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Controlling Perceptual Factors in Neural Style Transfer

arXiv 1611.07865 ↗First posted 23 Nov 2016cs.CVcited by 100

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Abstract (arXiv)

Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.

Key facts. Cited by 100 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 25; 2022: 24; 2023: 22; 2024: 16; 2025: 11; 2026: 2.

100 papers.

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  1. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 291
  2. 2024Shape of Motion: 4D Reconstruction from a Single Videocited by 250
  3. 2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 212
  4. 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
  5. 2022Monocular Dynamic View Synthesis: A Reality Checkcited by 200
  6. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 136
  7. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  8. 2022WildNet: Learning Domain Generalized Semantic Segmentation from the Wildcited by 76
  9. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  10. 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
  11. 2021Diffusion-Based Representation Learningcited by 57
  12. 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
38 more
  1. 2023SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priorscited by 43
  2. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  3. 2023StyleAdapter: A Unified Stylized Image Generation Modelcited by 39
  4. 2021Language-Driven Image Style Transfercited by 37
  5. 2021A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyondcited by 37
  6. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  7. 2023Going Beyond Nouns With Vision & Language Models Using Synthetic Datacited by 33
  8. 2022FIFO: Learning Fog-invariant Features for Foggy Scene Segmentationcited by 31
  9. 2025OmniStyle: Filtering High Quality Style Transfer Data at Scalecited by 30
  10. 2024StyleBooth: Image Style Editing with Multimodal Instructioncited by 28
  11. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  12. 2023DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagationcited by 25
  13. 2022Neural Neighbor Style Transfercited by 23
  14. 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
  15. 2021Creativity and Machine Learning: A Surveycited by 21
  16. 2022AesUST: Towards Aesthetic-Enhanced Universal Style Transfercited by 21
  17. 2024SGSST: Scaling Gaussian Splatting StyleTransfercited by 20
  18. 2022SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Mapscited by 19
  19. 2022QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelitycited by 18
  20. 2022Style-ERD: Responsive and Coherent Online Motion Style Transfercited by 17
  21. 2023InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Advisercited by 17
  22. 2022All-to-key Attention for Arbitrary Style Transfercited by 16
  23. 2021Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalizationcited by 15
  24. 2021Texture Reformer: Towards Fast and Universal Interactive Texture Transfercited by 12
  25. 2023Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentationcited by 12
  26. 2021Domain-robust VQA with diverse datasets and methods but no target labelscited by 11
  27. 2023A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learningcited by 10
  28. 2024SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolutioncited by 10
  29. 2021Multiple Style Transfer via Variational AutoEncodercited by 9
  30. 2022Industrial Style Transfer with Large-scale Geometric Warping and Content Preservationcited by 9
  31. 2021Automatic Non-Linear Video Editing Transfercited by 8
  32. 2022Controllable Style Transfer via Test-time Training of Implicit Neural Representationcited by 8
  33. 2021Neural Photometry-guided Visual Attribute Transfercited by 6
  34. 2022An Overview of Color Transfer and Style Transfer for Images and Videoscited by 6
  35. 2022Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metriccited by 6
  36. 2023ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylizationcited by 6
  37. 2025StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussianscited by 6
  38. 2021Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removalcited by 5

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