Pith. sign in

REVIEW 4 major objections 5 minor 57 references

SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read SSGaussian claims that 3D style transfer should be split into two stages: diffusion-based key-view stylization with cross-view attention, then instance-level transfer onto 3D Gaussians via group matching, preserving object structure and mul

desk verdict A plausible integration of diffusion priors and Gaussian Grouping for 3D style transfer, but the 'significantly outperforms' claim outruns the evidence. read the letter →

arxiv 2509.04379 v1 pith:YMHLKOHB submitted 2025-09-04 cs.CV cs.AI

Jimin Xu , Bosheng Qin , Tao Jin , Zhou Zhao , Zhenhui Ye , Jun Yu , Fei Wu This is my paper
classification cs.CVcs.AI
keywords 3DstyletransferGaussianSplattingdiffusionmodelscross-viewattentioninstancesegmentationgroupmatchingmulti-viewconsistency
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a 3D style transfer pipeline for scenes reconstructed with 3D Gaussian Splatting. It first uses a pretrained 2D diffusion model to stylize a handful of key viewpoints, then transfers that stylization onto the 3D representation. To keep the same object looking the same across views, it adds cross-view attention inside the diffusion model's final upsampling block. To lift the 2D results into 3D without blur or artifacts, it matches instances across views using identity encodings and applies nearest-neighbor style matching within each matched region. The authors report that this beats existing methods on both consistency and rendering-quality metrics over forward-facing and 360-degree scenes.

What carries the argument

Two mechanisms carry the argument. Cross-View Style Alignment (CVSA) extends the UNet's self-attention to cross-view attention, Attn(z, z1:K), letting the query of one key view look at keys and values of other key views; inserting it at the last upsampling block gives the best trade-off between style fidelity and instance-level consistency. Instance-level Style Transfer (IST) uses the Identity Encoding parameters from Gaussian Grouping to render per-pixel group identities, builds a group-matching mapping M(xi)=yi that associates the same instance across training and stylized key views, and then applies a localized nearest-neighbor feature matching loss per group against VGG features.

What would settle it

Take a scene containing two visually similar objects placed close together, stylize it, and compare per-instance style statistics (e.g., average color and texture within each instance mask) across stylized key views and novel views. If the identity encodings swap or blur the two instances across viewpoints, the group-matching map will associate the wrong regions and the final render will show object A adopting object B's style; a visible cross-over of style attributes between the two instances would directly contradict the instance-level consistency claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that enforcing instance-level, rather than pixel-level, consistency is the right design principle for diffusion-based 3D style transfer. Its pipeline has two stages: (1) stylized key views are generated by a latent diffusion model conditioned on a style image via IP-Adapter and on depth via ControlNet, with a Cross-View Style Alignment module that lets features from different key views attend to each other in the last upsampling block; (2) the stylized key views are transferred to 3D Gaussians by grouping pixels according to Gaussian Grouping identity encodings, matching groups between training views and key views, and minimizing the cosine distance between each

Load-bearing premise

The pipeline assumes that the identity encodings from Gaussian Grouping segment the same instance consistently across all training and key views, so that group matching always pairs the same objects; if those groupings are noisy or viewpoint-dependent, the instance-level style transfer will match the wrong regions and break structure preservation.

Editorial extensions

If this is right

  • If the pipeline works as claimed, 3D style transfer no longer needs strict pixel-level multi-view consistency from the diffusion model; instance-level consistency is enough for clean 3D stylization.
  • Stylized scenes would keep distinct objects visually separable, so a stylized truck, tree, or background region can receive different artistic treatments without bleeding into one another.
  • The two-stage design runs in about 20 minutes of training and renders at 118 FPS, making iterative high-quality 3D stylization practical on a single GPU.
  • The CVSA placement finding gives a concrete recipe for other multi-view generation and editing tasks: inject cross-view attention late in the decoder for semantic consistency without sacrificing detail.
  • The method extends beyond forward-facing captures to 360-degree scenes, where multi-view consistency is hardest.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct consequence the authors leave implicit is that the pipeline's success is bounded by segmentation quality: if Gaussian Grouping's identity encodings are unstable under occlusion or repeated similar objects, the group-matching map will pair wrong regions and the stylization will inherit that error.
  • The cross-view attention module is not specific to style transfer; it could be lifted into any multiview diffusion pipeline, such as coherent video editing or multi-image generation, wherever instance-level consistency matters more than pixel alignment.
  • Because the style loss operates per group with hard nearest-neighbor matching, it could be extended to soft or probabilistic matching to handle segmentation uncertainty.
  • A sharper validation than global LPIPS/RMSE would be per-instance style statistics: measuring whether each object's stylized appearance in novel views matches its appearance in the stylized key views.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes SSGaussian, a two-stage 3D style transfer pipeline for 3D Gaussian Splatting scenes. In the first stage, key views are stylized by a pretrained diffusion model augmented with IP-Adapter, ControlNet depth conditioning, and a Cross-View Style Alignment (CVSA) module that injects cross-view attention into the last upsampling block of the UNet. In the second stage, an Instance-level Style Transfer (IST) loss uses Identity Encoding parameters from Gaussian Grouping to establish group correspondences between training views and stylized key views, then performs localized nearest-neighbor feature matching in VGG space. The method is evaluated on LLFF and Tanks and Temples scenes against ARF, StyleGaussian, and G-Style, with additional comparison to video style transfer methods, a user study, and speed measurements. The abstract and conclusion claim significant improvement over state-of-the-art methods.

Significance. The technical integration is sensible and relevant: the two-stage decomposition, the use of diffusion priors with instance-aware Gaussian grouping, and the group-matching-based style loss are all reasonable design choices that address a genuine limitation of prior 3D style transfer methods. The reported training time (20 minutes) and real-time rendering speed (118 FPS) are practically attractive. The paper does not contain a hidden circularity of the kind where a prediction is fitted to an input; it is an optimization pipeline. However, the central claim of 'significantly outperforming' state-of-the-art methods rests on quantitative evidence that is currently too thin: small aggregate differences, no error bars or significance tests, and a self-consistency metric that may reward conservative stylization. If the quantitative validation is strengthened, the method would be a useful contribution; in its present form the evidence supports a novel pipeline but not the strong superiority claim.

major comments (4)
  1. [Section IV-B (Tables I, II; abstract and conclusion)] The claim to 'significantly outperform' is not supported by the reported numbers. Differences are small: short-range LPIPS 0.031 vs 0.035 (G-Style) and long-range 0.073 vs 0.077 (StyleGaussian); content loss 2.298 vs 2.300 (StyleGaussian). The paper provides no variance, per-scene breakdowns, number of independent runs, or statistical significance tests. Table IV's user study also lacks significance testing (36.7% vs 30.0% on structural integrity is 11 vs 9 of 30 participants). The manuscript itself notes that no standard quantitative metric exists; under these conditions, the 'significantly outperforms' wording in the abstract and conclusion is overreach. Provide confidence intervals and significance tests, or temper the claim.
  2. [Section IV-B, quantitative consistency metric] The consistency metric (warping one view to another with optical flow [53] and softmax splatting [54], then masked LPIPS/RMSE) only measures multi-view self-consistency. A method that applies little stylization and leaves the already 3D-consistent renderings almost unchanged can score well on this metric. Thus Table I alone cannot establish style-transfer quality. The authors should pair consistency results with a direct style-fidelity measure on novel views (e.g., style statistics, CLIP-score, or per-view style loss) and a content-preservation measure, plus per-scene results. Table II partially addresses this, but its content and Gram-matrix losses are aggregate and lack the same statistical support as Table I.
  3. [Section III-C, Eqs. (6)-(8)] The IST loss assumes that Gaussian Grouping's Identity Encodings produce reliable, discriminative instance groupings that are consistent across the sampled training view and the stylized key views. This is a load-bearing assumption: if identity encodings are noisy or inconsistent, group matching M maps a local region to the wrong instance in the key views, and the nearest-neighbor loss will pull style features from unrelated regions, directly harming structure preservation. The manuscript offers no quantitative validation of the segmentation/group-matching quality (e.g., mask IoU, matching accuracy, or failure cases). Please add an analysis or an ablation with oracle/alternate grouping to support this core assumption.
  4. [Section IV-D, Figs. 7-8] The ablations for both proposed modules are qualitative only. Since CVSA placement is justified by 'empirical analysis' in Fig. 3 with no quantitative comparison, and IST is claimed to reduce blur and artifacts without numbers, the reader cannot assess how much each component contributes. Add a quantitative ablation table using the same metrics as Tables I-II (consistency, content/style losses, or a perceptual metric) with significance where possible.
minor comments (5)
  1. [Eq. (7)] The notation 'SK j=1 yj' is likely a typo for a union or sum over groups; please clarify. Also state explicitly what 'yi = ∅' means when the stylized key views do not contain group i.
  2. [Section III-A and III-C] Define K clearly as the total number of groups and explain the linear layer f: Eq. (6) mentions 'restore the feature dimension back to K' while the Identity Encoding has length 16. The relationship between the 16-dim encoding and the K-way softmax is not fully specified.
  3. [Section IV-B (Table II)] Specify which VGG layers are used for content and style losses, how the losses are normalized, and whether the values are averaged over all novel views or a fixed set. Without this, the cross-method comparison in Table II is hard to interpret.
  4. [Section IV-C, Fig. 6] Describe how the video style transfer baselines are applied to multi-view sequences and whether temporal consistency is quantified or only shown qualitatively. The current sentence 'SSGaussian achieves superior performance' is not backed by a metric in this comparison.
  5. [Figure 2] The diagram labels 'Style Control', 'Depth Control', and 'IP-Adapter' are dense; consider making the data flow of the style image, depth map, and identity render clearer, especially in the Stage 2 group-matching branch.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SSGaussian is an optimization pipeline evaluated on external metrics; no claimed prediction reduces to a fitted input.

full rationale

The paper's derivation chain is a construction pipeline, not a predictive theory. Stage 1 (diffusion-based key-view stylization with CVSA) and Stage 2 (Instance-level Style Transfer via Eq. 8) are optimization procedures built on externally published components (3DGS, Gaussian Grouping, DDIM inversion, ControlNet, IP-Adapter). The central loss, Eq. 8, is a nearest-neighbor cosine feature-matching loss; the quantitative evaluation in Table II uses Gram-matrix style loss and VGG content loss from [31], which are not the same functional and are not optimized by the pipeline. Table I's consistency metrics are computed with optical-flow warping and softmax splatting, an independent protocol borrowed from prior work [21], [22]. No parameter is fitted to a subset of the evaluation data and then reported as a prediction, and no load-bearing premise is justified solely by a self-citation. The abstract's 'significantly outperforms' claim rests on small numerical margins without significance tests, but that is an evidentiary weakness, not circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physics-like entities; its novel components are software modules (CVSA, IST) built from existing components. The main external dependencies are the pretrained diffusion model, IP-Adapter, ControlNet, and Gaussian Grouping, which carry their own assumptions and limitations.

free parameters (1)
  • Identity encoding dimension = 16
    Chosen as a compact vector length to distinguish objects in the scene; no ablation or sensitivity analysis is provided for this dimension.
assumptions (4)
  • domain assumption Gaussian Grouping produces accurate and view-consistent instance segmentation
    The entire second stage relies on Identity Encoding parameters (Eq. 6) and the group matching mapping M (Eq. 7) to associate the same instance across different viewpoints. If the identities are noisy, the style transfer will be incorrect.
  • domain assumption Pretrained diffusion models with IP-Adapter and ControlNet can transfer style while preserving content and structure
    Stage 1 assumes these modules will yield stylized key views that retain enough content fidelity for subsequent 3D transfer. This is an empirical assumption about the capability of the pretrained models.
  • standard math VGG features are a reliable perceptual space for style and content matching
    The style loss (Eq. 8) uses cosine distance in a VGG feature space, an established but empirical choice from prior neural style transfer literature.
  • ad hoc to paper Cross-view attention at the last upsampling block of the UNet provides the best trade-off
    The placement is justified only by empirical observation shown in Figure 3, not by a formal analysis. The paper states that earlier blocks lead to insufficient semantic alignment, but gives no quantitative comparison of block placements.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer." pith.science (2026). https://pith.science/paper/YMHLKOHB

@misc{pith2026250904379,
  author       = {Pith},
  title        = {Pith review of: SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YMHLKOHB}},
  note         = {Machine review of arXiv:2509.04379}
}
read the original abstract

Recent advancements in neural representations, such as Neural Radiance Fields and 3D Gaussian Splatting, have increased interest in applying style transfer to 3D scenes. While existing methods can transfer style patterns onto 3D-consistent neural representations, they struggle to effectively extract and transfer high-level style semantics from the reference style image. Additionally, the stylized results often lack structural clarity and separation, making it difficult to distinguish between different instances or objects within the 3D scene. To address these limitations, we propose a novel 3D style transfer pipeline that effectively integrates prior knowledge from pretrained 2D diffusion models. Our pipeline consists of two key stages: First, we leverage diffusion priors to generate stylized renderings of key viewpoints. Then, we transfer the stylized key views onto the 3D representation. This process incorporates two innovative designs. The first is cross-view style alignment, which inserts cross-view attention into the last upsampling block of the UNet, allowing feature interactions across multiple key views. This ensures that the diffusion model generates stylized key views that maintain both style fidelity and instance-level consistency. The second is instance-level style transfer, which effectively leverages instance-level consistency across stylized key views and transfers it onto the 3D representation. This results in a more structured, visually coherent, and artistically enriched stylization. Extensive qualitative and quantitative experiments demonstrate that our 3D style transfer pipeline significantly outperforms state-of-the-art methods across a wide range of scenes, from forward-facing to challenging 360-degree environments. Visit our project page https://jm-xu.github.io/SSGaussian for immersive visualization.

Figures

Figures reproduced from arXiv: 2509.04379 by the authors.

Figure 1
Figure 1. Pipeline of SSGaussian. We begin by reconstructing the scene using a 3D Gaussian Splatting representation. Next, we select key viewpoints and render their corresponding RGB images. Then, given a reference style image, we apply a pretrained diffusion model enhanced with our proposed Cross-View Style Alignment module to generate consistent stylized results for the key views. Finally, we achieve full 3DGS stylization b… view at source ↗
Figure 2
Figure 2. Two Stage Stylization. We decompose the 3D style transfer task into two sequential stages: the stylization of key views and the stylization of the 3D Gaussian Splatting (3DGS) representation based on those stylized key views. In Stage 1, given a style reference image along with RGB and depth images rendered from the 3DGS, we design a diffusion model to effectively transfer style semantics to the selected key viewpoi… view at source ↗
Figure 3
Figure 3. Impact of the Cross-View Style Alignment module across different blocks of the denoising U-Net. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison on LLFF dataset. We compare our SSGaussian against the state-of-the-art methods on the task of stylizing forward-facing scenes from a reference style image. multi-view consistency, we extend this formulation by in￾troducing cross-view attention, …
Figure 5
Figure 5. Figure 5: Qualitative comparison on Tanks and Temples dataset. We compare our SSGaussian against the state-of-the-art methods on the task of stylizing 360-degree scenes from a reference style image. within the scene while maintaining computational efficiency. Similar to equation…
Figure 6
Figure 6. Figure 6: Comparisons with Video Style Transfer Methods. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Ablation experiment on the proposed Cross-View Style Alignment (CVSA). (A) and (B) show different scenes, with two views rendered for each. Our CVSA module significantly improves multi-view consistency in both large-scale style semantics and fine-grained style details.…
Figure 8
Figure 8. Figure 8: Ablation experiment on the proposed Instance-level Style Transfer (IST). (A) and (B) show different scenes, with two views rendered for each. Our IST approach enables high-quality 3D style transfer by effectively reducing blurriness and mitigating visual artifacts. ing…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

57 extracted references · 46 canonical work pages

  1. [53]

    Raft: Recurrent all-pairs field transforms for op- tical flow,

    Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for op- tical flow,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 402–419

    work page 2020

  2. [54]

    Softmax splatting for video frame interpolation,

    S. Niklaus and F. Liu, “Softmax splatting for video frame interpolation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5437–5446

    work page 2020

  3. [1]

    Sgdm: An adaptive style- guided diffusion model for personalized text to image generation,

    Y . Xu, X. Xu, H. Gao, and F. Xiao, “Sgdm: An adaptive style- guided diffusion model for personalized text to image generation,” IEEE Transactions on Multimedia , vol. 26, pp. 9804–9813, 2024

    work page 2024

  4. [2]

    Mmginpainting: Multi-modality guided image inpainting based on diffusion models,

    C. Zhang, W. Yang, X. Li, and H. Han, “Mmginpainting: Multi-modality guided image inpainting based on diffusion models,” IEEE Transactions on Multimedia , vol. 26, pp. 8811–8823, 2024

    work page 2024

  5. [3]

    Animediff: Customized image generation of anime characters using diffusion model,

    Y . Jiang, Q. Liu, D. Chen, L. Yuan, and Y . Fu, “Animediff: Customized image generation of anime characters using diffusion model,” IEEE Transactions on Multimedia , vol. 26, pp. 10 559–10 572, 2024

    work page 2024

  6. [4]

    Videodreamer: Customized multi-subject text-to-video generation with disen-mix finetuning on language-video foundation models,

    H. Chen, X. Wang, G. Zeng, Y . Zhang, Y . Zhou, F. Han, Y . Wu, and W. Zhu, “Videodreamer: Customized multi-subject text-to-video generation with disen-mix finetuning on language-video foundation models,” IEEE Transactions on Multimedia , vol. 27, pp. 2875–2885, 2025

    work page 2025

  7. [5]

    High- resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High- resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

    work page 2022

  8. [6]

    Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models,

    H. Ye, J. Zhang, S. Liu, X. Han, and W. Yang, “Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models,” arXiv preprint arXiv:2308.06721 , 2023

    arXiv 2023

Show all 57 references
  1. [7]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021

    2021

  2. [8]

    3d gaussian splatting for real-time radiance field rendering

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering.” ACM Trans. Graph. , vol. 42, no. 4, pp. 139–1, 2023

    2023

  3. [9]

    Dreamfusion: Text- to-3d using 2d diffusion,

    B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text- to-3d using 2d diffusion,” in The Eleventh International Conference on Learning Representations, 2023

    2023

  4. [10]

    Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors,

    G. Qian, J. Mai, A. Hamdi, J. Ren, A. Siarohin, B. Li, H.-Y . Lee, I. Skorokhodov, P. Wonka, S. Tulyakovet al., “Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors,” in The Twelfth International Conference on Learning Representations , 2024

    2024

  5. [11]

    Realfusion: 360deg reconstruction of any object from a single image,

    L. Melas-Kyriazi, I. Laina, C. Rupprecht, and A. Vedaldi, “Realfusion: 360deg reconstruction of any object from a single image,” in Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 8446–8455

    2023

  6. [12]

    Instruct-nerf2nerf: Editing 3d scenes with instructions,

    A. Haque, M. Tancik, A. A. Efros, A. Holynski, and A. Kanazawa, “Instruct-nerf2nerf: Editing 3d scenes with instructions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 740–19 750. JOURNAL OF LATEX CLASS FILES, VOL. XX, NO. XX, XX XXXX 10

    2023

  7. [13]

    Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,

    Y . Chen, Z. Chen, C. Zhang, F. Wang, X. Yang, Y . Wang, Z. Cai, L. Yang, H. Liu, and G. Lin, “Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 476–21 485

    2024

  8. [14]

    Instantstyle- gaussian: Efficient art style transfer with 3d gaussian splatting,

    X.-Y . Yu, J.-X. Yu, L.-B. Zhou, Y . Wei, and L.-L. Ou, “Instantstyle- gaussian: Efficient art style transfer with 3d gaussian splatting,” arXiv preprint arXiv:2408.04249, 2024

    2024 arXiv

  9. [15]

    Instructpix2pix: Learning to follow image editing instructions,

    T. Brooks, A. Holynski, and A. A. Efros, “Instructpix2pix: Learning to follow image editing instructions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 392–18 402

    2023

  10. [16]

    Learning to stylize novel views,

    H.-P. Huang, H.-Y . Tseng, S. Saini, M. Singh, and M.-H. Yang, “Learning to stylize novel views,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 869–13 878

    2021

  11. [17]

    3d photo stylization: Learning to generate stylized novel views from a single image,

    F. Mu, J. Wang, Y . Wu, and Y . Li, “3d photo stylization: Learning to generate stylized novel views from a single image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 273–16 282

    2022

  12. [18]

    3dstylenet: Creating 3d shapes with geometric and texture style variations,

    K. Yin, J. Gao, M. Shugrina, S. Khamis, and S. Fidler, “3dstylenet: Creating 3d shapes with geometric and texture style variations,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 12 456–12 465

    2021

  13. [19]

    Text2mesh: Text-driven neural stylization for meshes,

    O. Michel, R. Bar-On, R. Liu, S. Benaim, and R. Hanocka, “Text2mesh: Text-driven neural stylization for meshes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 492–13 502

    2022

  14. [20]

    Arf: Artistic radiance fields,

    K. Zhang, N. Kolkin, S. Bi, F. Luan, Z. Xu, E. Shechtman, and N. Snavely, “Arf: Artistic radiance fields,” in European Conference on Computer Vision. Springer, 2022, pp. 717–733

    2022

  15. [21]

    Stylerf: Zero-shot 3d style transfer of neural radiance fields,

    K. Liu, F. Zhan, Y . Chen, J. Zhang, Y . Yu, A. El Saddik, S. Lu, and E. P. Xing, “Stylerf: Zero-shot 3d style transfer of neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 8338–8348

    2023

  16. [22]

    Stylegaussian: Instant 3d style transfer with gaussian splatting,

    K. Liu, F. Zhan, M. Xu, C. Theobalt, L. Shao, and S. Lu, “Stylegaussian: Instant 3d style transfer with gaussian splatting,” in SIGGRAPH Asia 2024 Technical Communications , 2024, pp. 1–4

    2024

  17. [23]

    G-style: Stylized gaussian splatting,

    ´A. S. Kov ´acs, P. Hermosilla, and R. G. Raidou, “G-style: Stylized gaussian splatting,” in Computer Graphics F orum, vol. 43, no. 7. Wiley Online Library, 2024, p. e15259

    2024

  18. [24]

    Gaussian grouping: Segment and edit anything in 3d scenes,

    M. Ye, M. Danelljan, F. Yu, and L. Ke, “Gaussian grouping: Segment and edit anything in 3d scenes,” in European Conference on Computer Vision. Springer, 2024, pp. 162–179

    2024

  19. [25]

    U-net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 ...

    2015

  20. [26]

    Neural style palette: A multimodal and interactive style transfer from a single style image,

    J. J. Virtusio, J. J. M. Ople, D. S. Tan, M. Tanveer, N. Kumar, and K.-L. Hua, “Neural style palette: A multimodal and interactive style transfer from a single style image,” IEEE Transactions on Multimedia , vol. 23, pp. 2245–2258, 2021

    2021

  21. [27]

    Structure-guided arbitrary style transfer for artistic image and video,

    S. Liu and T. Zhu, “Structure-guided arbitrary style transfer for artistic image and video,” IEEE Transactions on Multimedia , vol. 24, pp. 1299– 1312, 2022

    2022

  22. [28]

    Texture preserving photo style transfer network,

    H. Mun, G.-J. Yoon, J. Song, and S. M. Yoon, “Texture preserving photo style transfer network,” IEEE Transactions on Multimedia , vol. 24, pp. 3823–3834, 2022

    2022

  23. [29]

    Lccstyle: Arbitrary style transfer with low computational complexity,

    Y . Huang, M. Jing, J. Zhou, Y . Liu, and Y . Fan, “Lccstyle: Arbitrary style transfer with low computational complexity,” IEEE Transactions on Multimedia , vol. 25, pp. 501–514, 2023

    2023

  24. [30]

    Towards high-quality photorealistic image style transfer,

    H. Ding, H. Zhang, G. Fu, C. Jiang, F. Luo, C. Xiao, and M. Xu, “Towards high-quality photorealistic image style transfer,” IEEE Trans- actions on Multimedia , vol. 26, pp. 9892–9905, 2024

    2024

  25. [31]

    Image style transfer using convolutional neural networks,

    L. A. Gatys, A. S. Ecker, and M. Bethge, “Image style transfer using convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2414–2423

    2016

  26. [32]

    Arbitrary style transfer in real-time with adaptive instance normalization,

    X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE interna- tional conference on computer vision , 2017, pp. 1501–1510

    2017

  27. [33]

    Neural style transfer: A review,

    Y . Jing, Y . Yang, Z. Feng, J. Ye, Y . Yu, and M. Song, “Neural style transfer: A review,” IEEE transactions on visualization and computer graphics, vol. 26, no. 11, pp. 3365–3385, 2019

    2019

  28. [34]

    Styledrop: text-to-image generation in any style,

    K. Sohn, N. Ruiz, K. Lee, D. C. Chin, I. Blok, H. Chang, J. Barber, L. Jiang, G. Entis, Y . Li et al. , “Styledrop: text-to-image generation in any style,” in Proceedings of the 37th International Conference on Neural Information Processing Systems , 2023, pp. 66 860–66 889

    2023

  29. [35]

    Styleadapter: A unified stylized image generation model,

    Z. Wang, X. Wang, L. Xie, Z. Qi, Y . Shan, W. Wang, and P. Luo, “Styleadapter: A unified stylized image generation model,” International Journal of Computer Vision , vol. 133, no. 4, pp. 1894–1911, 2025

    1911

  30. [36]

    Style aligned image generation via shared attention,

    A. Hertz, A. V oynov, S. Fruchter, and D. Cohen-Or, “Style aligned image generation via shared attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4775–4785

    2024

  31. [37]

    Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer,

    J. Chung, S. Hyun, and J.-P. Heo, “Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8795–8805

    2024

  32. [38]

    Instantstyle: Free lunch towards style-preserving in text-to-image gen- eration,

    H. Wang, M. Spinelli, Q. Wang, X. Bai, Z. Qin, and A. Chen, “Instantstyle: Free lunch towards style-preserving in text-to-image gen- eration,” arXiv preprint arXiv:2404.02733 , 2024

    2024 arXiv

  33. [39]

    Vica-nerf: View-consistency-aware 3d editing of neural radiance fields,

    J. Dong and Y .-X. Wang, “Vica-nerf: View-consistency-aware 3d editing of neural radiance fields,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  34. [40]

    Dreameditor: Text- driven 3d scene editing with neural fields,

    J. Zhuang, C. Wang, L. Lin, L. Liu, and G. Li, “Dreameditor: Text- driven 3d scene editing with neural fields,” in SIGGRAPH Asia 2023 Conference Papers, 2023, pp. 1–10

    2023

  35. [41]

    Gaussctrl: Multi-view consistent text-driven 3d gaussian splatting edit- ing,

    J. Wu, J.-W. Bian, X. Li, G. Wang, I. Reid, P. Torr, and V . A. Prisacariu, “Gaussctrl: Multi-view consistent text-driven 3d gaussian splatting edit- ing,” in European Conference on Computer Vision . Springer, 2024, pp. 55–71

    2024

  36. [42]

    Zero-shot text-driven dynamic neural radiance fields stylization,

    W. Liang, H. Xu, W. Gan, and W. Kang, “Zero-shot text-driven dynamic neural radiance fields stylization,” IEEE Transactions on Multimedia, pp. 1–14, 2025

    2025

  37. [43]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PmLR, 2021, pp. 8748–8763

    2021

  38. [44]

    Locally stylized neural radi- ance fields,

    H.-W. Pang, B.-S. Hua, and S.-K. Yeung, “Locally stylized neural radi- ance fields,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, 2023, pp. 307–316

    2023

  39. [45]

    Segment anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Loet al., “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4015–4026

    2023

  40. [46]

    Tracking anything with decoupled video segmentation,

    H. K. Cheng, S. W. Oh, B. Price, A. Schwing, and J.-Y . Lee, “Tracking anything with decoupled video segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 1316–1326

    2023

  41. [47]

    Layer normalization,

    J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450, 2016

    2016 arXiv

  42. [48]

    Adding conditional control to text-to-image diffusion models,

    L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 3836–3847

    2023

  43. [49]

    Denoising diffusion implicit models,

    J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2021

    2021

  44. [50]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  45. [51]

    Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,

    B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ra- mamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Transactions on Graphics (ToG), vol. 38, no. 4, pp. 1–14, 2019

    2019

  46. [52]

    Tanks and temples: Benchmarking large-scale scene reconstruction,

    A. Knapitsch, J. Park, Q.-Y . Zhou, and V . Koltun, “Tanks and temples: Benchmarking large-scale scene reconstruction,” ACM Transactions on Graphics (ToG), vol. 36, no. 4, pp. 1–13, 2017

    2017

  47. [55]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595

    2018

  48. [56]

    Anyv2v: A tuning- free framework for any video-to-video editing tasks,

    M. Ku, C. Wei, W. Ren, H. Yang, and W. Chen, “Anyv2v: A tuning- free framework for any video-to-video editing tasks,” Transactions on Machine Learning Research , 2024

    2024

  49. [57]

    Univst: A unified framework for training-free localized video style transfer,

    Q. Song, M. Lin, W. Zhan, S. Yan, L. Cao, and R. Ji, “Univst: A unified framework for training-free localized video style transfer,”arXiv preprint arXiv:2410.20084, 2024

    2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.