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We present a novel CNN-based image editing strategy that allows the user tochange the semantic information of an image over an arbitrary region bymanipulating the feature-space representation of the image in a trained GANmodel. We will present two variants of our strategy: (1) spatial conditionalbatch normalization (sCBN), a type of conditional batch normalization withuser-specifiable spatial weight maps, and (2) feature-blending, a method ofdirectly modifying the intermediate features. Our methods can be used to editboth artificial image and real image, and they both can be used together withany GAN with conditional normalization layers. We will demonstrate the power ofour method through experiments on various types of GANs trained on differentdatasets. Code will be available athttps://github.com/pfnet-research/neural-collage.

Spatially Controllable Image Synthesis with Internal Representation Collaging