Abstract. We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD 1 ). 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.
For un-aligned faces, Zhu et al. REF proposed to jointly learn face hallucination and facial dense spatial correspondence field estimation.
Deep Cascaded Bi-Network for Face Hallucination
ArXiv
ArXiv
Computer Science