Image completion is widely used in unwanted object removal and media editing,which aims to find a semantically consistent way to recover corrupted images.This paper is based on generative adversarial network(GAN)inversion,which leverages a pre-trained GAN model as an effective prior to filling in the missing regions with photo-realistic textures.However,existing GAN inversion methods ignore that image completion is a generative task with hard constraints,making final images have noticeable color and semantic discontinuity issues.This paper designs a novel bi-directional perceptual generator and pre-modulation network to seamlessly fill in the images.The bi-directional perceptual generator uses extended latent space to help the model perceive the non-missing regions of the input images in terms of data representations.The pre-modulated networks utilize a multiscale structure further providing more discriminative semantics for the style vectors.In this paper,experiments are conducted on Places2 and CelebA-HQ datasets to verify that the proposed method builds a bridge between GAN inversion and image completion and outperforms current mainstream algorithms,especially in FID metrics up to 49.2％enhancement at most.
万方数据
维普
