Large Mask Image Completion with Conditional GAN

Abstract

Recently, learning-based image completion methods have made encouraging progress on square or irregular masks. The generative adversarial networks (GANs) have been able to produce visually realistic and semantically correct results. However, much texture and structure information will be lost in the completion process. If the missing part is too large to provide useful information, the result will be ambiguity, residual shadow, and object confusion. In order to complete large mask images, we present a novel model using conditional GAN called coarse-to-fine condition GAN (CF CGAN). We use a coarse-to-fine generator with symmetry and new perceptual loss based on VGG-16. The generator is symmetric in structure. For large mask image completion, our method produces visually realistic and semantically correct results. The generalization ability of our model is also excellent. We evaluate our model on the CelebA dataset and use FID, LPIPS, and SSIM as the metrics. Experiments demonstrate superior performance in terms of both quality and reality in free-form image completion.