mlsanityData to 14 Oct 2026

Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting

arXiv 2606.26754 ↗First posted 25 Jun 2026cs.CVcited by 0

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Abstract (arXiv)

While 3D Gaussian Splatting (3DGS) provides an efficient and explicit representation for novel view synthesis, enforcing stylistic coherence across viewpoints remains challenging. Existing 3D stylization methods typically apply 2D feature-matching losses independently per rendered view, which leads to unstable style allocation, many-to-one feature reuse, and limited cross-view consistency. We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded in optimal transport. Specifically, we reformulate local style matching as a semi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigates many-to-one matching and enables controllable allocation of style features. This transport-based objective provides a principled mechanism for balancing feature coverage and stylistic diversity while maintaining stable correspondences across viewpoints. To further enhance cross-view coherence, we incorporate a novel cross-view matching guidance to constrain correspondences between scene content and style patterns. In addition, we introduce several geometric regularizations to enhance the vanilla 3DGS, thereby enabling optimized Gaussian primitives to represent finer-grained textures during stylization. Extensive experiments demonstrate that our approach significantly improves multi-view stylistic consistency and produces stable, expressive 3D stylizations while preserving the core semantic structure of the scene.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 25 Jun 2026.

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33 of 43 references.

References · 33

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  4. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  5. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  6. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  8. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  9. 2024Depth Anything V2cited by 1,422
  10. 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
  11. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  12. 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
21 more
  1. 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 168
  2. 2022ARF: Artistic Radiance Fieldscited by 147
  3. 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
  4. 2021Stylizing 3D Scene via Implicit Representation and HyperNetworkcited by 103
  5. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  6. 2025MEt3R: Measuring Multi-View Consistency in Generated Imagescited by 95
  7. 2023StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fieldscited by 79
  8. 2024StyleGaussian: Instant 3D Style Transfer with Gaussian Splattingcited by 79
  9. 2017Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Lossescited by 69
  10. 2022ArtFID: Quantitative Evaluation of Neural Style Transfercited by 49
  11. 2016Scaling Algorithms for Unbalanced Transport Problemscited by 36
  12. 2025Attention Distillation: A Unified Approach to Visual Characteristics Transfercited by 33
  13. 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
  14. 2024SGSST: Scaling Gaussian Splatting StyleTransfercited by 20
  15. 2024Deformable One-shot Face Stylization via DINO Semantic Guidancecited by 14
  16. 2024FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fieldscited by 9
  17. 2025CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splattingcited by 8
  18. 2025ABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splattingcited by 7
  19. 2023ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylizationcited by 6
  20. 2025Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Stylescited by 4
  21. 2024StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fieldscited by 2

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