Transcription of Globally and Locally Consistent Image Completion
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Globally and Locally Consistent Image Completion
Globally and Locally Consistent Image CompletionSATOSHI IIZUKA,Waseda UniversityEDGAR SIMO-SERRA,Waseda UniversityHIROSHI ISHIKAWA,Waseda UniversityFig. 1. Image Completion results by our approach. The masked area is shown in white. Our approach can generate novel fragments that are not presentelsewhere in the Image , such as needed for completing faces; this is not possible with patch-based methods. Photographs courtesy of Michael D Beckwith(CC0), Mon Mer (Public Domain), davidgsteadman (Public Domain), and Owen Lucas (Public Domain).We present a novel approach for Image Completion that results in imagesthat are both Locally and Globally Consistent . With a fully-convolutionalneural network, we can complete images of arbitrary resolutions by lling-in missing regions of any shape. To train this Image Completion network tobe Consistent , we use global and local context discriminators that are trainedto distinguish real images from completed ones.
locally and globally consistent natural image completion. Our ar-chitecture is composed of three networks: a completion network, a global context discriminator, and a local context discriminator. The completion network is fully convolutional and used to complete the image, while both the global and the local context discrimina-
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