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
DRGAN: A dual resolution guided low-resolution image inpainting
Li Huang,Yaping Huang +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a dual-resolution generative adversarial network (DRGAN) for low-resolution image inpainting, which first trains a generative network to obtain high-quality images from noise vectors, and then a dual resolution loss is designed to optimize the input vectors initialized by random noise to generate high quality images.
Proceedings ArticleDOI
SECI-GAN: Semantic and Edge Completion for dynamic objects removal
TL;DR: The SECI-GAN architecture is proposed, an architecture that jointly exploits the high-level cues extracted by semantic segmentation and the fine-grained details captured by edge extraction to condition the image inpainting process and is evaluated on the Cityscapes dataset.
Book ChapterDOI
Incomplete Texture Repair of Iris Based on Generative Adversarial Networks.
TL;DR: Wang et al. as mentioned in this paper proposed a PGGAN-based iris image restoration framework for autonomously restoring obscured iris information regions in iris images, where the fade-in operation was introduced in the training phase of resolution doubling, so that the resolution increase can smoothly transition.
Proceedings ArticleDOI
Face Inpainting with Dilated Skip Architecture and Multi-Scale Adversarial Networks
TL;DR: Dilated skip architecture is introduced, which combines the advantages of dilated convolution and U-net, which can enlarge the perception area for better repairing, as well as preserve the completeness of the original image features during the repairing process.
Journal ArticleDOI
Throwaway Shadows Using Parallel Encoders Generative Adversarial Network
TL;DR: This work proposes a novel generative adversarial network (GAN) based image-to-image translation approach for shadow removal in face images and finds that this combination in the generator results in learning an incorporated semantic structure and in disentangling visual discrepancies problems under the shadow area.
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