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. The global discriminatorlooks at the entire Image to assess if it is coherent as a whole, while the localdiscriminator looks only at a small area centered at the completed region toensure the local consistency of the generated patches.
Globally and Locally Consistent Image Completion SATOSHI IIZUKA, Waseda University EDGAR SIMO-SERRA, Waseda University HIROSHI ISHIKAWA, Waseda University Fig. 1. Image completion results by our approach.
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