mlsanityData to 7 Oct 2026

Texturize a GAN Using a Single Image

arXiv 2302.10600 ↗First posted 21 Feb 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Can we customize a deep generative model which can generate images that can match the texture of some given image? When you see an image of a church, you may wonder if you can get similar pictures for that church. Here we present a method, for adapting GANs with one reference image, and then we can generate images that have similar textures to the given image. Specifically, we modify the weights of the pre-trained GAN model, guided by the reference image given by the user. We use a patch discriminator adversarial loss to encourage the output of the model to match the texture on the given image, also we use a laplacian adversarial loss to ensure diversity and realism, and alleviate the contradiction between the two losses. Experiments show that the proposed method can make the outputs of GANs match the texture of the given image as well as keep diversity and realism.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 38 of its 41 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

38 of 41 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  4. 2014Going Deeper with Convolutionscited by 4,231
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  7. 2016Improved Techniques for Training GANscited by 2,938
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  9. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  10. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  11. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  12. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
26 more
  1. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  2. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  3. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  4. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  5. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  6. 2013DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognitioncited by 422
  7. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  8. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  9. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  10. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  11. 2016Learning from Simulated and Unsupervised Images through Adversarial Trainingcited by 287
  12. 2020Swapping Autoencoder for Deep Image Manipulationcited by 215
  13. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  14. 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
  15. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  16. 2020Semantic Photo Manipulation with a Generative Image Priorcited by 178
  17. 2018Transferring GANs: generating images from limited datacited by 134
  18. 2020Few-shot Image Generation with Elastic Weight Consolidationcited by 122
  19. 2019MineGAN: effective knowledge transfer from GANs to target domains with few imagescited by 106
  20. 2020Rewriting a Deep Generative Modelcited by 105
  21. 2019Image Generation From Small Datasets via Batch Statistics Adaptationcited by 102
  22. 2020RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Realcited by 89
  23. 2022A Closer Look at Few-shot Image Generationcited by 49
  24. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  25. 2021Sketch Your Own GANcited by 37
  26. 2020On Data Augmentation for GAN Trainingcited by 25

Built on by

Not cited yet.