  Face frontalization refers to the process of synthesizing the frontal view of
a face from a given profile. Due to self-occlusion and appearance distortion in
the wild, it is extremely challenging to recover faithful results and preserve
texture details in a high-resolution. This paper proposes a High Fidelity Pose
Invariant Model (HF-PIM) to produce photographic and identity-preserving
results. HF-PIM frontalizes the profiles through a novel texture warping
procedure and leverages a dense correspondence field to bind the 2D and 3D
surface spaces. We decompose the prerequisite of warping into dense
correspondence field estimation and facial texture map recovering, which are
both well addressed by deep networks. Different from those reconstruction
methods relying on 3D data, we also propose Adversarial Residual Dictionary
Learning (ARDL) to supervise facial texture map recovering with only monocular
images. Exhaustive experiments on both controlled and uncontrolled environments
demonstrate that the proposed method not only boosts the performance of
pose-invariant face recognition but also dramatically improves high-resolution
frontalization appearances.
