  Face image manipulation via three-dimensional guidance has been widely
applied in various interactive scenarios due to its semantically-meaningful
understanding and user-friendly controllability. However, existing
3D-morphable-model-based manipulation methods are not directly applicable to
out-of-domain faces, such as non-photorealistic paintings, cartoon portraits,
or even animals, mainly due to the formidable difficulties in building the
model for each specific face domain. To overcome this challenge, we propose, as
far as we know, the first method to manipulate faces in arbitrary domains using
human 3DMM. This is achieved through two major steps: 1) disentangled mapping
from 3DMM parameters to the latent space embedding of a pre-trained StyleGAN2
that guarantees disentangled and precise controls for each semantic attribute;
and 2) cross-domain adaptation that bridges domain discrepancies and makes
human 3DMM applicable to out-of-domain faces by enforcing a consistent latent
space embedding. Experiments and comparisons demonstrate the superiority of our
high-quality semantic manipulation method on a variety of face domains with all
major 3D facial attributes controllable: pose, expression, shape, albedo, and
illumination. Moreover, we develop an intuitive editing interface to support
user-friendly control and instant feedback. Our project page is
https://cassiepython.github.io/sigasia/cddfm3d.html.
