  Across photography, marketing, and website design, being able to direct the
viewer's attention is a powerful tool. Motivated by professional workflows, we
introduce an automatic method to make an image region more attention-capturing
via subtle image edits that maintain realism and fidelity to the original. From
an input image and a user-provided mask, our GazeShiftNet model predicts a
distinct set of global parametric transformations to be applied to the
foreground and background image regions separately. We present the results of
quantitative and qualitative experiments that demonstrate improvements over
prior state-of-the-art. In contrast to existing attention shifting algorithms,
our global parametric approach better preserves image semantics and avoids
typical generative artifacts. Our edits enable inference at interactive rates
on any image size, and easily generalize to videos. Extensions of our model
allow for multi-style edits and the ability to both increase and attenuate
attention in an image region. Furthermore, users can customize the edited
images by dialing the edits up or down via interpolations in parameter space.
This paper presents a practical tool that can simplify future image editing
pipelines.
