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Generative Rendering Network based on U-shape Discriminator

This paper introduces a generative rendering network(GRN) based on a U-shape discriminator for novel view image synthesis. Recently, some generative adversarial networks start to explore 3D space and synthesize new images by rendering methods. Compared with 2D-only networks, they have achieved impressive results in synthesizing novel view images. However, they still have a common shortcoming: the discriminator cannot provide more detailed information for training a strong generator. For this problem, we design an U-shape based discriminator to extract both global information and pixel-wise information. This innovative discriminator will feedback more instructive clues to train a better generator. Benefit from this strategy, our synthesis results on ShapeNet v2 “cars” and “chairs” and celebA have achieved better performance than comparative methods.