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.

本研究提出了一种新型的规模感知的盲目人脸修复模型，名为 FaceFormer，利用 Facial Feature Up-sampling 模块生成动态上采样滤波器以适应不同尺度的脸部输入，并借助于 facial feature embedding 模块利用 transformer 分层提取面部潜在多样性和稳健性，该方法在合成数据集训练得到的结果不仅具有高保真度和鲁棒性，还能泛化至自然低质量图像。