SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting

In this paper, we focus on image <PRE_TAG>inpainting</POST_TAG> task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative models enables an efficient end-to-end framework for image synthesis and inpainting tasks, but existing methods based on generative models don't exploit the segmentation information to constrain the object shapes, which usually lead to blurry results on the boundary. To tackle this problem, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image <PRE_TAG>inpainting</POST_TAG>. This leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments. Our model factorizes the image <PRE_TAG>inpainting</POST_TAG> process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results. Experiments on multiple public datasets show that our approach outperforms existing methods in optimizing the image <PRE_TAG>inpainting</POST_TAG> quality, and the interactive segmentation guidance provides possibilities for multi-modal predictions of image inpainting.