Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
- pp 6882-6890
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TLDR
A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.Abstract:
Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.read more
Citations
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Journal ArticleDOI
The generative adversarial networks and its application in machine vision
TL;DR: The basic principles and existing problems of GAN are described, then several improved GAN models, including Info, DC-GAN, f-gan, Cat-GAN and others are introduced.
Book ChapterDOI
Facial Expression Restoration Based on Improved Graph Convolutional Networks
TL;DR: Wang et al. as mentioned in this paper proposed a facial expression restoration method based on generative adversarial network by integrating an improved graph convolutional network (IGCN) and region relation modeling block (RRMB).
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Deep Blind Image Inpainting
Yang Liu,Jinshan Pan,Zhixun Su +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input, which is motivated by the residual learning algorithm which aims to learn the missing infor- mation in corrupted regions.
Proceedings ArticleDOI
Solving Jigsaw Puzzles With Eroded Boundaries
TL;DR: The key idea of the proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as ”neighbors or not”.
Proceedings ArticleDOI
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
TL;DR: JPGNet as discussed by the authors proposes a joint predictive filtering and generative network that contains three branches: predictive filtering & uncertainty network (PFUNet), deep generative networks, and uncertainty-aware fusion network (UAFNet), which adaptively predict pixel-wise kernels for filtering-based inpainting according to the input image and output an uncertainty map.
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