For face texture completion, previous methods typically use some complete textures captured by multiview imaging systems or 3D scanners for supervised learning. This paper deals with a new challenging problem \u2013 learning to complete invisible texture in a single face image without using any complete texture. We simply leverage a large corpus of face images of different subjects (e. g., FFHQ) to train a texture completion model in an unsupervised manner. To achieve this, we propose DSD-GAN, a novel deep neural network based method that applies two discriminators in UV map space and image space. These two discriminators work in a complementary manner to learn both facial structures and texture details. We show that their combination is essential to obtain high-fidelity results. Despite the network never sees any complete facial appearance, it is able to generate compelling full textures from single images.<\/p>\n","protected":false},"excerpt":{"rendered":"
For face texture completion, previous methods typically use some complete textures captured by multiview imaging systems or 3D scanners for supervised learning. This paper deals with a new challenging problem \u2013 learning to complete invisible texture in a single face image without using any complete texture. We simply leverage a large corpus of face images 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