mlsanityData to 14 Oct 2026

ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization

arXiv 2308.12452 ↗First posted 23 Aug 2023cs.CV cs.GRcited by 6

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Abstract (arXiv)

The radiance fields style transfer is an emerging field that has recently gained popularity as a means of 3D scene stylization, thanks to the outstanding performance of neural radiance fields in 3D reconstruction and view synthesis. We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer. In this paper, we present ARF-Plus, a 3D neural style transfer framework offering manageable control over perceptual factors, to systematically explore the perceptual controllability in 3D scene stylization. Four distinct types of controls - color preservation control, (style pattern) scale control, spatial (selective stylization area) control, and depth enhancement control - are proposed and integrated into this framework. Results from real-world datasets, both quantitative and qualitative, show that the four types of controls in our ARF-Plus framework successfully accomplish their corresponding perceptual controls when stylizing 3D scenes. These techniques work well for individual style inputs as well as for the simultaneous application of multiple styles within a scene. This unlocks a realm of limitless possibilities, allowing customized modifications of stylization effects and flexible merging of the strengths of different styles, ultimately enabling the creation of novel and eye-catching stylistic effects on 3D scenes.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 23 Aug 2023.

Builds on

28 of 35 references.

References · 28

  1. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  2. 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
  3. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 905
  4. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  5. 2017Neural Style Transfer: A Reviewcited by 199
  6. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  7. 2022ARF: Artistic Radiance Fieldscited by 147
  8. 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
  9. 2022NeRF-Art: Text-Driven Neural Radiance Fields Stylizationcited by 126
  10. 2021Stylizing 3D Scene via Implicit Representation and HyperNetworkcited by 103
  11. 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
  12. 2023StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fieldscited by 79
16 more
  1. 2021Learning to Stylize Novel Viewscited by 76
  2. 2022Unified Implicit Neural Stylizationcited by 67
  3. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  4. 2021StyleMesh: Style Transfer for Indoor 3D Scene Reconstructionscited by 58
  5. 2022ArtFID: Quantitative Evaluation of Neural Style Transfercited by 49
  6. 2022UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scenecited by 47
  7. 2017Artistic style transfer for videos and spherical imagescited by 45
  8. 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
  9. 2022Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylizationcited by 35
  10. 2018Stroke Controllable Fast Style Transfer with Adaptive Receptive Fieldscited by 33
  11. 2022SNeRF: Stylized Neural Implicit Representations for 3D Scenescited by 26
  12. 2016Preserving Color in Neural Artistic Style Transfercited by 20
  13. 2018EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfercited by 13
  14. 2017Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentationcited by 8
  15. 2019ETNet: Error Transition Network for Arbitrary Style Transfercited by 3
  16. 2019A Method for Arbitrary Instance Style Transfercited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 2.

6 papers.