Deep Cascaded Bi-Network for Face Hallucination
Shizhan Zhu1, Sifei Liu2, Chen Change Loy1,3, Xiaoou Tang1,3
1Department of Information Engineering, The Chinese University of Hong Kong
2University of California, Merced
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
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.
The code is available on the github page. Please refer to that page for details.
Shizhan Zhu, Sifei Liu, Chen Change Loy, Xiaoou Tang. Deep Cascaded Bi-Network for Face Hallucinationg, in Proceedings of European Conference on Computer Vision (ECCV), 2016.