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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Proceedings ArticleDOI
Scene Aware Person Image Generation through Global Contextual Conditioning
TL;DR: In this article , a Wasserstein Generative Adversarial Network (WGAN) is used to predict the potential location and the skeletal structure of the new person by conditioning a WGAN on the existing human skeletons present in the scene, and the predicted skeleton is refined through a shallow linear network to achieve higher structural accuracy in the generated image.
Proceedings ArticleDOI
Overview of Image Inpainting Techniques: A Survey
TL;DR: An overview of the traditional methods and deep learning methods which have been used for inpainting task can be found in this article , where traditional methods could accurately fill the missing regions when the hole size is small but failed to inpaint large sized holes and also they cannot hallucinate novel contents.
Journal ArticleDOI
Siamese CNN-based rank learning for quality assessment of inpainted images
TL;DR: Comparative experimental results demonstrate that the proposed deep rank learning-based method outperforms existing NR-IIQA metrics in evaluating both inpainted images and inpainting algorithms.
Journal ArticleDOI
Towards Source-Based Classification of Image Inpainting Techniques: A Survey
TL;DR: Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way as mentioned in this paper, which is a common task in image inpaintering. In the proposed framework, existing image-inpainting methods are clas...
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