  Generative Networks have proved to be extremely effective in image
restoration and reconstruction in the past few years. Generating faces from
textual descriptions is one such application where the power of generative
algorithms can be used. The task of generating faces can be useful for a number
of applications such as finding missing persons, identifying criminals, etc.
This paper discusses a novel approach to generating human faces given a textual
description regarding the facial features. We use the power of state of the art
natural language processing models to convert face descriptions into learnable
latent vectors which are then fed to a generative adversarial network which
generates faces corresponding to those features. While this paper focuses on
high level descriptions of faces only, the same approach can be tailored to
generate any image based on fine grained textual features.
