  Automatic synthesis of faces from visual attributes is an important problem
in computer vision and has wide applications in law enforcement and
entertainment. With the advent of deep generative convolutional neural networks
(CNNs), attempts have been made to synthesize face images from attributes and
text descriptions. In this paper, we take a different approach, where we
formulate the original problem as a stage-wise learning problem. We first
synthesize the facial sketch corresponding to the visual attributes and then we
generate the face image based on the synthesized sketch. The proposed
framework, is based on a combination of two different Generative Adversarial
Networks (GANs) - (1) a sketch generator network which synthesizes realistic
sketch from the input attributes, and (2) a face generator network which
synthesizes facial images from the synthesized sketch images with the help of
facial attributes. Extensive experiments and comparison with recent methods are
performed to verify the effectiveness of the proposed attribute-based two-stage
face synthesis method.
