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Text to artistic image generation

arXiv 2205.02439 ↗First posted 5 May 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Painting is one of the ways for people to express their ideas, but what if people with disabilities in hands want to paint? To tackle this challenge, we create an end-to-end solution that can generate artistic images from text descriptions. However, due to the lack of datasets with paired text description and artistic images, it is hard to directly train an algorithm which can create art based on text input. To address this issue, we split our task into three steps: (1) Generate a realistic image from a text description by using Dynamic Memory Generative Adversarial Network (arXiv:1904.01310), (2) Classify the image as a genre that exists in the WikiArt dataset using Resnet (arXiv: 1512.03385), (3) Select a style that is compatible with the genre and transfer it to the generated image by using neural artistic stylization network (arXiv:1705.06830).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 5 May 2022.

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26 of 36 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2014Going Deeper with Convolutionscited by 4,241
  5. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  6. 2013Describing Textures in the Wildcited by 2,125
  7. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  8. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  9. 2016Conditional Image Generation with PixelCNN Decoderscited by 862
  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. 2015A Neural Algorithm of Artistic Stylecited by 483
14 more
  1. 2016A Learned Representation For Artistic Stylecited by 341
  2. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  3. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  4. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  5. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  6. 2019Object-driven Text-to-Image Synthesis via Adversarial Trainingcited by 117
  7. 2016Resnet in Resnet: Generalizing Residual Architecturescited by 110
  8. 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
  9. 2017Exploring the structure of a real-time, arbitrary neural artistic stylization networkcited by 89
  10. 2019Semantic Object Accuracy for Generative Text-to-Image Synthesiscited by 89
  11. 2014Convolutional Neural Networks at Constrained Time Costcited by 81
  12. 2018Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Datacited by 76
  13. 2019Generating Multiple Objects at Spatially Distinct Locationscited by 41
  14. 2018Semantically Invariant Text-to-Image Generationcited by 2

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