GaFET: Learning Geometry-aware Facial Expression Translation from
  In-The-Wild Images
Tianxiang Ma Bingchuan Li Qian He Jing Dong Tieniu Tan
While current face animation methods can manipulate expressions individually,
they suffer from several limitations. The expressions manipulated by some
motion-based facial reenactment models are crude. Other ideas modeled with
facial action units cannot generalize to arbitrary expressions not covered by
annotations. In this paper, we introduce a novel Geometry-aware Facial
Expression Translation (GaFET) framework, which is based on parametric 3D
facial representations and can stably decoupled expression. Among them, a
Multi-level Feature Aligned Transformer is proposed to complement non-geometric
facial detail features while addressing the alignment challenge of spatial
features. Further, we design a De-expression model based on StyleGAN, in order
to reduce the learning difficulty of GaFET in unpaired "in-the-wild" images.
Extensive qualitative and quantitative experiments demonstrate that we achieve
higher-quality and more accurate facial expression transfer results compared to
state-of-the-art methods, and demonstrate applicability of various poses and
complex textures. Besides, videos or annotated training data are omitted,
making our method easier to use and generalize.