  Blind face restoration usually encounters with diverse scale face inputs,
especially in the real world. However, most of the current works support
specific scale faces, which limits its application ability in real-world
scenarios. In this work, we propose a novel scale-aware blind face restoration
framework, named FaceFormer, which formulates facial feature restoration as
scale-aware transformation. The proposed Facial Feature Up-sampling (FFUP)
module dynamically generates upsampling filters based on the original
scale-factor priors, which facilitate our network to adapt to arbitrary face
scales. Moreover, we further propose the facial feature embedding (FFE) module
which leverages transformer to hierarchically extract diversity and robustness
of facial latent. Thus, our FaceFormer achieves fidelity and robustness
restored faces, which possess realistic and symmetrical details of facial
components. Extensive experiments demonstrate that our proposed method trained
with synthetic dataset generalizes better to a natural low quality images than
current state-of-the-arts.
