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Multi-Attributed and Structured Text-to-Face Synthesis

arXiv 2108.11100 ↗First posted 25 Aug 2021cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Frechet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 25 Aug 2021.

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References · 16

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  4. 2016Improved Techniques for Training GANscited by 2,970
  5. 2014Conditional Generative Adversarial Netscited by 2,162
  6. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  7. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  8. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  9. 2016Generative Adversarial Text to Image Synthesiscited by 735
  10. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  11. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
  12. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
4 more
  1. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  2. 2019MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networkscited by 14
  3. 2019FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generationcited by 6
  4. 2018Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptionscited by 4

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