  High-level manipulation of facial expressions in images --- such as changing
a smile to a neutral expression --- is challenging because facial expression
changes are highly non-linear, and vary depending on the appearance of the
face. We present a fully automatic approach to editing faces that combines the
advantages of flow-based face manipulation with the more recent generative
capabilities of Variational Autoencoders (VAEs). During training, our model
learns to encode the flow from one expression to another over a low-dimensional
latent space. At test time, expression editing can be done simply using latent
vector arithmetic. We evaluate our methods on two applications: 1) single-image
facial expression editing, and 2) facial expression interpolation between two
images. We demonstrate that our method generates images of higher perceptual
quality than previous VAE and flow-based methods.
