  Recent studies on facial expression editing have obtained very promising
progress. On the other hand, existing methods face the constraint of requiring
a large amount of expression labels which are often expensive and
time-consuming to collect. This paper presents an innovative label-free
expression editing via disentanglement (LEED) framework that is capable of
editing the expression of both frontal and profile facial images without
requiring any expression label. The idea is to disentangle the identity and
expression of a facial image in the expression manifold, where the neutral face
captures the identity attribute and the displacement between the neutral image
and the expressive image captures the expression attribute. Two novel losses
are designed for optimal expression disentanglement and consistent synthesis,
including a mutual expression information loss that aims to extract pure
expression-related features and a siamese loss that aims to enhance the
expression similarity between the synthesized image and the reference image.
Extensive experiments over two public facial expression datasets show that LEED
achieves superior facial expression editing qualitatively and quantitatively.
