mlsanityData to 7 Oct 2026

Texture Representation via Analysis and Synthesis with Generative Adversarial Networks

arXiv 2212.09983 ↗First posted 20 Dec 2022cs.CVcited by 3

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Abstract (arXiv)

We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with Gramian loss for real textures. We propose perceptual procedures for evaluating network capabilities, exploring the global and local behavior of latent space trajectories, and comparing with existing texture analysis-synthesis techniques.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 39 of its 60 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

39 of 60 references.

References · 39

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  9. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  10. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  11. 2013Describing Textures in the Wildcited by 2,110
  12. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
27 more
  1. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  2. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  3. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  4. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  5. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 691
  6. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  7. 2020In-Domain GAN Inversion for Real Image Editingcited by 420
  8. 2021GAN Inversion: A Surveycited by 366
  9. 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
  10. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  11. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
  12. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  13. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  14. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  15. 2016Inverting The Generator Of A Generative Adversarial Networkcited by 140
  16. 2020Generative Hierarchical Features from Synthesizing Imagescited by 83
  17. 2014Material Recognition in the Wild with the Materials in Context Databasecited by 77
  18. 2014PCANet: A Simple Deep Learning Baseline for Image Classification?cited by 70
  19. 2021Using latent space regression to analyze and leverage compositionality in GANscited by 55
  20. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  21. 2019Learning a Neural 3D Texture Space from 2D Exemplarscited by 53
  22. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  23. 2018From BoW to CNN: Two Decades of Texture Representation for Texture Classificationcited by 39
  24. 2015Deep filter banks for texture recognition, description, and segmentationcited by 19
  25. 2020GramGAN: Deep 3D Texture Synthesis From 2D Exemplarscited by 18
  26. 2016Using Filter Banks in Convolutional Neural Networks for Texture Classificationcited by 15
  27. 2020Differential Viewpoints for Ground Terrain Material Recognitioncited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2023StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotationcited by 3
  2. 2024Multi-Texture Synthesis through Signal Responsive Neural Cellular Automatacited by 1
  3. 2024ColorwAI: Generative Colorways of Textiles through GAN and Diffusion Disentanglementcited by 1