Different Techniques of Facial Image Generation from Textual Input : A Survey

Eakanath Raparla, Veeresh Raavipaati, Shiva Nikhil G, Sameer S T Md, K. S
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Abstract

The task of Text-to-Face synthesis is quite intricate and there hasn't been much research done on it, until recently. This is mainly due to the complex nature of human face and it's features, which vary widely over different situations. That being said, the field of Text-to-Image synthesis has gathered considerable interest quite late. In earlier research, generation is mainly done using reconstruction of visuals which correlate to the given words. Due to the rise of generative models in the field of deep learning, there has been a departure from these traditional computer vision based retrieval methods. One of the most significant factors for this change is the introduction of GANs. The idea of learning and reproducing the image as a whole, helped in producing better images. The introduction of attention based mechanisms, helped in synthesizing more detailed images which manages to show several facial features like eye brows, hair color, nose shape etc. In this survey, we discuss and summarize some of the methods used for the purpose of image & face generation and their development over the years.
基于文本输入的人脸图像生成技术综述
文本到人脸合成的任务非常复杂,直到最近才有很多研究。这主要是由于人脸的复杂性及其特征,在不同的情况下差异很大。话虽如此,文本到图像的合成领域在相当晚的时候才引起了相当大的兴趣。在早期的研究中,生成主要是通过重建与给定单词相关的视觉效果来完成的。由于生成模型在深度学习领域的兴起,这些传统的基于计算机视觉的检索方法已经有所不同。造成这种变化的最重要因素之一是gan的引入。学习和复制整个图像的想法有助于产生更好的图像。引入基于注意力的机制,有助于合成更详细的图像,这些图像能够显示出几个面部特征,如眉毛、头发颜色、鼻子形状等。在本调查中,我们讨论和总结了一些用于图像和人脸生成的方法及其多年来的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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