mlsanityData to 14 Oct 2026

Controllable Style Transfer via Test-time Training of Implicit Neural Representation

arXiv 2210.07762 ↗First posted 14 Oct 2022cs.CVcited by 8

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Abstract (arXiv)

We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training. Unlike traditional image optimization methods that often suffer from unstable convergence and learning-based methods that require intensive training and have limited generalization ability, we present a model optimization framework that optimizes the neural networks during test-time with explicit loss functions for style transfer. After being test-time trained once, thanks to the flexibility of the INR-based model, our framework can precisely control the stylized images in a pixel-wise manner and freely adjust image resolution without further optimization or training. We demonstrate several applications.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 40 of its 62 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

40 of 62 references.

References · 40

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28 more
  1. 2020Implicit Geometric Regularization for Learning Shapescited by 710
  2. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
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  19. 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
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  21. 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
  22. 2018Arbitrary Style Transfer with Deep Feature Reshufflecited by 75
  23. 2017Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Lossescited by 69
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  25. 2021Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalizationcited by 15
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 2.

8 papers.

  1. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
  2. 2024FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Modelscited by 22
  3. 2024CoARF: Controllable 3D Artistic Style Transfer for Radiance Fieldscited by 14
  4. 2025Advances in Artificial Intelligence: A Review for the Creative Industriescited by 2
  5. 2025Bias for Action: Video Implicit Neural Representations with Bias Modulationcited by 2
  6. 2024Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representationscited by 1
  7. 2026MAST: Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfercited by 0
  8. 2026Coarse-to-fine Framework for Generative MEF via Implicit Neural Representationcited by 0