mlsanityData to 7 Oct 2026

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

arXiv 1603.03417 ↗First posted 10 Mar 2016cs.CVcited by 173

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Abstract (arXiv)

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys~et~al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.

Key facts. Cited by 173 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 51; 2022: 45; 2023: 30; 2024: 27; 2025: 12; 2026: 8.

173 papers.

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  1. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 236
  2. 2021Few-shot Image Generation via Cross-domain Correspondencecited by 206
  3. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  4. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  5. 2021Stylizing 3D Scene via Implicit Representation and HyperNetworkcited by 103
  6. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  7. 2021Learning to Stylize Novel Viewscited by 76
  8. 2021Region-aware Adaptive Instance Normalization for Image Harmonizationcited by 71
  9. 2022Unified Implicit Neural Stylizationcited by 67
  10. 2022MagicMix: Semantic Mixing with Diffusion Modelscited by 63
  11. 2024StyleShot: A Snapshot on Any Stylecited by 60
  12. 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
38 more
  1. 2022ArtFID: Quantitative Evaluation of Neural Style Transfercited by 49
  2. 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
  3. 2022CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfercited by 46
  4. 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
  5. 2022UV Volumes for Real-time Rendering of Editable Free-view Human Performancecited by 43
  6. 2021Rethinking Style Transfer: From Pixels to Parameterized Brushstrokescited by 42
  7. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  8. 2022Federated Domain Generalization for Image Recognition via Cross-Client Style Transfercited by 40
  9. 2021Generative Modelling of BRDF Textures from Flash Imagescited by 35
  10. 2021ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similaritycited by 34
  11. 2022FIFO: Learning Fog-invariant Features for Foggy Scene Segmentationcited by 31
  12. 2023General Image-to-Image Translation with One-Shot Image Guidancecited by 31
  13. 2021Style-Aware Normalized Loss for Improving Arbitrary Style Transfercited by 30
  14. 2024Generative Human Motion Stylization in Latent Spacecited by 30
  15. 2021Where and What? Examining Interpretable Disentangled Representationscited by 27
  16. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  17. 2023DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagationcited by 25
  18. 2023CAP-VSTNet: Content Affinity Preserved Versatile Style Transfercited by 24
  19. 2024TexSliders: Diffusion-Based Texture Editing in CLIP Spacecited by 23
  20. 2021Exemplar-Based 3D Portrait Stylizationcited by 21
  21. 2022AesUST: Towards Aesthetic-Enhanced Universal Style Transfercited by 21
  22. 2021ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approachcited by 20
  23. 2024SGSST: Scaling Gaussian Splatting StyleTransfercited by 20
  24. 2021Robust Differentiable SVDcited by 19
  25. 2022SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Mapscited by 19
  26. 2022MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfercited by 19
  27. 2022CoordGAN: Self-Supervised Dense Correspondences Emerge from GANscited by 17
  28. 2023Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaboratecited by 17
  29. 2022All-to-key Attention for Arbitrary Style Transfercited by 16
  30. 2021Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalizationcited by 15
  31. 2021A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networkscited by 14
  32. 2022Name Your Style: An Arbitrary Artist-aware Image Style Transfercited by 14
  33. 2022S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attentioncited by 14
  34. 2024StyleTex: Style Image-Guided Texture Generation for 3D Modelscited by 14
  35. 2021Facial-Sketch Synthesis: A New Challengecited by 13
  36. 2022Consistent Style Transfercited by 13
  37. 20223D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesiscited by 13
  38. 2023Improving the Transferability of Adversarial Examples with Arbitrary Style Transfercited by 13

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