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Towards Interactive Image Inpainting via Sketch Refinement

Chang Liu, Shunxin Xu, Jialun Peng, Kaidong Zhang, Dong Liu

[Paper] / [Project] / [Baidu Disk] (Models) (Test Protocol) / [Huggingface] (Models) (Test Protocol) / [Google Drive] / [Interactive Demo]

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(The password to download the real-world test protocol: rwtp)

1. News
2. Citation
3. Overview
4. To-Do Lists
5. Prerequisites
6. Usage
7. Qualitative Comparisons
8. License
9. Acknowledgements
10. Frequently Asked Questions
11. Forked and Star History

🔥News

[Aug. 7th] We are currently modifying the current codebase (ICCV version) to the corresponding version of our accepted paper (TMM version). Code will be progressively updated in near future.
[May 17th] Our paper titled "Towards Interactive Image Inpainting via Robust Sketch Refinement" is accepted by TMM 2024!

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Citation

If you find our work is enlightening or the proposed dataset is useful to you, please cite our TMM 2024 paper.

@ARTICLE{liu-etal-2024-sketchrefiner,
  author={Liu, Chang and Xu, Shunxin and Peng, Jialun and Zhang, Kaidong and Liu, Dong},
  journal={IEEE Transactions on Multimedia}, 
  title={Towards Interactive Image Inpainting via Robust Sketch Refinement}, 
  year={2024},
  pages={1-15},
}

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Overview

tissor One tough problem of image inpainting is to restore complex structures in the corrupted regions. It motivates interactive image inpainting which leverages additional hints, e.g., sketches, to assist the inpainting process. Sketch is simple and intuitive to end users, but meanwhile has free forms with much randomness. Such randomness may confuse the inpainting models, and incur severe artifacts in completed images. To address this problem, we propose a two-stage image inpainting method termed SketchRefiner. In the first stage, we propose using a cross-correlation loss function to robustly calibrate and refine the user-provided sketches in a coarse-to-fine fashion. In the second stage, we learn to extract features from the abstracted sketches in a latent space and modulate the inpainting process. We also propose an algorithm to simulate real sketches automatically and build a test protocol to evaluate different methods under real applications. Experimental results on public datasets demonstrate that SketchRefiner effectively utilizes sketch information and eliminates the artifacts due to the free-form sketches. Our method consistently outperforms state-of-the-art baselines both qualitatively and quantitatively, meanwhile revealing great potential in real-world applications.

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To-Do Lists

Online demo of SketchRefiner.
Official instructions for installation and usage of SketchRefiner.
Training code of SketchRefiner.
Testing code of SketchRefiner.
The proposed sketch-based test protocol.
Pre-trained model weights.
Regular maintain

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Prerequisites

For installing the environment, you could execute the following scripts:

conda create -n sketchrefiner python=3.6
conda activate sketchrefiner
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install opencv-python

For utilizing the HRF loss to train the model, please download the pre-trained models provided by LaMa. You could download the model by executing the following command lines:

mkdir -p ade20k/ade20k-resnet50dilated-ppm_deepsup/
wget -P ade20k/ade20k-resnet50dilated-ppm_deepsup/ http://sceneparsing.csail.mit.edu/model/pytorch/ade20k-resnet50dilated-ppm_deepsup/encoder_epoch_20.pth

Note that the ade20k folder is placed at SIN_src/models/ade20k.

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Usage

Data Pre-processing

Before you train all the networks, you need to first prepare all the data following the procedures as follows:

Download ImageNet, CelebA-HQ, and Places2 datasets. Suppose that these data is downloaded at datasets/dataset_name.
Use the edge detector BDCN to extract edge map from the ground truth images in datasets. Suppose that the resulting edge maps are placed at datasets/dataset_name/edges.
Run scripts/make_deform_sketch.py to generate the deformed sketches for training. We offer an example command line as follows:

# generate deformed sketches from detected edge maps
python scripts/make_deform_sketch.py
      --edge datasets/dataset_name/edges
      --output datasets/dataset_name/sketches

You may adjust --grid_size, --filter_size, and --max_move to control the magnitude of warping the edge maps. And you can select the number of generated sketches by adjusting --num_samples.

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Training

1. Train the Sketch Refinement Network (SRN)

Before training the inpainting network of SketchRefiner, you need to first train the proposed Sketch Refinement Network (SRN). We demonstrate an example command line as follows:

# train the Registration Module (RM)
python SRN_train.py
      --images /path/to/images
      --edges_prefix /path/to/edge
      --output /path/to/output/dir
      
# train the Enhancement Module (EM)
python SRN_train.py
      --images /path/to/images/
      --edges_prefix /path/to/edge/
      --output /path/to/output/dir
      --train_EM
      --RM_checkpoint /path/to/model/weights/of/RM

If you need to evaluate the model during training, make sure you assign val_interval > 0 and configure the paths of images_val, masks_val, sketches_prefix_val, edges_prefix_val.

2. Train the Sketch-modulated Inpainting Network (SIN)

After the SRN is trained, you could start training the SIN by executing:

python SIN_train.py
      --config_path /path/to/config
      --GPU_ids gpu_id

Make sure you modify corresponding lines of the configuration file with the paths of training data. We put an example configuration file in SIN_src/configs/example.yml.

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Inference

3. Refine the Input Sketches

During inference, you need to first refine the input sketches and save them locally by running:

python SRN_test.py
      --images /path/to/test/source/images
      --masks /path/to/test/masks
      --edge_prefix /path/to/detected/edges
      --sketch_prefix /path/to/input/sketches
      --output /path/to/output/dir
      --RM_checkpoint /path/to/RM/checkpoint
      --EM_checkpoint /path/to/EM/checkpoint

The refined sketches would be saved at the assigned output path.

4. Inpaint the Corrupted Images with Refined Sketches

Now you could inpaint the corrupted images with the refined sketches from SRN, by running:

python SIN_test.py
      --config_path /path/to/config/path
      --GPU_ids gpu_id
      --images /path/to/source/images
      --masks /path/to/masks
      --edges /path/to/detected/edges
      --sketches /path/to/input/sketches
      --refined_sketches /path/to/refined/sketches
      --output /path/to/output/dir/
      --checkpoint /path/to/model/weights/of/SIM
      --num_samples maximum_samples

Edges are detected using bdcn, you could refer to their code in here.

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Pre-trained Model Weights

We release the pre-trained model weights of SketchRefiner on CelebA-HQ, Places2, and ImageNet, which can be referred to in here (password: skre).

Real-World Test Protocol

We release the curated real-world test protocol, which currently consists of 26 face images and 60 natural images with manually annotated masks and sketches. Note that the current released version might be further revised until our paper is finally accepted. You may download the test protocol in here (password: rwtp).

We provide some examples of the test protocol in the following figure. <details>

<summary>Examples of Real-World Test Protocol</summary>

realworld

</details>

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Qualitative Comparisons

We demonstrate the qualitative comparisons upon face and natural images in the following figures.

<details><summary>Face Images</summary>

celebahq

</details> <details><summary>Natural Images</summary>

places

</details> <details><summary>More Results</summary>

more_results

</details>

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License

This work is licensed under MIT license. See the LICENSE for details.

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Acknowledgements

This codebase is heavily modified from ZITS. Thanks for their great implementaion!

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Frequently Asked Questions

1. Usage of `BDCN`

We do not recommend using the official BDCN codebase for its Python 2 implementation, which might cause a series of errors with the latest libraries. Instead, we offer a Python 3 version of implementation in this repo (in data-processing/bdcn-edge-detection). The model weights of BDCN are available in this link. Please refer to the repo documentation for more details.

Related issues: #8

2. Usage of Configurations Arguments

If you have any questions about the usage of configurations arguments wrapped in argparse, please do not hesitate to discuss with us in the following issues, or start a new issue.

Related issues: #4, #5, #9, #10

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Forked and Star History

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Awesome

Towards Interactive Image Inpainting via Sketch Refinement

Table of Contents

🔥News

Citation

Overview

To-Do Lists

Prerequisites

Usage

Data Pre-processing

Training

1. Train the Sketch Refinement Network (SRN)

2. Train the Sketch-modulated Inpainting Network (SIN)

Inference

3. Refine the Input Sketches

4. Inpaint the Corrupted Images with Refined Sketches

Pre-trained Model Weights

Real-World Test Protocol

Qualitative Comparisons

License

Acknowledgements

Frequently Asked Questions

1. Usage of BDCN

2. Usage of Configurations Arguments

Forked and Star History

1. Usage of `BDCN`