  We investigate the problem of zero-shot semantic image painting. Instead of
painting modifications into an image using only concrete colors or a finite set
of semantic concepts, we ask how to create semantic paint based on open
full-text descriptions: our goal is to be able to point to a location in a
synthesized image and apply an arbitrary new concept such as "rustic" or
"opulent" or "happy dog." To do this, our method combines a state-of-the art
generative model of realistic images with a state-of-the-art text-image
semantic similarity network. We find that, to make large changes, it is
important to use non-gradient methods to explore latent space, and it is
important to relax the computations of the GAN to target changes to a specific
region. We conduct user studies to compare our methods to several baselines.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics