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DeepFillv2 (CVPR'2019)

Free-Form Image Inpainting with Gated Convolution

Task: Inpainting

Abstract

We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces.

Results and models

CelebA-HQ

Model Mask Type Resolution Train Iters Dataset l1 error PSNR SSIM Training Resources Download
DeepFillv2 free-form 256x256 20k CelebA-val 5.411 25.721 0.871 8 model | log

Places365-Challenge

Model Mask Type Resolution Train Iters Dataset l1 error PSNR SSIM Training Resources Download
DeepFillv2 free-form 256x256 100k Places365-val 8.635 22.398 0.815 8 model | log

Quick Start

Train

Train Instructions

You can use the following commands to train a model with cpu or single/multiple GPUs.

# cpu train
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py

# single-gpu train
python tools/train.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py

# multi-gpu train
./tools/dist_train.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py 8

For more details, you can refer to Train a model part in train_test.md.

Test

Test Instructions

You can use the following commands to test a model with cpu or single/multiple GPUs.

# cpu test
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth

# single-gpu test
python tools/test.py configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth

# multi-gpu test
./tools/dist_test.sh configs/deepfillv2/deepfillv2_8xb2_places-256x256.py https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth 8

For more details, you can refer to Test a pre-trained model part in train_test.md.

Citation

@inproceedings{yu2019free,
  title={Free-form image inpainting with gated convolution},
  author={Yu, Jiahui and Lin, Zhe and Yang, Jimei and Shen, Xiaohui and Lu, Xin and Huang, Thomas S},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={4471--4480},
  year={2019}
}