Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration
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We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with user-specifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user's choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like "change the nose of a beagle to that of a bulldog, and open her mouth." The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at https://github.com/pfnet-research/neural-collage.
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