TextFace: Text-to-Style Mapping Based Face Generation and Manipulation
As a subtopic of text-to-image synthesis, text-to-face generation has great potential in face-related applications. In this paper, we propose a generic text-to-face framework, namely, TextFace, to achieve diverse and high-quality face image generation from text descriptions. We introduce text-to-style mapping, a novel method where the text description can be directly encoded into the latent space of a pretrained StyleGAN. Guided by our text-image similarity matching and face captioning-based text alignment, the textual latent code can be fed into the generator of a well-trained StyleGAN to produce diverse face images with high resolution (1024×1024). Furthermore, our model inherently supports semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model.
IEEE Transactions on Multimedia
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Hou, Xianxu; Zhang, Xiaokang; Li, Yudong; and Shen, Linlin, "TextFace: Text-to-Style Mapping Based Face Generation and Manipulation" (2023). Kean Publications. 476.