  We present a method for fine-grained face manipulation. Given a face image
with an arbitrary expression, our method can synthesize another arbitrary
expression by the same person. This is achieved by first fitting a 3D face
model and then disentangling the face into a texture and a shape. We then learn
different networks in these two spaces. In the texture space, we use a
conditional generative network to change the appearance, and carefully design
input formats and loss functions to achieve the best results. In the shape
space, we use a fully connected network to predict the accurate shapes and use
the available depth data for supervision. Both networks are conditioned on
expression coefficients rather than discrete labels, allowing us to generate an
unlimited amount of expressions. We show the superiority of this disentangling
approach through both quantitative and qualitative studies. In a user study,
our method is preferred in 85% of cases when compared to the most recent work.
When compared to the ground truth, annotators cannot reliably distinguish
between our synthesized images and real images, preferring our method in 53% of
the cases.
