We present a novel framework for hallucinating faces of unconstrained poses
and with very low resolution (face size as small as 5pxIOD). In contrast to
existing studies that mostly ignore or assume pre-aligned face spatial
configuration (e.g. facial landmarks localization or dense correspondence
field), we alternatingly optimize two complementary tasks, namely face
hallucination and dense correspondence field estimation, in a unified
framework. In addition, we propose a new gated deep bi-network that contains
two functionality-specialized branches to recover different levels of texture
details. Extensive experiments demonstrate that such formulation allows
exceptional hallucination quality on in-the-wild low-res faces with significant
pose and illumination variations.

该论文提出了一种新的框架来幻想姿态不受限、分辨率非常低的面部图像，并在统一的框架内交替优化两个互补的任务，即面部幻象和密集对应场估计，并提出一种新的门控深度双向神经网络以恢复不同层次的纹理细节，广泛的实验证明这种表述方式在低分辨率、有显著姿势和光照变化的野外低分辨率面孔上具有例外的幻象质量。