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In this short report, we present a simple, yet effective approach to editingreal images via generative adversarial networks (GAN). Unlike previoustechniques, that treat all editing tasks as an operation that affects pixelvalues in the entire image in our approach we cut up the image into a set ofsmaller segments. For those segments corresponding latent codes of a generativenetwork can be estimated with greater accuracy due to the lower number ofconstraints. When codes are altered by the user the content in the image ismanipulated locally while the rest of it remains unaffected. Thanks to thisproperty the final edited image better retains the original structures and thushelps to preserve natural look.

Real Image Inversion via Segments