  Synthesis of face images from visual attributes is an important problem in
computer vision and biometrics due to its applications in law enforcement and
entertainment. Recent advances in deep generative networks have made it
possible to synthesize high-quality face images from visual attributes.
However, existing methods are specifically designed for generating unimodal
images (i.e visible faces) from attributes. In this paper, we propose a novel
generative adversarial network that simultaneously synthesizes identity
preserving multimodal face images (i.e. visible, sketch, thermal, etc.) from
visual attributes without requiring paired data in different domains for
training the network. We introduce a novel generator with multimodal
stretch-out modules to simultaneously synthesize multimodal face images.
Additionally, multimodal stretch-in modules are introduced in the discriminator
which discriminates between real and fake images. Extensive experiments and
comparisons with several state-of-the-art methods are performed to verify the
effectiveness of the proposed attribute-based multimodal synthesis method.
