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Face photo synthesis from simple line drawing is a one-to-many task as simpleline drawing merely contains the contour of human face. Previous exemplar-basedmethods are over-dependent on the datasets and are hard to generalize tocomplicated natural scenes. Recently, several works utilize deep neuralnetworks to increase the generalization, but they are still limited in thecontrollability of the users. In this paper, we propose a deep generative modelto synthesize face photo from simple line drawing controlled by face attributessuch as hair color and complexion. In order to maximize the controllability offace attributes, an attribute-disentangled variational auto-encoder (AD-VAE) isfirstly introduced to learn latent representations disentangled with respect tospecified attributes. Then we conduct photo synthesis from simple line drawingbased on AD-VAE. Experiments show that our model can well disentangle thevariations of attributes from other variations of face photos and synthesizedetailed photorealistic face images with desired attributes. Regardingbackground and illumination as the style and human face as the content, we canalso synthesize face photos with the target style of a style photo.

Attribute-controlled face photo synthesis from simple line drawing