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Controllable semantic image editing enables a user to change entire imageattributes with a few clicks, e.g., gradually making a summer scene look likeit was taken in winter. Classic approaches for this task use a GenerativeAdversarial Net (GAN) to learn a latent space and suitable latent-spacetransformations. However, current approaches often suffer from attribute editsthat are entangled, global image identity changes, and diminishedphoto-realism. To address these concerns, we learn multiple attributetransformations simultaneously, integrate attribute regression into thetraining of transformation functions, and apply a content loss and anadversarial loss that encourages the maintenance of image identity andphoto-realism. We propose quantitative evaluation strategies for measuringcontrollable editing performance, unlike prior work, which primarily focuses onqualitative evaluation. Our model permits better control for both single- andmultiple-attribute editing while preserving image identity and realism duringtransformation. We provide empirical results for both natural and syntheticimages, highlighting that our model achieves state-of-the-art performance fortargeted image manipulation.

Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation