Semantic Image Synthesis from Inaccurate and Coarse Masks

Semantic image synthesis is an image-to-image translation problem where the goal is to learn mapping from semantic segmentation masks to corresponding photorealistic images. However, conventional semantic image synthesis methods require numerous pairs of correct semantic masks and real images, and collecting these pairs is not always possible. To address this issue, we propose a smoothing method, which we call local label smoothing (LLS), that incorporates label smoothing per small patch of an input mask to learn mapping from masks to images even when semantic masks are inaccurate. Furthermore, we also propose an extended method for coarse masks. We demonstrate the advantage of the proposed methods over existing methods to deal with noisy masks on several datasets.