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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Book ChapterDOI
Semantic Image Completion and Enhancement Using GANs
TL;DR: In this article, the authors discuss the underlying GAN architecture and how they can be used for image completion tasks and propose an efficient image completion and enhancement model to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image.
Proceedings ArticleDOI
On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs
Catalin Stoean,Nebojsa Bacanin,Ruxandra Stoean,Leonard Ionescu,Cristian Daniel Alecsa,M. Hotoleanu,Miguel A. Atencia Ruiz,Gonzalo Joya +7 more
TL;DR: In this paper , a U-Net approach with a perceptual loss for the semantic inpainting of traditional Romanian vests was proposed to decide the most visually appropriate in-painting for very degraded historical items.
Posted Content
Generative Image Inpainting with Submanifold Alignment
TL;DR: Li et al. as discussed by the authors exploited Local Intrinsic Dimensionality (LID) to measure, in deep feature space, the alignment between data submanifolds learned by a GAN model and those of the original data, from a perspective of both images and local patches of images.
Journal ArticleDOI
Simultaneous Multi‐Attribute Image‐to‐Image Translation Using Parallel Latent Transform Networks
Sen-Zhe Xu,Yu-Kun Lai +1 more
TL;DR: This work proposes a novel approach to multi‐attribute image‐to‐image translation using several parallel latent transform networks, where multiple attributes are manipulated in parallel and simultaneously, which eliminates both issues.
Proceedings ArticleDOI
Facial Image Inpainting with Variational Autoencoder
Ching-Ting Tu,Yi-Fu Chen +1 more
TL;DR: A novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance by using a trained standard Variational Autoencoder (VAE) for un-occluded face generation.
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