  The ability to edit facial expressions has a wide range of applications in
computer graphics. The ideal facial expression editing algorithm needs to
satisfy two important criteria. First, it should allow precise and targeted
editing of individual facial actions. Second, it should generate high fidelity
outputs without artifacts. We build a solution based on StyleGAN, which has
been used extensively for semantic manipulation of faces. As we do so, we add
to our understanding of how various semantic attributes are encoded in
StyleGAN. In particular, we show that a naive strategy to perform editing in
the latent space results in undesired coupling between certain action units,
even if they are conceptually distinct. For example, although brow lowerer and
lip tightener are distinct action units, they appear correlated in the training
data. Hence, StyleGAN has difficulty in disentangling them. We allow
disentangled editing of such action units by computing detached regions of
influence for each action unit, and restrict editing to these regions. We
validate the effectiveness of our local editing method through perception
experiments conducted with 23 subjects. The results show that our method
provides higher control over local editing and produces images with superior
fidelity compared to the state-of-the-art methods.
