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TextFace: Text-to-Style Mapping Based Face Generation and Manipulation
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AI
DOI：10.1109/TMM.2022.3160360.png)
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Abstract
En 中文
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 (1024x1024). Furthermore, our model inherently supports semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model.
Keywords:
GANs
text-to-image generation
cross modal
text-to-face generation
text-guided semantic face manipulation
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