Eye In-Painting with Exemplar Generative Adversarial Networks

Understand

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs).

• ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in painting results.
• We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object.
• Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power.