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> </p>
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> <p align="center" float="left">
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> <img src="docs/figures/demo/case2.sat.gif" alt="drawing" width="19%">
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> <img src="docs/figures/demo-density/case2.gif" alt="drawing" width="38%">
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> <img src="docs/figures/demo/case2.render.gif" alt="drawing" width="38%">
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> </p>
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> <p align="center" float="left">
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> <img src="docs/figures/demo/case3.sat.gif" alt="drawing" width="19%">
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> <img src="docs/figures/demo-density/case3.gif" alt="drawing" width="38%">
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> <img src="docs/figures/demo/case3.render.gif" alt="drawing" width="38%">
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> </p>
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> <p align="center" float="left">
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> <img src="docs/figures/demo/case4.sat.gif" alt="drawing" width="19%">
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> <img src="docs/figures/demo-density/case4.gif" alt="drawing" width="38%">
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> <img src="docs/figures/demo/case4.render.gif" alt="drawing" width="38%">
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> </p>
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## Checkpoints Downloading
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> Two checkpoints for CVACT and CVUSA can be found from [thisurl](https://github.com/sat2density/checkpoints/releases). You can also run the following command to download them.
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```
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bash scripts/download_weights.sh
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```
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## QuickStart Demo
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### Video Synthesis
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#### Example Usage
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```
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python test.py --yaml=sat2density_cvact \
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--test_ckpt_path=2u87bj8w \
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--task=test_vid \
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--demo_img=demo_img/case1/satview-input.png \
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--sty_img=demo_img/case1/groundview.image.png \
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--save_dir=results/case1
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```
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####
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### Illumination Interpolation
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<!-- ```
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bash inference/quick_demo_interpolation.sh
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``` -->
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```
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python test.py --task=test_interpolation \
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--yaml=sat2density_cvact \
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--test_ckpt_path=2u87bj8w \
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--sty_img1=demo_img/case9/groundview.image.png \
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--sty_img2=demo_img/case7/groundview.image.png \
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--demo_img=demo_img/case3/satview-input.png \
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--save_dir=results/case2
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```
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## Train & Inference
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- *We trained our model using 1 V100 32GB GPU. The training phase will take about 20 hours.*
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- *For data preparation, please check out [data.md](dataset/INSTALL.md).*
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### Inference
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To test Center Ground-View Synthesis setting
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If you want save results, please add --task=vis_test
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```bash
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# CVACT
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python offline_train_test.py --yaml=sat2density_cvact --test_ckpt_path=2u87bj8w
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# CVUSA
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python offline_train_test.py --yaml=sat2density_cvusa --test_ckpt_path=2cqv8uh4
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```
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To test inference with different illumination
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```bash
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# CVACT
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bash inference/single_style_test_cvact.sh
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# CVUSA
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bash inference/single_style_test_cvusa.sh
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```
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To test synthesis ground videos
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```bash
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bash inference/synthesis_video.sh
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```
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## Training
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### Training command
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```bash
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# CVACT
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CUDA_VISIBLE_DEVICES=X python train.py --yaml=sat2density_cvact
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# CVUSA
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CUDA_VISIBLE_DEVICES=X python train.py --yaml=sat2density_cvusa
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```
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## Citation
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If you use this code for your research, please cite
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```
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@inproceedings{qian2021sat2density,
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title={Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs},
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author={Qian, Ming and Xiong, Jincheng and Xia, Gui-Song and Xue, Nan},
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booktitle={ICCV},
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year={2023}
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}
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```
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## License
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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For commercial use, please contact [mingqian@whu.edu.cn].
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---
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title: sat3density
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emoji: 🐠
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colorFrom: yellow
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colorTo: purple
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sdk: gradio
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sdk_version: 3.36.1
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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