An easy way to implement this is to first do zero padding for both features and masks and then apply the partial convolution operation and mask updating. This will help to reduce the border artifacts. Note that we didn’t directly use existing padding scheme like zero/reflection/repetition padding instead, we use partial convolution as padding by assuming the region outside the images (border) are holes.It will have a big impact on the scale of the perceptual loss and style loss. However, other framework (tensorflow, chainer) may not do that. The VGG model pretrained on pyTorch divides the image values by 255 before feeding into the network like this pyTorch’s pretrained VGG model was also trained in this way. Be careful of the scale difference issues.What are the scale of VGG feature and its losses?.Later, we use random dilation, rotation and cropping to augment the mask dataset (if the generated holes are too small, you may try videos with larger motions). The first step is to get the forward and backward flow using some code like deepflow or flownet2 the second step is to use theconsistency checking code to generate mask. The mask dataset is generated using the forward-backward optical flow consistency checking described in this paper.However, for some network initialization schemes, the latter one may be easier to train. * X) * sum(I) / sum(M) + b, where I is a tensor filled with all 1 and having same channel, height and width with M. * X) / sum(M) is too small, an alternative to W^T* (M. Note: M has same channel, height and width with feature/image.for computing sum(M), we use another convolution operator D, whose kernel size and stride is the same with the one above, but all its weights are 1 and bias are 0. we will have convolution operator C to do the basic convolution we want it has W, b as the shown in the equations.I implemented by extending the existing Convolution layer provided by pyTorch. How Equation (1) and (2) are implemented?. If you find the dataset useful, please consider citing this page directly shown below instead of the data-downloading link = , NVIDIA Irregular Mask Dataset: Testing Set Note and Reference In total, we have created 6 × 2 × 1000 = 12, 000 masks. Each category contains 1000 masks with and without border constraints. NVIDIA Irregular Mask Dataset: Training Set Testing Set For our training, we use threshold 0.6 to binarize the masks first and then use from 9 to 49 pixels dilation to randomly dilate the holes, followed by random translation, rotation and cropping. To train the network, please use random augmentation tricks including random translation, rotation, dilation and cropping to augment the dataset. Data (NVIDIA Irregular Mask Dataset) Training Set Video Media Coverage (Selected)įortune, Forbes, Fast Company, Engadget, SlashGear, Digital Trends, TNW, eTeknix, Game Debate, Alphr, Gizbot, Fossbytes Techradar, Beeborn, Bit-tech, Hexus, HotHardWare, BleepingComputer, hardocp, boingboing, PetaPixel, 搜狐, 新浪, 量子位(知乎) Online Demo We show qualitative and quantitative comparisons with other methods to validate our approach. Our model outperforms other methods for irregular masks. We further include a mechanism to automatically generate an updated mask for the next layer as part of the forward pass. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. This often leads to artifacts such as color discrepancy and blurriness. Įxisting deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro, Image Inpainting for Irregular Holes Using Partial Convolutions, Proceedings of the European Conference on Computer Vision (ECCV) 2018. Recommended citation: Guilin Liu, Fitsum A. Image Inpainting for Irregular Holes Using Partial Convolutions
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