  Artistic style transfer can be thought as a process to generate different
versions of abstraction of the original image. However, most of the artistic
style transfer operators are not optimized for human faces thus mainly suffers
from two undesirable features when applying them to selfies. First, the edges
of human faces may unpleasantly deviate from the ones in the original image.
Second, the skin color is far from faithful to the original one which is
usually problematic in producing quality selfies. In this paper, we take a
different approach and formulate this abstraction process as a gradient domain
learning problem. We aim to learn a type of abstraction which not only achieves
the specified artistic style but also circumvents the two aforementioned
drawbacks thus highly applicable to selfie photography. We also show that our
method can be directly generalized to videos with high inter-frame consistency.
Our method is also robust to non-selfie images, and the generalization to
various kinds of real-life scenes is discussed. We will make our code publicly
available.
