  Powerful generative adversarial networks (GAN) have been developed to
automatically synthesize realistic images from text. However, most existing
tasks are limited to generating simple images such as flowers from captions. In
this work, we extend this problem to the less addressed domain of face
generation from fine-grained textual descriptions of face, e.g., "A person has
curly hair, oval face, and mustache". We are motivated by the potential of
automated face generation to impact and assist critical tasks such as criminal
face reconstruction. Since current datasets for the task are either very small
or do not contain captions, we generate captions for images in the CelebA
dataset by creating an algorithm to automatically convert a list of attributes
to a set of captions. We then model the highly multi-modal problem of text to
face generation as learning the conditional distribution of faces (conditioned
on text) in same latent space. We utilize the current state-of-the-art GAN
(DC-GAN with GAN-CLS loss) for learning conditional multi-modality. The
presence of more fine-grained details and variable length of the captions makes
the problem easier for a user but more difficult to handle compared to the
other text-to-image tasks. We flipped the labels for real and fake images and
added noise in discriminator. Generated images for diverse textual descriptions
show promising results. In the end, we show how the widely used inceptions
score is not a good metric to evaluate the performance of generative models
used for synthesizing faces from text.
