  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.
