Pith. sign in

REVIEW 4 major objections 6 minor 18 references

AnyStyle stylizes 3D scenes from text or reference images in a single forward pass, without retraining the geometry network.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-03 04:45 UTC pith:CZN2MLSP

load-bearing objection Useful, honestly-scoped extension of feed-forward 3D stylization; the multimodal conditioning is real but the evaluation is thinner than the claims. the 4 major comments →

arxiv 2602.04043 v2 pith:CZN2MLSP submitted 2026-02-03 cs.CV

AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting

Joanna Kaleta , Bartosz \'Swirta , Kacper Kania , Tomasz Trzci\'nski , Przemys{\l}aw Spurek , Marek Kowalski This is my paper
classification cs.CV
keywords 3D Gaussian Splattingstyle transferzero-shot stylizationmultimodal conditioningfeed-forward reconstructionpose-free reconstructionstyle interpolationzero-convolution injection
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces AnyStyle, a feed-forward framework that takes unposed photos of a scene and a style signal — a natural-language description or a reference image — and outputs a stylized 3D Gaussian scene in one pass, in under 0.1 seconds per content image. Its central claim is that artistic appearance can be decoupled from geometry: a frozen reconstruction backbone recovers structure and camera poses, while a lightweight trainable style branch alters only surface appearance. Because the style branch attaches to any transformer-based reconstruction model through zero-initialized convolution adapters, the approach avoids the from-scratch retraining required by earlier feed-forward stylization methods. The paper reports the best ArtFID among feed-forward methods and a statistically significant preference in a user study, plus smooth interpolation between text and image styles.

Core claim

AnyStyle's core claim is that zero-shot stylization of 3D scenes can be achieved by injecting a style embedding into selected token representations of a frozen feed-forward 3D reconstruction transformer, leaving geometry untouched. A copied aggregator and Gaussian head are fine-tuned in parallel, and a Gaussian adapter merges the frozen geometric parameters with the stylized appearance parameters before differentiable rendering. Alternating text-conditioned and image-conditioned batches trains a single injector to handle both modalities and enables continuous interpolation between them in the shared embedding space. The paper claims this yields superior stylization quality among feed-forward

What carries the argument

The Style Injector: a lightweight module that projects a style embedding from a shared text–image embedding space into the token dimension with an MLP and adds it to token features through a zero-initialized 1x1 convolution. At initialization the adapter outputs zero, so the network is identical to the pretrained backbone; during training it gradually learns to modulate appearance. Placed at selected aggregator layers and on tokens entering the copied Gaussian head, it carries the entire stylistic control while the frozen backbone provides geometry.

Load-bearing premise

The method assumes that after alternating text- and image-conditioned training, one shared style injector treats text descriptions and reference images as interchangeable enough to give reliable zero-shot text styling and smooth interpolation; the paper acknowledges a residual modality gap between the two.

What would settle it

Take a set of style images and their text descriptions, and measure the cosine similarity of their embeddings in the space used for conditioning; if the average text-image similarity is close to the average image-image similarity of unrelated styles, the shared-space premise is weak, and alternating batch training would be compensating for a fundamental misalignment rather than bridging a small gap.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • Users can restyle a reconstructed 3D scene interactively by editing text prompts, without re-optimizing the scene.
  • A user can start from a reference image and refine the result with textual cues such as 'softer color tones' or 'expressive brushwork'.
  • Future transformer-based 3D reconstruction models can gain stylization by attaching a style branch, rather than retraining from scratch.
  • The reported sub-0.1-second per-image inference time makes interactive or on-device 3D stylization practical.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the residual modality gap is larger than the paper suggests, text-conditioned stylization may improve more slowly than image-conditioned with more training data; a direct text-to-image similarity audit on the embedding space would reveal the ceiling.
  • The interpolation capability implies the style injector encodes a roughly linear style manifold; probing it with attribute-specific text edits could turn this into a controllable style editor for 3D content.
  • Since the training styles come from WikiArt, out-of-distribution aesthetics will likely need test-time embedding optimization or broader style data — the paper's own failure cases hint at this boundary.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. AnyStyle extends AnySplat with a dual-branch architecture: a frozen geometric backbone and a fine-tuned style branch consisting of a copied aggregator and Gaussian head, conditioned on Long-CLIP embeddings of a style image or text via zero-initialized convolutional style injectors. The two branches are combined by a Gaussian adapter and rendered by differentiable splatting. Training uses VGG content/style losses, global and patch CLIP directional losses. The paper reports ArtScore/ArtFID on four scenes (Train, Truck, M60, Garden), ablations, a user study against Styl3R, qualitative interpolation, multi-view consistency, and supplementary analysis including explicit limitation and failure cases.

Significance. Single-pass stylization without per-scene optimization is practically valuable, and the zero-convolution injection on a frozen pretrained backbone is an elegant way to avoid retraining geometry. The paper is unusually honest about metric caveats and failure cases, and it ships code and detailed training settings. If the multimodal bridge is genuinely effective, the method would be a clear improvement over image-only feed-forward stylizers. However, the current evidence base is too narrow and lacks a direct quantitative test of the modality-bridging and interpolation mechanisms, which are the core novelty.

major comments (4)
  1. [Section 3, 'Multimodal Style Conditioning', and Table 2] The central contribution is unified text/image conditioning and interpolation, but the only mitigation for the acknowledged Long-CLIP modality gap is alternating text/image batches. Table 2 shows text-conditioned ArtFID is consistently worse than image-conditioned on all four scenes (e.g., Train 24.41 vs 22.86; Truck 24.67 vs 22.95), and the interpolation results are only qualitative. This does not establish that the shared injector treats modalities as interchangeable or that intermediate embeddings give coherent intermediate styles. Please provide a quantitative test — e.g., matched text/image outputs compared by a style metric, CLIP directional similarity, or an interpolation consistency curve — or soften the multimodal/interpolation claims.
  2. [Section 4, Tables 2 and 3, user study] The main quantitative comparisons are single-run values on four scenes with no error bars or repeated-seed statistics; the claim of 'best ArtFID among feed-forward approaches' is therefore not statistically grounded. The user study is useful but only compares with Styl3R; Stylos, the other feed-forward baseline, is absent because code was unavailable. Given that the abstract claims superiority over 'existing state-of-the-art,' please report per-style distributions or confidence intervals, and either obtain Stylos results from official outputs or restrict the claim.
  3. [Section 3, Eq. (4), Eq. (11), and Supp. D] The training objective is underspecified. Eq. (11) lists only content, style, global CLIP, and patch CLIP losses, but Supp. D states a depth consistency loss with weight 0.1 that never appears in the objective; the depth loss is not defined. Also, Eq. (4) says ZeroConv is a 1x1 convolution, but it is applied to a token vector f in R^{df}; the tensor layout and whether the additive term is content-independent need clarification. Additionally, since L_style in Eq. (8) is defined against a style image S_I, the paper should state explicitly which image is used as the style target in text-conditioned batches.
  4. [Section 1 and Section 3, 'architecture-agnostic'] The architecture-agnostic claim is not demonstrated. Only AnySplat is used as the backbone; the 'could switch to DepthAnything 3' sentence in Section 3 is not an experiment, and DepthAnything 3 is a monocular depth estimator rather than a feed-forward 3D reconstruction model. The Head-only ablation shows injection can be applied outside attention, which supports modularity, but it does not show transfer to a different backbone. Please either add an experiment with a second backbone or qualify the claim from 'architecture-agnostic' to 'applicable to the AnySplat architecture and, by design, to other transformer-based backbones.'
minor comments (6)
  1. [Supp. E, Table 4] The supplementary discussion correctly notes that lower RMSE/LPIPS may reflect reduced color variation and that GT images can score worse. This should be stated in the main text wherever Table 4 is referenced; otherwise the table can be misread as evidence of consistency.
  2. [Section 4, user study] The p-values are reported only in Supplementary Section C; the main text should include at least the effect size or the test direction, since 800 responses per question can make trivially small effects statistically significant.
  3. [Section 3, training losses] Please define all symbols in the main text (K_content, K_style, N_patch, patch size) rather than only in Supp. D.
  4. [Throughout] Minor language and formatting issues: 'at two location' (Section 3), 'StylOS' vs 'Stylos' inconsistent, 'AnyStyletxt' spacing, and a duplicated Chung et al. 2024a/b reference.
  5. [Table 2 and Figures 3-8] Clarify which variant (full AnyStyle vs Head) is used in the qualitative figures and in the user study.
  6. [Training/evaluation metrics] Since the training objective includes a VGG-based style loss and ArtFID is a VGG-feature metric, the margin over methods not trained with this loss may partly reflect metric alignment; a reader would benefit from this being acknowledged.

Circularity Check

0 steps flagged

No significant circularity: AnyStyle's derivation is self-contained, with only a minor non-load-bearing self-citation and a VGG-loss/ArtFID metric overlap.

full rationale

I walked the claimed derivation chain: a frozen AnySplat backbone reconstructs geometry, a copied aggregator/Gaussian-head style branch is fine-tuned with zero-convolution style injection (Eqs. 4-6), and training uses content, VGG style, CLIP directional, and patch CLIP losses (Eqs. 7-11). No equation is defined in terms of a quantity it is later used to predict, and no fitted parameter is renamed as a prediction. The two areas that could look circular are minor. First, the training style loss (Eq. 8) directly matches VGG channel-wise mean/std between the rendered image and the reference style image, while the headline metric ArtFID is a VGG-feature-based style-distance; this is metric alignment, not a tautology, because evaluation uses held-out scenes and style images explicitly excluded from training (Section 4 and Section D), and a user study provides independent human-preference evidence. Second, the CLIP directional loss is attributed to Kwon & Ye 2022 and to Howil et al. 2025, the latter sharing a co-author (Spurek); however, the loss's functional form is given explicitly in the paper and the ablation shows CLIP losses alone are weak, so this self-citation is not load-bearing. The paper's own stated limitations, including the residual Long-CLIP modality gap and the supplementary admission that multi-view RMSE can improve merely because stylization reduces color variation, weaken some claims but are not circular. Overall, the central stylization and reconstruction results are supported by external benchmarks and an independent user study; no derivation reduces to its inputs by construction.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

AnyStyle introduces no new physical or natural entities; it adds trainable architectural components (Style Injector, style branch, Gaussian Adapter). The empirical results depend on a set of hand-set hyperparameters and on assumptions about CLIP cross-modal alignment, frozen-backbone geometry preservation, and transferability of ControlNet-style zero convolutions.

free parameters (4)
  • Loss weights (lambda_content=0.05, lambda_style=1.0, lambda_clip=2.0, lambda_patch_clip=4.0, depth=0.1) = 0.05 / 1.0 / 2.0 / 4.0 / 0.1
    Hand-chosen hyperparameters that directly determine stylization strength and geometry preservation; no sensitivity analysis is provided beyond removing full loss terms.
  • Style injection layer sets = Head: [4,11,17,23]; Aggregator: [0,4,11,17,23]
    Arbitrary selection of intermediate token layers for injection; the paper only ablates full vs head-only injection, not the choice of specific layers.
  • Patch CLIP crop count and size = 16 crops of size 64
    Chosen to enforce local style consistency; the number and size are not ablated.
  • Training schedule and learning rates = 90k iterations; Gaussian head/style injectors 1e-4; aggregator 0.3x backbone LR
    Standard optimization hyperparameters with no reported grid search; results may depend on them.
axioms (5)
  • domain assumption Long-CLIP text and image embeddings are aligned well enough that a single style injector, trained with alternating text/image batches, can handle both modalities and interpolation between them.
    The paper explicitly acknowledges a residual modality gap in Section 3 and treats batch alternation as a sufficient mitigation.
  • domain assumption A frozen AnySplat backbone produces sufficiently accurate geometry and camera poses, and fine-tuning only the copied aggregator/head in the style branch does not degrade that geometry.
    The dual-branch design in Section 3 relies on this decoupling; the paper provides qualitative evidence but no dedicated geometry-error evaluation against the frozen backbone.
  • domain assumption Zero-initialized convolutions, originally designed for ControlNet in 2D diffusion, preserve pretrained behavior and stabilize fine-tuning when applied to transformer token features in a 3D reconstruction network.
    The method transfers this mechanism from a different architecture family without formal justification; it is validated only empirically on the AnySplat backbone.
  • domain assumption VGG feature statistics, CLIP directional losses, ArtScore, and ArtFID are adequate proxies for stylization quality and style fidelity.
    The training objective and evaluation metrics share VGG-feature style statistics, so metric improvements may partly reflect direct optimization of the same features.
  • domain assumption Training on DL3DV-480P and WikiArt transfers zero-shot to the TnT and Mip-NeRF 360 test scenes and held-out WikiArt styles.
    The evaluation uses only 4 scenes and 50 held-out styles from the same artistic distribution as training; the authors acknowledge out-of-distribution degradation in Section G.

pith-pipeline@v1.3.0-alltime-deepseek · 17304 in / 9377 out tokens · 92734 ms · 2026-08-03T04:45:08.472484+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting." pith.science (2026). https://pith.science/paper/CZN2MLSP

@misc{pith2026260204043,
  author       = {Pith},
  title        = {Pith review of: AnyStyle: Single-Pass Multimodal Stylization for 3D Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CZN2MLSP}},
  note         = {Machine review of arXiv:2602.04043}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

The growing demand for rapid and scalable 3D asset creation has driven interest in feed-forward 3D reconstruction methods, with 3D Gaussian Splatting (3DGS) emerging as an effective scene representation. While recent approaches have demonstrated pose-free reconstruction from unposed image collections, integrating stylization or appearance control into such pipelines remains underexplored. Existing attempts largely rely on image-based conditioning, which limits both controllability and flexibility. In this work, we introduce AnyStyle, a feed-forward 3D reconstruction and stylization framework that enables pose-free, zero-shot stylization through multimodal conditioning. Our method supports both textual and visual style inputs, allowing users to control the scene appearance using natural language descriptions or reference images. We propose a modular stylization architecture that requires only minimal architectural modifications and can be integrated into existing feed-forward 3D reconstruction backbones. Experiments demonstrate that AnyStyle improves style controllability over prior feed-forward stylization methods while preserving high-quality geometric reconstruction. A user study further confirms that AnyStyle achieves superior stylization quality compared to an existing state-of-the-art approach. Repository: https://github.com/joaxkal/AnyStyle.

Figures

Figures reproduced from arXiv: 2602.04043 by Bartosz \'Swirta, Joanna Kaleta, Kacper Kania, Marek Kowalski, Przemys{\l}aw Spurek, Tomasz Trzci\'nski.

Figure 1
Figure 1. Figure 1: Teaser. Given a set of unposed input images and a style conditioning signal provided as either text or an image, our method generates a stylized 3D scene represented with 3D Gaussian Splats in a single forward pass. The reconstructed scene can be stylized in under 0.1 second per input content image. in teleconferencing, gaming, and film production. Building upon this foundation, AnySplat introduced a pose-… view at source ↗
Figure 2
Figure 2. Figure 2: Method overview. AnyStyle takes unposed content images of a scene together with an arbitrary style input (text or image) and produces a stylized 3D Gaussian representation from which novel stylized views can be rendered. The architecture follows a dual-branch design that decouples geometric reconstruction from appearance stylization. Content images are processed by a pretrained frozen backbone to recover g… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between AnyStyle and existing 3D style transfer methods with different architectural designs: feed-forward (purple), per-scene optimization (green), and hybrid approaches (blue). Our method achieves high-quality style transfer while faithfully preserving fine details (top row) as well as overall scene structure. All compared methods are conditioned on a style image. consider two location at whic… view at source ↗
Figure 4
Figure 4. Figure 4: Stylization using text prompts. We compare AnyStyle with ClipGaussian (Howil et al., 2025), which requires per-scene optimization (>20min). Despite using identical text prompts, Clip￾Gaussian introduces semantic artifacts from the style input. 4. Experiments We provide additional results including qualitative results on scenes from other dataset, training details, code, ex￾tended analysis, user study detai… view at source ↗
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Stylization with text prompts vs. images. We compare renderings conditioned either on a reference test style image or on a textual description generated by Mini-CPM-V4.5 for that image. Our method achieves coherent and plausible stylization across both modalities. Please note that due to the inherently lower amount of information encoded in text prompts and more ambiguous nature of natural language, text-b… view at source ↗
Figure 9
Figure 9. Figure 9: Ablation study. The first three columns show stylization conditioned on a reference style image. Full model produces richer and more vivid colors, while the Head variant yields slightly flatter appearances. Removing the style loss prevents accurate style learning, whereas removing the CLIP directional loss reduces color expressiveness. Finetuning all geometric features leads to visual inconsistencies (high… view at source ↗
Figure 7
Figure 7. Figure 7: Style interpolation. Thanks to the unified CLIP em￾bedding space, our method enables smooth interpolation between two style images, between an image and a text prompt, as well as between two text prompts. Compared to image-only style transfer methods, the latter capability provides greater control over the stylization process and supports an iterative workflow for refining specific stylistic attributes (hi… view at source ↗
Figure 8
Figure 8. Figure 8: Multi-view consistency of the stylization. By di￾rectly updating 3D Gaussian representation, our method ensures multi-view consistency of stylization while preserving fine-grained details across viewpoints. Floor dot (marked in green) and table discoloration (marked in red) remain consistent across views [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional qualitative results of our method on RE10K dataset. We present stylization with image and text prompt. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Additional qualitative results of our method on RE10K dataset. We present stylization with image and text prompt. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Additional qualitative results of our method on RE10K dataset. We present stylization with image and corresponding text prompt. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Additional qualitative results of our method on RE10K dataset. We present stylization with image and corresponding text prompt. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Additional qualitative results of our method on RE10K dataset. We present stylization with image and corresponding text prompt. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Additional qualitative results of our method on RE10K dataset. We present stylization with natural-language text prompt [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Additional qualitative results of our method on RE10K dataset. We present stylization with natural-language text prompt. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Visualisation of warping and consistency metric RMSE on Train scene. In both sub-figures, stylized images of a train (bottom rows) achieve lower RMSE then GT RGB images (top rows). 19 [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Visualisation of warping and consistency metric RMSE on Garden scene. In both sub-figures, stylized images of a garden (bottom rows) achieve lower RMSE then GT RGB images (top rows). 20 [PITH_FULL_IMAGE:figures/full_fig_p020_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: , [PITH_FULL_IMAGE:figures/full_fig_p023_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Example failure case when providing text as conditioning signal. While overall aesthetics my be pleasing, model fails to create realistic nighttime view. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_20.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

18 extracted references · 8 linked inside Pith

  1. [6]

    Liu, K., Zhan, F., Chen, Y ., Zhang, J., Yu, Y ., El Saddik, A., Lu, S., and Xing, E

    URL https://arxiv.org/abs/ 2509.26455. Liu, K., Zhan, F., Chen, Y ., Zhang, J., Yu, Y ., El Saddik, A., Lu, S., and Xing, E. P. StyleRF: Zero-Shot 3D Style Transfer of Neural Radiance Fields. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8338–8348,

    arXiv

  2. [7]

    StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting.arXiv preprint arXiv:2403.07807, 2024a

    Liu, K., Zhan, F., Xu, M., Theobalt, C., Shao, L., and Lu, S. StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting.arXiv preprint arXiv:2403.07807, 2024a. Liu, K., Zhan, F., Xu, M., Theobalt, C., Shao, L., and Lu, S. Stylegaussian: Instant 3d style transfer with gaussian splatting. InSIGGRAPH Asia 2024 Technical Communi- cations, pp. 1–4. 2024...

    Pith/arXiv arXiv 2024

  3. [10]

    URL https://doi.org/ 10.1145/3528223.3530107

    1145/3528223.3530107. URL https://doi.org/ 10.1145/3528223.3530107. Niklaus, S. and Liu, F. Softmax Splatting for Video Frame Interpolation. InIEEE Conference on Computer Vision and Pattern Recognition,

    arXiv

  4. [11]

    and Deng, J

    Teed, Z. and Deng, J. Raft: Recurrent all-pairs field trans- forms for optical flow. In Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.-M. (eds.),Computer Vision – ECCV 2020, pp. 402–419, Cham,

    2020

  5. [12]

    WikiArt: Visual Art Encyclopedia

    WikiArt. WikiArt: Visual Art Encyclopedia. https: //www.wikiart.org/. Accessed: 2025-10. Wright, M. and Ommer, B. ArtFID: Quantitative Evaluation of Neural Style Transfer.GCPR,

    2025

  6. [13]

    MiniCPM-V: A GPT-4V Level MLLM on Your Phone.arXiv preprint arXiv:2408.01800,

    Yao, Y ., Yu, T., Zhang, A., Wang, C., Cui, J., Zhu, H., Cai, T., Li, H., Zhao, W., He, Z., et al. MiniCPM-V: A GPT-4V Level MLLM on Your Phone.arXiv preprint arXiv:2408.01800,

    Pith/arXiv arXiv

  7. [14]

    No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images.arXiv preprint arXiv:2410.24207,

    Ye, B., Liu, S., Xu, H., Li, X., Pollefeys, M., Yang, M.-H., and Peng, S. No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images.arXiv preprint arXiv:2410.24207,

    Pith/arXiv arXiv

  8. [15]

    In- stantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting I.arXiv preprint arXiv:2408.04249,

    Yu, X.-Y ., Yu, J.-X., Zhou, L.-B., Wei, Y ., and Ou, L.-L. In- stantStyleGaussian: Efficient Art Style Transfer with 3D Gaussian Splatting I.arXiv preprint arXiv:2408.04249,

    Pith/arXiv arXiv

  9. [16]

    Long-CLIP: Unlocking the Long-Text Capability of CLIP

    Zhang, B., Zhang, P., Dong, X., Zang, Y ., and Wang, J. Long-CLIP: Unlocking the Long-Text Capability of CLIP. arXiv preprint arXiv:2403.15378, 2024a. Zhang, C., Xu, X., Wang, L., Dai, Z., and Yang, J. S2W AT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention. InProceedings of the AAAI conference on artificial intellig...

    Pith/arXiv arXiv

  10. [18]

    We then compute masked RMSE and LPIPS (Zhang et al.,

    and softmax splatting (Niklaus & Liu, 2020). We then compute masked RMSE and LPIPS (Zhang et al.,

    2020

  11. [64]

    Depth consistency loss weight is set to 0.1. Architecture choices.Tokens from the intermediate layers [4,11,17,23] are passed to the Gaussian Head, and style injection for Gaussian Head is performed for tokens from these layers. For the Aggregator, features are injected into the tokens before they enter the following layers:[0,4,11,17,23]. 18 AnyStyle E. ...

    2025

  12. [1932]

    H., Chen, D

    Lin, H., Chen, S., Liew, J. H., Chen, D. Y ., Li, Z., Shi, G., Feng, J., and Kang, B. Depth anything 3: recov- ering the visual space from any views.arXiv preprint arXiv:2511.10647,

    Pith/arXiv arXiv

  13. [2019]

    Style- booth: Image style editing with multimodal instruction

    Han, Z., Mao, C., Jiang, Z., Pan, Y ., and Zhang, J. Style- booth: Image style editing with multimodal instruction. InProceedings of the IEEE/CVF International Confer- ence on Computer Vision (ICCV) Workshops, pp. 1947– 1957,

    1947

  14. [2020]

    ISBN 978-3-030-58536-5

    Springer International Publishing. ISBN 978-3-030-58536-5. Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., and Novotny, D. VGGT: Visual Geometry Grounded Transformer. InProceedings of the Computer Vision and Pattern Recognition Conference, pp. 5294–5306, 2025a. Wang, P., Liu, X., and Liu, P. Styl3R: Instant 3D Stylized Reconstruction for Arbit...

    arXiv

  15. [2021]

    Smart, B., Zheng, C., Laina, I., and Prisacariu, V . A. Splatt3R: Zero-shot Gaussian Splatting from Uncali- brated Image Pairs.arXiv preprint arXiv:2408.13912,

    Pith/arXiv arXiv

  16. [2023]

    Liu, H., Huang, J., Lu, M., Saripalli, S., and Jiang, P

    URLhttps://arxiv.org/abs/2312.16256. Liu, H., Huang, J., Lu, M., Saripalli, S., and Jiang, P. Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting,

    Pith/arXiv arXiv

  17. [2024]

    Learning to Evaluate the Artness of AI-Generated Images.IEEE Transactions on Multimedia, 26:10731–10740, 2024a

    Chen, J., An, J., Lyu, H., Kanan, C., and Luo, J. Learning to Evaluate the Artness of AI-Generated Images.IEEE Transactions on Multimedia, 26:10731–10740, 2024a. doi: 10.1109/TMM.2024.3410672. Chen, Y ., Xu, H., Zheng, C., Zhuang, B., Pollefeys, M., Geiger, A., Cham, T.-J., and Cai, J. MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-view Images...

    arXiv 2024

  18. [2025]

    CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting.arXiv preprint arXiv:2505.22854,

    Howil, K., Borycki, P., Dziarmaga, T., Mazur, M., Spurek, P., et al. CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting.arXiv preprint arXiv:2505.22854,

    arXiv