Semantic Image Synthesis via Location Aware Generative Adversarial Network

Abstract

Semantic image synthesis aims to synthesize photo-realistic images through the given semantic segmentation masks. Most existing models use conditional batch normalization (CBN) to regulate normalization activation by spatially varying modulation parameters. It can prevent semantic information from being eliminated during normalization. But the modulation parameters in CBN lack location constraint, resulting in the lack of structural information in the synthetic image. And CBN is highly dependent on the batch size. To address these limitations, we propose location aware conditional group normalization (LACGN) and construct a location aware generative adversarial network (LAGAN) based on this method. LACGN can learn spatial location aware information in a weakly supervised manner that relies on the current image synthesis process to guide transformations spatially. It allows the synthetic image to have more structural information and detailed features. At the same time, group normalization(GN) replace the traditional BN to eliminate the dependence on batch size. Extensive experiments show that LAGAN is better than other methods.