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Semantic Text-to-Face GAN -ST^2FG

Generating synthetic face images based on a specific text description is a difficult task. For security and criminal identification, a system that works like a sketch artist would be incredibly useful. In this paper, we propose a novel method for semantic textto-face generation. Our proposed architecture is designed to steer the image generation process towards highly precise images by controlling the input noise vector. Our models are trained using an affine combination mechanism to combine the image and text embedding along with 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. The proposed approach can generate images that are very accurately aligned to the exhaustive textual descriptions of faces with many fine detail features and help generate precise and realistic images. The proposed method is also capable of repeatedly modifying images based on given text descriptions with minimal distortion in the image, unlike the general behavior of GANs. Our method outperforms the current state-of-the-art methods like ManiGAN and TediGAN across four different metrics when tested on the benchmarked Multi-Modal CelebA-HQ consisting of 30,000 images and descriptions corresponding to each image, out of which 6,000 images belong to a pre-defined test set.