https://github.com/Kaminyou/Dense-Normalization
[ECCV 2024] Official implementation of "Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization"
https://github.com/Kaminyou/Dense-Normalization
computer-vision eccv2024 generative-adversarial-network generative-model high-resolution-image image-to-image-translation parallelism
Last synced: 2 months ago
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[ECCV 2024] Official implementation of "Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization"
- Host: GitHub
- URL: https://github.com/Kaminyou/Dense-Normalization
- Owner: Kaminyou
- License: agpl-3.0
- Created: 2024-07-01T17:37:12.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-25T08:18:17.000Z (7 months ago)
- Last Synced: 2024-11-25T09:25:11.913Z (7 months ago)
- Topics: computer-vision, eccv2024, generative-adversarial-network, generative-model, high-resolution-image, image-to-image-translation, parallelism
- Language: Python
- Homepage: https://kaminyou.com/Dense-Normalization/
- Size: 24.5 MB
- Stars: 28
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
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ECCV 2024
Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization
[Ming-Yang Ho](https://kaminyou.com/)1, [Che-Ming Wu](https://github.com/st9007a)2, [Min-Sheng Wu](https://github.com/Min-Sheng)3, [Yufeng Jane Tseng](https://www.csie.ntu.edu.tw/en/member/Faculty/Yufeng-Jane-Tseng-95281407)1
1National Taiwan University, 2Amazon Web Services, 3aetherAI
[[`Paper (arxiv)`](https://arxiv.org/abs/2407.04245)] [[`Paper (official)`](https://link.springer.com/chapter/10.1007/978-3-031-72995-9_18)] [[`Project Page`](https://kaminyou.com/Dense-Normalization/)]
## Get started with an example
We provide a simple example (one image from the Kyoto summer2autumn dataset) for you to translate an UHR image with our DN.
### Download example data
```bash
$ ./download.sh
$ unzip simple_example.zip
```
### Environment preparation
1. Please check your GPU driver version and modify `Dockerifle` accordingly
2. Then, execute
```bash
$ docker-compose up --build -d
```
3. Get into the docker container
```bash
$ docker exec -it dn-env bash
```
### Inference
1. In the docker container, please execute
```bash
$ python3 transfer.py -c data/japan/config.yaml
```
2. Then, you can see a translated image at `experiments/japan_CUT/test/IMG_6610/combined_dn_10.png`
3. To see the image conveniently, you can leverage the provided `visualization.ipynb`. The setup of jupyter notebbok can be achived by
- a. modify a port mapping setting in `docker-compose.yml`; e,g, `- 19000:8888`
- b. install `jupyter` in the container
- c. run your jupyter notebook by `nohup jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root &`
- d. open the jupter notebook service on your port (`19000` here)
## Datasets
### `real2paint` Dataset
For the real domain, please download the [UHDM dataset](https://xinyu-andy.github.io/uhdm-page/) from its official website. For the painting domain, we have curated a dataset of high-resolution Vincent van Gogh paintings, which can be downloaded at [link1](https://github.com/Kaminyou/UHR-Vincent-van-Gogh-real2paint) or [link2](https://www.dropbox.com/scl/fi/gohkhvipij61w496eeqdw/vincent_van_gogh.zip?rlkey=vco57kdadendwhy4zzednkk4i&st=d127g9bk&dl=0). Please note that we do not own these images; users should ensure their use does not trigger legal issues.
### `Kyoto-summer2autumn` Dataset
Please download it at [link](https://github.com/Kaminyou/Kyoto-summer2autumn).
### `ANHIR` Dataset
Please download it at [link](https://anhir.grand-challenge.org/Data/). Please note that we do not own these images; users should ensure their use does not trigger legal issues.
### `ACROBAT` Dataset
Please download it at [link](https://acrobat.grand-challenge.org/). Please note that we do not own these images; users should ensure their use does not trigger legal issues.
## Train your model
The training of I2I model is the same as [KIN](https://github.com/Kaminyou/URUST). DN is a plugin for any I2I model with InstanceNorm layers.
## Citation
```
@InProceedings{10.1007/978-3-031-72995-9_18,
author="Ho, Ming-Yang and Wu, Che-Ming and Wu, Min-Sheng and Tseng, Yufeng Jane",
title="Every Pixel Has Its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization",
booktitle="Computer Vision -- ECCV 2024",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="312--328",
isbn="978-3-031-72995-9"
}
```