Perceptual Face Completion using a Local-Global Generative Adversarial Network

Face completion is one of the most challenging problems, as the reconstruction algorithm should render the missing pixels with semantically plausible contents. Recent methods have achieved promising advances in photorealistic human face synthesis. However, these approaches are limited to deal with general or structure specified faces. In this paper, we propose a Two-Pathway Perceptual Generative Adversarial Network (TPP-GAN) for face completion by perceiving semantic representations from both global structures and local details of a face. We combine a reconstruction network and a perceptual network containing two pathway adversarial networks (local and global) into our framework to efficiently ensure the transfer of the prominent facial features to the occluded parts, which encourages a visually high-quality image completion results. Experimental results well demonstrate that our proposed framework not only generates locally semantic and globally consistent fragments, but also outperforms existing methods on unaligned faces and synthesis of part components.