  Image inpainting is the task of filling-in missing regions of a damaged or
incomplete image. In this work we tackle this problem not only by using the
available visual data but also by incorporating image semantics through the use
of generative models. Our contribution is twofold: First, we learn a data
latent space by training an improved version of the Wasserstein generative
adversarial network, for which we incorporate a new generator and discriminator
architecture. Second, the learned semantic information is combined with a new
optimization loss for inpainting whose minimization infers the missing content
conditioned by the available data. It takes into account powerful contextual
and perceptual content inherent in the image itself. The benefits include the
ability to recover large regions by accumulating semantic information even it
is not fully present in the damaged image. Experiments show that the presented
method obtains qualitative and quantitative top-tier results in different
experimental situations and also achieves accurate photo-realism comparable to
state-of-the-art works.
