  Faces generated using generative adversarial networks (GANs) have reached
unprecedented realism. These faces, also known as "Deep Fakes", appear as
realistic photographs with very little pixel-level distortions. While some work
has enabled the training of models that lead to the generation of specific
properties of the subject, generating a facial image based on a natural
language description has not been fully explored. For security and criminal
identification, the ability to provide a GAN-based system that works like a
sketch artist would be incredibly useful. In this paper, we present a novel
approach to generate facial images from semantic text descriptions. The learned
model is provided with a text description and an outline of the type of face,
which the model uses to sketch the features. Our models are trained using an
Affine Combination Module (ACM) mechanism to combine the text embedding from
BERT and the GAN latent space using a self-attention matrix. This avoids the
loss of features due to inadequate "attention", which may happen if text
embedding and latent vector are simply concatenated. Our approach is capable of
generating images that are very accurately aligned to the exhaustive textual
descriptions of faces with many fine detail features of the face and helps in
generating better images. The proposed method is also capable of making
incremental changes to a previously generated image if it is provided with
additional textual descriptions or sentences.
