  Semantic face editing has achieved substantial progress in recent years.
Known as a growingly popular method, latent space manipulation performs face
editing by changing the latent code of an input face to liberate users from
painting skills. However, previous latent space manipulation methods usually
encode an entire face into a single low-dimensional embedding, which constrains
the reconstruction capacity and the control flexibility of facial components,
such as eyes and nose. This paper proposes IA-FaceS as a bidirectional method
for disentangled face attribute manipulation as well as flexible, controllable
component editing without the need for segmentation masks or sketches in the
original image. To strike a balance between the reconstruction capacity and the
control flexibility, the encoder is designed as a multi-head structure to yield
embeddings for reconstruction and control, respectively: a high-dimensional
tensor with spatial properties for consistent reconstruction and four
low-dimensional facial component embeddings for semantic face editing.
Manipulating the separate component embeddings can help achieve disentangled
attribute manipulation and flexible control of facial components. To further
disentangle the highly-correlated components, a component adaptive modulation
(CAM) module is proposed for the decoder. The semantic single-eye editing is
developed for the first time without any input visual guidance, such as
segmentation masks or sketches. According to the experimental results, IA-FaceS
establishes a good balance between maintaining image details and performing
flexible face manipulation. Both quantitative and qualitative results indicate
that the proposed method outperforms the other techniques in reconstruction,
face attribute manipulation, and component transfer.
