Demo assets: you can download demo_results/vigor/mesh.obj to visualize the colored mesh locally.

For more visualization results, please see the Project Page.

We propose Sat2Density++, a novel framework for high-quality street-view video synthesis from satellite imagery:

• Minimal Training Requirements: It only requires $N$ pairs of GPS-matched satellite and street-view panorama images. No video data and no 3D annotations are needed during training.
• Flexible Inference: Given a single satellite image and a user-defined driving trajectory, the model generates a temporally consistent panorama video.
• 3D Scene Reconstruction: Given a single satellite image, it can generate a colored 3D mesh scene (rough but useful for visualization).
• Superior Performance: Sat2Density++ significantly outperforms the previous conference version (Sat2Density) in 3D shape, multi-view consistency, faithfulness to the reference satellite image, and image quality. Visual comparisons are available on our Project Page.
• Arbitrary Length: Theoretically, our method can generate street-view videos of arbitrary length, requiring only a simple adjustment to the total frame count hyperparameter during the trajectory generation process.

conda create -n sat2densitypp python=3.8
conda activate sat2densitypp

# Recommended: PyTorch 2.4.1
# Choose the appropriate installation command based on your CUDA version
# See: https://pytorch.org/get-started/locally/

# CUDA 12.4 example 
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.4 -c pytorch -c nvidia

pip install -r requirements.txt

Download the pretrained weights from Hugging Face and place them in the checkpoints/ directory:

Pretrained weights: https://huggingface.co/qian43/Sat2Densitypp

checkpoints/
├── cvact/
│   ├── generator_config.json
│   └── generator_smooth.pth
├── cvusa/
│   ├── generator_config.json
│   └── generator_smooth.pth
└── vigor/
    ├── generator_config.json
    └── generator_smooth.pth

We provide bundled demo data for immediate testing:

• CVACT: 3 satellite images + panoramas + sky masks
• CVUSA: 2 satellite images + panoramas + sky masks
• VIGOR: 5 satellite images + panoramas + sky masks

Sky masks can be obtained with any model as long as the format matches.
(We will release the processing code for sky mask generation later.)

# CVACT dataset
bash demos/inference_demo/demo_cvact.sh 0

# CVUSA dataset
bash demos/inference_demo/demo_cvusa.sh 0

# VIGOR dataset
bash demos/inference_demo/demo_vigor.sh 0

where 0 is the GPU device ID.

We also provide demo results for quick preview:

• demo_results/vigor/mesh.obj: Color mesh visualization example
• demo_results/vigor/vid.gif: Video preview

Generated results will be saved under: work_dirs/visualize_result/{satellite_image_name}/{dataset}seed{seed}/

Typical outputs include:

• vid.mp4: Combined satellite + street-view video
• vid.gif: GIF preview
• save_street_only_vid.mp4: Pure street-view video
• pred_street.png: Street-view rendering
• pred_satrgb.png / pred_satdep.png: Satellite-view renderings
• mesh.obj: Extracted 3D mesh model (supports color mesh visualization)
• save_sat/: Rendered satellite frames

python inference.py \
    --model checkpoints/s2d_vigor_combine05/checkpoint-437500.pth \
    --sat_img_path demo_data/VIGOR/satellite/satellite_41.88584553507432_-87.67181147737129.png \
    --sky_path demo_data/VIGOR/panorama/2AJ82KxYyUg0pT6dGdO7PQ,41.885744,-87.624162,.jpg \
    --position_path demo_data/VIGOR/pixels_satellite_41.88584553507432_-87.67181147737129.csv \
    --save_video True \
    --save_shape True \
    --save_sky True \
    --save_street True \
    --save_sat True \
    --seed 0

Mix and match satellite images with different sky conditions:

# List available demo images
ls demo_data/VIGOR/satellite/
ls demo_data/VIGOR/panorama/

# Run with different combination
python inference.py \
    --model checkpoints/s2d_vigor_combine05/checkpoint-437500.pth \
    --sat_img_path demo_data/VIGOR/satellite/YOUR_CHOICE.png \
    --sky_path demo_data/VIGOR/panorama/YOUR_SKY_CHOICE.jpg \
    --position_path demo_data/VIGOR/pixels_xxx.csv \
    --save_video True

To use your own satellite images or create new trajectories:

python make_trajectory.py \
    --input_img_path your_satellite_image.png \
    --work_dir work_dirs/visualize_result/

This will open an interactive window where you can draw a path on the satellite image. The trajectory will be saved as pixels.csv.

Note: This requires a graphical interface (X11 or local display). If running on a remote server, use ssh -X to enable X11 forwarding.

Sat2Densitypp_open/
├── inference.py              # Main inference script
├── make_trajectory.py        # Interactive trajectory creation tool
├── models/                   # Model definitions
├── utils/                    # Utility functions
├── demos/
│   └── inference_demo/       # Demo scripts
│       ├── demo_cvact.sh
│       ├── demo_cvusa.sh
│       └── demo_vigor.sh
├── demo_data/                # Bundled demo data (ready to use!)
│   ├── CVACT/
│   ├── CVUSA/
│   └── VIGOR/
├── demo_results/             # Example outputs for quick preview
├── requirements.txt          # Python dependencies
├── CHANGELOG.md              # Changes from original codebase
└── README.md                 # This file

See demo_data/README.md for detailed information about the bundled demo data.

• CVACT and CVUSA are prepared in the same format as our conference version (for training).
• VIGOR is open-source. We will release the training/inference lists soon.
• Full datasets are only needed for training. For inference, you can use the bundled demo data or your own satellite/panorama images.

If our work helps your research, please cite:

@ARTICLE{Qian_2026_Sat2Densitypp,
    author={Qian, Ming and Tan, Bin and Wang, Qiuyu and Zheng, Xianwei and Xiong, Hanjiang and Xia, Gui-Song and Shen, Yujun and Xue, Nan},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={Seeing Through Satellite Images at Street Views}, 
    year={2026},
    volume={48},
    number={5},
    pages={5692-5709},
    doi={10.1109/TPAMI.2026.3652860}}

@InProceedings{Qian_2023_Sat2Density,
    author    = {Qian, Ming and Xiong, Jincheng and Xia, Gui-Song and Xue, Nan},
    title     = {Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {3683-3692}
}

We welcome any questions, discussions, or feedback:

• 📝 Issues: Please submit issues on this repository
• 🌐 Project Page: https://qianmingduowan.github.io/sat2density-pp/

This project is released under the MIT License. See LICENSE file for details.

This work is built upon the following excellent open-source projects:

• EG3D - 3D-aware generative models
• StyleGAN2 - High-quality image generation
• PyTorch - Deep learning framework
• CARVER - framework

Thanks to all researchers and developers who contributed to this project!

Thanks to Yuanbo Yang, Shuailei Ma, Wen Wang, Zimin Xia, Zifan Shi, Xingxing Weng, and Chao Pang for their fruitful discussions.

If this project helps your research or work, please:

• ⭐ Star this repository to support our work
• 📝 Cite our paper (see Citation section below)
• 🔗 Share with researchers who might be interested

Your support motivates us to keep improving! 🚀

• [ICLR 2026] Sat3DGen accepted: Our new work can generate higher-quality 3D representations from reference single satellite image input and supports more downstream applications. Code will be released soon. Code will be released on: https://github.com/qianmingduowan/Sat3DGen

• Release data splits and sky masks
• Release Gradio demo
• Release training code