mlsanityData to 7 Oct 2026

QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity

arXiv 2212.10431 ↗First posted 20 Dec 2022cs.CV cs.LG cs.MMcited by 18

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Abstract (arXiv)

The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However, this type of approach cannot guarantee visual fidelity, i.e., the generated artworks should be indistinguishable from real ones. In this paper, we devise a new style transfer framework called QuantArt for high visual-fidelity stylization. QuantArt pushes the latent representation of the generated artwork toward the centroids of the real artwork distribution with vector quantization. By fusing the quantized and continuous latent representations, QuantArt allows flexible control over the generated artworks in terms of content preservation, style similarity, and visual fidelity. Experiments on various style transfer settings show that our QuantArt framework achieves significantly higher visual fidelity compared with the existing style transfer methods.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 56 of its 69 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

56 of 69 references.

References · 56

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 5; 2025: 5; 2026: 1.

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  1. 2023StyleDrop: Text-to-Image Generation in Any Stylecited by 197
  2. 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
  3. 2023InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Advisercited by 17
  4. 2023Z^*: Zero-shot Style Transfer via Attention Rearrangementcited by 17
  5. 2023Learning to Evaluate the Artness of AI-generated Imagescited by 10
  6. 2024StyleMamba : State Space Model for Efficient Text-driven Image Style Transfercited by 5
  7. 2024Evaluation in Neural Style Transfer: A Reviewcited by 4
  8. 2025SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfercited by 4
  9. 2025A Training-Free Style-aligned Image Generation with Scale-wise Autoregressive Modelcited by 4
  10. 2023Painterly Image Harmonization by Learning from Painterly Objectscited by 3
  11. 2025Voxify3D: Pixel Art Meets Volumetric Renderingcited by 3
  12. 2023Optimal Image Transport on Sparse Dictionariescited by 1
6 more
  1. 2023Progressive Painterly Image Harmonization from Low-level Styles to High-level Stylescited by 1
  2. 2024Harnessing the Latent Diffusion Model for Training-Free Image Style Transfercited by 1
  3. 2026Image-to-Image Translation Framework Embedded with Rotation Symmetry Priorscited by 1
  4. 2024Bridging Text and Image for Artist Style Transfer via Contrastive Learningcited by 0
  5. 2025PQDAST: Depth-Aware Arbitrary Style Transfer for Games via Perceptual Quality-Guided Distillationcited by 0
  6. 2025Pluggable Style Representation Learning for Multi-Style Transfercited by 0