  We present User-predictable Face Editing (UP-FacE) -- a novel method for
predictable face shape editing. In stark contrast to existing methods for face
editing using trial and error, edits with UP-FacE are predictable by the human
user. That is, users can control the desired degree of change precisely and
deterministically and know upfront the amount of change required to achieve a
certain editing result. Our method leverages facial landmarks to precisely
measure facial feature values, facilitating the training of UP-FacE without
manually annotated attribute labels. At the core of UP-FacE is a
transformer-based network that takes as input a latent vector from a
pre-trained generative model and a facial feature embedding, and predicts a
suitable manipulation vector. To enable user-predictable editing, a scaling
layer adjusts the manipulation vector to achieve the precise desired degree of
change. To ensure that the desired feature is manipulated towards the target
value without altering uncorrelated features, we further introduce a novel
semantic face feature loss. Qualitative and quantitative results demonstrate
that UP-FacE enables precise and fine-grained control over 23 face shape
features.
