We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods 1 .
The method presented in REF generates facial parts by CNNs and explicitly synthesizes fine-grained facial structures through part enhancement.
Learning to Hallucinate Face Images via Component Generation and Enhancement
ArXiv
ArXiv
Computer Science