mlsanityData to 7 Oct 2026

Visual Attribute Transfer through Deep Image Analogy

arXiv 1705.01088 ↗First posted 2 May 2017cs.CVcited by 123

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Abstract (arXiv)

We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene. Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of "image analogy" with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique Deep Image Analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style/texture transfer, color/style swap, sketch/painting to photo, and time lapse.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2017.

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Citing papers per year: 2021: 36; 2022: 31; 2023: 17; 2024: 23; 2025: 10; 2026: 6.

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  1. 2022Inversion-Based Style Transfer with Diffusion Modelscited by 290
  2. 2021StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generatorscited by 164
  3. 2022ARF: Artistic Radiance Fieldscited by 147
  4. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 135
  5. 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
  6. 2024Training-Free Consistent Text-to-Image Generationcited by 123
  7. 2021Learning Accurate Dense Correspondences and When to Trust Themcited by 113
  8. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  9. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  10. 2021PDC-Net+: Enhanced Probabilistic Dense Correspondence Networkcited by 80
  11. 2021CATs: Cost Aggregation Transformers for Visual Correspondencecited by 77
  12. 2021Enhancing Photorealism Enhancementcited by 74
38 more
  1. 2022Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfercited by 66
  2. 2022CATs++: Boosting Cost Aggregation with Convolutions and Transformerscited by 53
  3. 2021Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Modelscited by 51
  4. 2023Photoswap: Personalized Subject Swapping in Imagescited by 47
  5. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  6. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  7. 2024Customizing Text-to-Image Models with a Single Image Paircited by 36
  8. 2022Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylizationcited by 35
  9. 2021ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similaritycited by 34
  10. 20223D Highlighter: Localizing Regions on 3D Shapes via Text Descriptionscited by 32
  11. 2021Deep Matching Prior: Test-Time Optimization for Dense Correspondencecited by 30
  12. 2024Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Modelcited by 30
  13. 2025OmniStyle: Filtering High Quality Style Transfer Data at Scalecited by 30
  14. 2022Learning Graph Neural Networks for Image Style Transfercited by 29
  15. 2022EleGANt: Exquisite and Locally Editable GAN for Makeup Transfercited by 28
  16. 2021Dynamic Neural Garmentscited by 27
  17. 2022SNeRF: Stylized Neural Implicit Representations for 3D Scenescited by 26
  18. 2023Visual Instruction Inversion: Image Editing via Visual Promptingcited by 26
  19. 2022Rewriting Geometric Rules of a GANcited by 25
  20. 2022Neural Neighbor Style Transfercited by 23
  21. 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
  22. 2021SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removalcited by 22
  23. 2022MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translationcited by 22
  24. 2021Exemplar-Based 3D Portrait Stylizationcited by 21
  25. 2022AesUST: Towards Aesthetic-Enhanced Universal Style Transfercited by 21
  26. 2021Bi-level Feature Alignment for Versatile Image Translation and Manipulationcited by 20
  27. 2023Locally Stylized Neural Radiance Fieldscited by 20
  28. 2021Data-Driven Design-by-Analogy: State of the Art and Future Directionscited by 18
  29. 2022QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelitycited by 18
  30. 2024StyleSplat: 3D Object Style Transfer with Gaussian Splattingcited by 18
  31. 2021PSGAN++: Robust Detail-Preserving Makeup Transfer and Removalcited by 17
  32. 2022Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labelscited by 16
  33. 2022All-to-key Attention for Arbitrary Style Transfercited by 16
  34. 2024Geometry Transfer for Stylizing Radiance Fieldscited by 16
  35. 2024CoARF: Controllable 3D Artistic Style Transfer for Radiance Fieldscited by 14
  36. 2021Diverse Generation from a Single Video Made Possiblecited by 13
  37. 2024SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editingcited by 13
  38. 2024Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulationcited by 13

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