mlsanityData to 14 Oct 2026

Coarse-to-Fine Structure-Aware Artistic Style Transfer

arXiv 2502.05387 ↗First posted 8 Feb 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Artistic style transfer aims to use a style image and a content image to synthesize a target image that retains the same artistic expression as the style image while preserving the basic content of the content image. Many recently proposed style transfer methods have a common problem; that is, they simply transfer the texture and color of the style image to the global structure of the content image. As a result, the content image has a local structure that is not similar to the local structure of the style image. In this paper, we present an effective method that can be used to transfer style patterns while fusing the local style structure into the local content structure. In our method, dif-ferent levels of coarse stylized features are first reconstructed at low resolution using a Coarse Network, in which style color distribution is roughly transferred, and the content structure is combined with the style structure. Then, the reconstructed features and the content features are adopted to synthesize high-quality structure-aware stylized images with high resolution using a Fine Network with three structural selective fusion (SSF) modules. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a com-parison with some state-of-the-art style transfer methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 8 Feb 2025.

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References · 25

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  8. 2017Universal Style Transfer via Feature Transformscited by 354
  9. 2016A Learned Representation For Artistic Stylecited by 341
  10. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  11. 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 198
  12. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
13 more
  1. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 174
  2. 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 168
  3. 2018Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decorationcited by 115
  4. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  5. 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
  6. 2022Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfercited by 66
  7. 2017Multi-style Generative Network for Real-time Transfercited by 61
  8. 2018A Style-Aware Content Loss for Real-time HD Style Transfercited by 58
  9. 2020Collaborative Distillation for Ultra-Resolution Universal Style Transfercited by 52
  10. 2019Diversified Arbitrary Style Transfer via Deep Feature Perturbationcited by 42
  11. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  12. 2016Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfercited by 30
  13. 2022VToonify: Controllable High-Resolution Portrait Video Style Transfercited by 20

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