LSRAGAN: GENERATING MULTIFARIOUS COLOR PHOTOGRAPHES FROM SKETCH

Image translation is to translate source domain image into target domain image. Generative Adversarial Networks (GANs) have achieved appealing performances on one-to-one image translation. The task of sketch translating to real-world images is a one-to-many task. In this paper, a new translation network, named Latent Space Regularization Attention GAN (LSRAGAN), is proposed to generate multifarious color photographes from sketch. We add Gaussian prior and latent variable from target domain to generator to realize multi-modal mapping. Attention weighting is implemented on ResNet blocks in generator. L1-constrained multi-layer perceptual loss is incorporated in conditional GAN. In addition, we present a latent space regularization (LSR) loss to force generator pay attention to the influence of latent code vector, which makes the generated images more diverse. Experiments demonstrate that our method outperforms state-of-the-arts in both quantitative and visual performances.