object_removal/TFill/README.md

TFill

paper | arXiv | Project | Video

This repository implements the training, testing and editing tools for "Bridging Global Context Interactions for High-Fidelity Image Completion (CVPR2022, scores: 1, 1, 2, 2)" by Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai and Dinh Phung. Given masked images, the proposed TFill model is able to generate high-fidelity plausible results on various settings.

Examples

Object Removal

Object Repair

Framework

We propose the two-stages image completion framework, where the upper content inference network (TFill-Coarse) generates semantically correct content using a transformer encoder to directly capture the global context information; the lower appearance refinement network (TFill-refined) copies global visible and generated features to holes.

Getting started

• Clone this repo:

git clone https://github.com/lyndonzheng/TFill
cd TFill

Requirements

The original model is trained and evaluated with Pytorch v1.9.1, which cannot be visited in current PyTorch. Therefore, we create a new environment with Pytorch v1.10.0 to test the model, where the performance is the same.

A suitable conda environment named Tfill can be created and activated with:

conda env create -f environment.yaml
conda activate TFill

Runing pretrained models

Download the pre-trained models using the following links (CelebA-HQ, FFHQ, ImageNet, Plcases2 ) and put them undercheckpoints/ directory. It should have the following structure:

./checkpoints/
├── celeba
│   ├── latest_net_D.pth
│   ├── latest_net_D_Ref.pth
│   ├── latest_net_E.pth
│   ├── latest_net_G.pth
│   ├── latest_net_G_Ref.pth
│   ├── latest_net_T.pth
├── ffhq
│   ├── ...
├── ...

• Test the model

sh ./scripts/test.sh

For different models, the users just need to modify lines 2-4, including name,img_file,mask_file. For instance, we can replace the celeba to imagenet.

The default results will be stored under the results/ folder, in which:

• examples/: shows original and masked images;
• img_out/: shows upsampled Coarse outputs;
• img_ref_out/: shows the final Refined outputs.

Datasets

• face dataset:
  • 24,183 training images and 2,824 test images from CelebA and use the algorithm of Growing GANs to get the high-resolution CelebA-HQ dataset.
  • 60,000 training images and 10,000 test images from FFHQ provided by StyleGAN.
• natural scenery: original training and val images from Places2.
• object original training images from ImageNet.

Traning

• Train a model (two stage: Coarse and Refinement)

sh ./scripts/train.sh

The default setting is for the top Coarse training. The users just need to replace the coarse with refine at line 6. Then, the model can continue training for high-resolution image completion. More hyper-parameter can be in options/.

The coarse results using transformer and restrictive CNN is impressive, which provides plausible results for both foreground objects and background scene.

GUI

The GUI operation is similar to our previous GUI in PIC, where steps are also the same.

Basic usage is:

sh ./scripts/ui.sh 

In gui/ui_model.py, users can modify the img_root(line 30) and the corresponding img_files(line 31) to randomly edit images from the testing dataset.

Editing Examples

• Results (original, output) for face editing

• Results (original, masked input, output) for nature scene editing

Next

• Higher-resolution pluralistic image completion

License

This work is licensed under a MIT License.

This software is for educational and academic research purpose only. If you wish to obtain a commercial royalty bearing license to this software, please contact us at [email protected].

Citation

The code also uses our previous PIC. If you use this code for your research, please cite our papers.

@InProceedings{Zheng_2022_CVPR,
    author    = {Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei and Phung, Dinh},
    title     = {Bridging Global Context Interactions for High-Fidelity Image Completion},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {11512-11522}
}

@inproceedings{zheng2019pluralistic,
  title={Pluralistic Image Completion},
  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1438--1447},
  year={2019}
}

@article{zheng2021pluralistic,
  title={Pluralistic Free-From Image Completion},
  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
  journal={International Journal of Computer Vision},
  pages={1--20},
  year={2021},
  publisher={Springer}
}