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.

翻译：过去几年来,生成网络在图像的恢复和重建方面证明极为有效,从文字描述中生成面孔是能够利用基因算法的力量的一种应用,生成面孔的任务对于寻找失踪人员、识别罪犯等许多应用可能有用。本文讨论了一种新颖的生成人脸孔的方法,对面部特征作了文字描述。我们利用现代自然语言处理模型的状态,将面部描述转换成可学习的潜在矢量,然后将其输入产生与这些特征相适应面孔的基因对抗网络。虽然本文只侧重于对面孔的高度描述,但同样的做法也可以根据微小的文字特征来生成任何图像。