  The latest methods based on deep learning have achieved amazing results
regarding the complex work of inpainting large missing areas in an image. But
this type of method generally attempts to generate one single "optimal" result,
ignoring many other plausible results. Considering the uncertainty of the
inpainting task, one sole result can hardly be regarded as a desired
regeneration of the missing area. In view of this weakness, which is related to
the design of the previous algorithms, we propose a novel deep generative model
equipped with a brand new style extractor which can extract the style feature
(latent vector) from the ground truth. Once obtained, the extracted style
feature and the ground truth are both input into the generator. We also craft a
consistency loss that guides the generated image to approximate the ground
truth. After iterations, our generator is able to learn the mapping of styles
corresponding to multiple sets of vectors. The proposed model can generate a
large number of results consistent with the context semantics of the image.
Moreover, we evaluated the effectiveness of our model on three datasets, i.e.,
CelebA, PlantVillage, and MauFlex. Compared to state-of-the-art inpainting
methods, this model is able to offer desirable inpainting results with both
better quality and higher diversity. The code and model will be made available
on https://github.com/vivitsai/PiiGAN.
