  Unlike a conventional background inpainting approach that infers a missing
area from image patches similar to the background, face completion requires
semantic knowledge about the target object for realistic outputs. Current image
inpainting approaches utilize generative adversarial networks (GANs) to achieve
such semantic understanding. However, in adversarial learning, the semantic
knowledge is learned implicitly and hence good semantic understanding is not
always guaranteed. In this work, we propose a collaborative adversarial
learning approach to face completion to explicitly induce the training process.
Our method is formulated under a novel generative framework called
collaborative GAN (collaGAN), which allows better semantic understanding of a
target object through collaborative learning of multiple tasks including face
completion, landmark detection, and semantic segmentation. Together with the
collaGAN, we also introduce an inpainting concentrated scheme such that the
model emphasizes more on inpainting instead of autoencoding. Extensive
experiments show that the proposed designs are indeed effective and
collaborative adversarial learning provides better feature representations of
the faces. In comparison with other generative image inpainting models and
single task learning methods, our solution produces superior performances on
all tasks.
