  Controllable semantic image editing enables a user to change entire image
attributes with a few clicks, e.g., gradually making a summer scene look like
it was taken in winter. Classic approaches for this task use a Generative
Adversarial Net (GAN) to learn a latent space and suitable latent-space
transformations. However, current approaches often suffer from attribute edits
that are entangled, global image identity changes, and diminished
photo-realism. To address these concerns, we learn multiple attribute
transformations simultaneously, integrate attribute regression into the
training of transformation functions, and apply a content loss and an
adversarial loss that encourages the maintenance of image identity and
photo-realism. We propose quantitative evaluation strategies for measuring
controllable editing performance, unlike prior work, which primarily focuses on
qualitative evaluation. Our model permits better control for both single- and
multiple-attribute editing while preserving image identity and realism during
transformation. We provide empirical results for both natural and synthetic
images, highlighting that our model achieves state-of-the-art performance for
targeted image manipulation.
