Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks

TL;DR

This paper presents a novel method that combines natural language processing and generative adversarial networks to create human face images from textual descriptions, with potential applications in missing persons and criminal identification.

Contribution

It introduces a new approach that converts textual facial descriptions into latent vectors for face generation using GANs, enhancing image synthesis from natural language.

Findings

Effective face generation from high-level textual descriptions

Potential for generating diverse images based on detailed text

Framework adaptable to other image generation tasks

Abstract

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.