  Deep generative models have the capacity to render high fidelity images of
content like human faces. Recently, there has been substantial progress in
conditionally generating images with specific quantitative attributes, like the
emotion conveyed by one's face. These methods typically require a user to
explicitly quantify the desired intensity of a visual attribute. A limitation
of this method is that many attributes, like how "angry" a human face looks,
are difficult for a user to precisely quantify. However, a user would be able
to reliably say which of two faces seems "angrier". Following this premise, we
develop the $\textit{PrefGen}$ system, which allows users to control the
relative attributes of generated images by presenting them with simple paired
comparison queries of the form "do you prefer image $a$ or image $b$?" Using
information from a sequence of query responses, we can estimate user
preferences over a set of image attributes and perform preference-guided image
editing and generation. Furthermore, to make preference localization feasible
and efficient, we apply an active query selection strategy. We demonstrate the
success of this approach using a StyleGAN2 generator on the task of human face
editing. Additionally, we demonstrate how our approach can be combined with
CLIP, allowing a user to edit the relative intensity of attributes specified by
text prompts. Code at https://github.com/helblazer811/PrefGen.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning