D World Generation

Now you can generate a 3D world by just running a single command:

./generate.sh

Or you can choose to generate a 3D world step by step.

• 🖼️ Step 1: Text/Image to Panorama Image

You can either generate a panorama image from text prompt:

python code/panoramic_image_generation.py \
    --mode=t2p \
    --prompt="a vibrant, industrial-style scene, featuring a large warehouse with exposed brick walls, metal beams, and scattered barrels and crates, set against a backdrop of modern skyscrapers and lush greenery" \
    --output_path="./output/example1"

Or from an input image:

python code/panoramic_image_generation.py \
    --mode=i2p \
    --input_image_path="./data/image1.jpg" \
    --output_path="./output/example1"

The generated panorama image will be saved in the output/example1 folder.

• 📹 Step 2: Generate Panoramic Video

VISIBLE_GPU_NUM=1
torchrun --nproc_per_node ${VISIBLE_GPU_NUM} code/panoramic_image_to_video.py \
  --inout_dir="./output/example1"  \
  --resolution=720

You can switch the resolution option in [480,720] to perform video generation under 960 × 480 resolution or 1440 × 720 resolution. The generated panoramic tour video will be saved in output/example1/pano_video.mp4 . It will take about an hour to generate a 720p video on an A800 GPU. You can accelerate this process with multi-gpu inference by setting VISIBLE_GPU_NUM.

• 🏡 Step 3: Extract 3D Scene

Here we provide two options, one is high-quality optimization-based 3D scene reconstruction and another is efficient feed-forward 3D scene reconstruction.

To perform optimization-based reconstruction, run

 python code/panoramic_video_to_3DScene.py \
    --inout_dir="./output/example1" \
    --resolution=720

Modify the resolution option as the value used in panoramic video generation. The extracted 3D scene in .ply format will be saved in output/example1/generated_3dgs_opt.ply .

To perform feed-forward reconstruction, run

python code/panoramic_video_480p_to_3DScene_lrm.py \
--video_path="./data/case1/sample_video.mp4" \
--pose_path='./data/case1/sample_cam.json' \
--out_path='./output/example2'

The extracted 3D scene in .ply format and rendered perspective videos will be saved output/example2 . If you want to reconstruct 3D scene with another panorama videos and conditioned camera pose, just replace the video_path and pose_path accordingly.

🎬 Create Your Own

We provide three movement modes: Straight Travel , S-curve Travel , and Forward on the Right , which can be configured in --movement_mode in code/panoramic_image_to_video.py .

You can also provide your own camera trajectory in .json format and use it for video generation.

VISIBLE_GPU_NUM=1
torchrun --nproc_per_node ${VISIBLE_GPU_NUM} code/panoramic_image_to_video.py \
  --inout_dir="./output/example1"  \
  --resolution=720
  --json_path YOUR_TRAJECTORY_FILE.json

All camera matrices used in our project are world to camera matrices in opencv format. Please refer to the sample file ./data/test_cameras/test_cam_front.json , and use code/generate_example_camera.py to generate your own camera trajectory.

📚 Citation

If you find this project useful, please consider citing it as follows:

@article{yang2025matrix3d,
  title     = {Matrix-3D: Omnidirectional Explorable 3D World Generation},
  author    = {Zhongqi Yang and Wenhang Ge and Yuqi Li and Jiaqi Chen and Haoyuan Li and Mengyin An and Fei Kang and Hua Xue and Baixin Xu and Yuyang Yin and Eric Li and Yang Liu and Yikai Wang and Hao-Xiang Guo and Yahui Zhou},
  journal   = {arXiv preprint arXiv:2508.08086},
  year      = {2025}
}

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🤝 Acknowledgements

This project is built on top of the follows, please consider citing them if you find them useful:

• FLUX.1
• Wan2.1
• WorldGen
• MoGe
• nvdiffrast
• gaussian-splatting
• StableSR
• VEnhancer

📧 Contact

If you have any questions, please feel free post an issue.