  Face reenactment is a challenging task, as it is difficult to maintain
accurate expression, pose and identity simultaneously. Most existing methods
directly apply driving facial landmarks to reenact source faces and ignore the
intrinsic gap between two identities, resulting in the identity mismatch issue.
Besides, they neglect the entanglement of expression and pose features when
encoding driving faces, leading to inaccurate expressions and visual artifacts
on large-pose reenacted faces. To address these problems, we propose a
Large-pose Identity-preserving face reenactment network, LI-Net. Specifically,
the Landmark Transformer is adopted to adjust driving landmark images, which
aims to narrow the identity gap between driving and source landmark images.
Then the Face Rotation Module and the Expression Enhancing Generator decouple
the transformed landmark image into pose and expression features, and reenact
those attributes separately to generate identity-preserving faces with accurate
expressions and poses. Both qualitative and quantitative experimental results
demonstrate the superiority of our method.
