PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting
This addresses the need for pluralistic inpainting in computer vision applications, offering an incremental improvement over existing deep learning methods by introducing style extraction and consistency loss.
The paper tackles the problem of generating only a single plausible result in image inpainting by proposing PiiGAN, a model that produces multiple diverse inpainting results consistent with image context, achieving better quality and higher diversity compared to state-of-the-art methods on datasets like CelebA, PlantVillage, and MauFlex.
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.