Abstract
Most of the existing face photo de-meshing methods have accomplished promising results; there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder–decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network.