Real Image Inversion via Segments
This work addresses the problem of local image editing for users of generative models, though it appears incremental as it builds on existing GAN techniques.
The authors tackled real image editing by segmenting images to improve latent code estimation in GANs, resulting in edited images that better retain original structures and preserve a natural look.
In this short report, we present a simple, yet effective approach to editing real images via generative adversarial networks (GAN). Unlike previous techniques, that treat all editing tasks as an operation that affects pixel values in the entire image in our approach we cut up the image into a set of smaller segments. For those segments corresponding latent codes of a generative network can be estimated with greater accuracy due to the lower number of constraints. When codes are altered by the user the content in the image is manipulated locally while the rest of it remains unaffected. Thanks to this property the final edited image better retains the original structures and thus helps to preserve natural look.