Facial Image Inpainting Using Multi-level Generative Network
Jie Liu, Cheolkon Jung · 2019
Facial image inpainting is a challenging task due to the loss of key components in faces such as eyes and nose. In this paper, we propose a simple yet efficient method to inpaint missing information from a facial image. We build an end-to-end multi-level generative network to capture features at various levels while reducing training time. We adopt multi-scale feature maps to produce natural-looking faces with realistic texture. To optimize the parameters of the proposed network, we use two kinds of losses: content and texture. The former loss consists of mean absolute error (MAE) and multi-scale structural similarity (MS-SSIM) losses to minimize distortion in content, while the latter one contains style and adversarial losses to facilitate texture synthesis. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative measurements. The code is available at https://github.com/JieLiu95/MLGN.