mlsanityData to 14 Oct 2026

Text-to-Face Generation with StyleGAN2

arXiv 2205.12512 ↗First posted 25 May 2022cs.CVcited by 0

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Abstract (arXiv)

Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation (T2F) is a sub-domain of Text-to-Image generation (T2I) that is more challenging due to the complexity and variation of facial attributes. It has a number of applications mainly in the domain of public safety. Even though several models are available for T2F, there is still the need to improve the image quality and the semantic alignment. In this research, we propose a novel framework, to generate facial images that are well-aligned with the input descriptions. Our framework utilizes the high-resolution face generator, StyleGAN2, and explores the possibility of using it in T2F. Here, we embed text in the input latent space of StyleGAN2 using BERT embeddings and oversee the generation of facial images using text descriptions. We trained our framework on attribute-based descriptions to generate images of 1024x1024 in resolution. The images generated exhibit a 57% similarity to the ground truth images, with a face semantic distance of 0.92, outperforming state-of-the-artwork. The generated images have a FID score of 118.097 and the experimental results show that our model generates promising images.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 25 May 2022.

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References · 23

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  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
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  10. 2016Improved Techniques for Training GANscited by 2,970
  11. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  12. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
11 more
  1. 2016Generative Adversarial Text to Image Synthesiscited by 735
  2. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  3. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  4. 2016Learning Deep Representations of Fine-grained Visual Descriptionscited by 182
  5. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  6. 2019Object-driven Text-to-Image Synthesis via Adversarial Trainingcited by 117
  7. 2018Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Networkcited by 64
  8. 2020Faces `a la Carte: Text-to-Face Generation via Attribute Disentanglementcited by 16
  9. 2019Text2FaceGAN: Face Generation from Fine Grained Textual Descriptionscited by 9
  10. 2019FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generationcited by 6
  11. 2018Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptionscited by 4

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