  Existing methods for face image manipulation generally focus on editing the
expression, changing some predefined attributes, or applying different filters.
However, users lack the flexibility of controlling the shapes of different
semantic facial parts in the generated face. In this paper, we propose an
approach to compute a disentangled shape representation for a face image,
namely the FaceShapeGene. The proposed FaceShapeGene encodes the shape
information of each semantic facial part separately into a 1D latent vector. On
the basis of the FaceShapeGene, a novel part-wise face image editing system is
developed, which contains a shape-remix network and a conditional label-to-face
transformer. The shape-remix network can freely recombine the part-wise latent
vectors from different individuals, producing a remixed face shape in the form
of a label map, which contains the facial characteristics of multiple subjects.
The conditional label-to-face transformer, which is trained in an unsupervised
cyclic manner, performs part-wise face editing while preserving the original
identity of the subject. Experimental results on several tasks demonstrate that
the proposed FaceShapeGene representation correctly disentangles the shape
features of different semantic parts. %In addition, we test our system on
several novel part-wise face editing tasks. Comparisons to existing methods
demonstrate the superiority of the proposed method on accomplishing novel face
editing tasks.
