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
Semantic Image Manipulation with Background-guided Internal Learning
TL;DR: Semantic Image Manipulation with Background-guided Internal Learning (SIMBIL) is proposed, which combines high-level and low-level manipulation and is scalable to images of various sizes without reliance on external visual datasets for training.
Journal ArticleDOI
Analysis of image inpainting and object removal methodologies
Shiney Thomas,Juby Mathew +1 more
TL;DR: Three benchmark methods of which two are conventional and other GAN based are evaluated to check the effectives of the inpainting process, for different class of images found the conventional method capable of providing visually good restored images with an average SSIM score.
Book ChapterDOI
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
TL;DR: DuelGAN as discussed by the authors introduces a duel between discriminators to discourage their agreement and increase the level of diversity of generated samples, which alleviates the issue of early mode collapse by preventing the discriminators from converging too fast.
Posted Content
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block
TL;DR: A new deep generative model-based approach, which trains a shared network twice with different targets and utilizes a single network during the testing phase, so that it can effectively save inference time.
Journal ArticleDOI
Non-Local and Multi-Scale Mechanisms for Image Inpainting.
TL;DR: Zhang et al. as mentioned in this paper combined two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas.
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