Stylos: Multi-View 3D Stylization with Single-Forward Gaussian Splatting
Authors: Hanzhou Liu
Jia Huang
Mi Lu
Srikanth Saripalli
Peng Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Style Transfer
3D Gaussian Splatting
Single-forward Stylization
3D Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Stylos is a single-pass framework that performs geometry-aware, view-consistent 3D style transfer from unposed content images using a reference style image, without per-scene optimization or precomputed poses.

Abstract: We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achieving geometry-aware, view-consistent stylization that generalizes to unseen categories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention to preserve geometric fidelity, while style is injected via cross-attention to enforce visual consistency across views. With the addition of a voxel-based 3D style loss that aligns aggregated scene features to style statistics, Stylos enforces view-consistent stylization while maintaining geometric coherence. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of the proposed style-content fusion block, the voxel-level style loss, and the scalability of our framework from single view to large-scale multi-view settings. Our codes are available at https://github.com/HanzhouLiu/Stylos.

Published as a conference paper at ICLR 2026
STYLOS: MULTI-VIEW 3D STYLIZATION WITH GAUSSIAN SPLATTING
Hanzhou Liu" Texas A&M University
Jia Huang
Mi Lu
Srikanth Saripalli
Peng Jiang"
ABSTRACT
We present Stylos, single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on separate reference style image. Stylos synthesizes stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achiev- ing gewme,ry-aware, view-consistent stylization that generalizes to unseen cate- gories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention fidelity, while style is injected via cross-attention to enforce visual consistency across views. With the addition of voxel-based 3D style loss that aligns aggre- gated scene features to style statistics, Stylos enforces view-consistent stylization while maintaining geometric coherence. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of the proposed style-content fusion block, the voxel-level style loss, and the scalability of our framework from single view to large-scale multi- view settings. Our codes are available at
preserve geometric
INTRODUCTION
Image guided 3D stylization aims to preserve scene geometry and cross-view consistency while transferring the reference style. With the rise of immersive content, augmented and virtual real- ity, demand for this capability is growing. Nevertheless, achieving reliable 3D stylization remains challenging and continues to attract significant research attention (Chen et al. 2025).
Early attempts typically focus 3D representations such meshes, volumetric data, and point |020||uuo
clouds (Kato et 2018 Liu et al. 2018 Kim et al., 2019 2020 Guo et al. [2021] Cao et al. 2020).
The introduction of implicit neural radiance fields (NeRF) (Mildenhall et al. 2020), enables higher-
fidelity rendering, and subsequent works extended it to artistic stylization (Zhang et al. 2022 Huang
al.) 2022 -guyen-Phuoe et al., 2022 Bao al., 2022] Liu et al. 2023). but typically require costly per-scene optimization, constraining their generalization to unseen scenes. More recently,
3D Gaussian Splatting (3DGS) has emerged as promising explicit representation that combines
high reconstruction quality with real-time rendering efficiency (Kerbl et al., (023). Stylization ap- proaches built on 3DGS achieve efficient multi-view consistency (Liu et al. [024 Mei al. 2024]
Galerne et al. 2025). yet still struggle to generalize beyond scene-specific training.
In contrast, we introduce Stylos (meaning pens in French), a single-forward framework for 3D style transfer that eliminates the need for per-scene optimization and precomputed camera parameters, and generalizes effectively to novel categories, scenes, and styles. Stylos employs a shared Transformer backbone with two pathways: content and style images projected into shared feature space, where content retains self-attention for geometric reasoning, and style is injected via the Cross- Block modules that primarily composed of self-attention layer followed by cross-attention. Geometry-relater attributes, such as depth, camera intrinsics, and extrinsics, are derived from back- bone features, whereas style conditioning guides the prediction of color coefficients. These outputs are estimated through prediction heads that serve as the interface between feature space and the final Gaussian representation. A representative style loss function in 2D style transfer is based on fea- ture distribution alignment (Li et al. 2017 Huang & Belongic 2017 Jing et al.) 2019 Singh et al.)
Equal Contribution

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Published as a conference paper at ICLR 2026
2021). which operate on image-level statisties and do not explicitly enforce multi-vies structural consistency required for 3D stylization. To address this, we explore alternative objectives and pro- pose voxel-level 3D style loss that aligns aggregated scene features with style statistics, providing stronger view-consistent stylization while preserving geometric fidelity.
We evaluate Stylos across different scenarios, including category-level transfer and cross-scene gen- eralization. Our assessment spans challenging real-world benchmarks, where Stylos produces styl- ized renderings with high visual fidelity and consistent geometry. demonstrating robustness even in previously unseen environments. Our main contributions are threefold:
We propose shared-backbone design with two pathways: geometry predictions retain self-attention for geometric reasoning, while style is injected through cross-attention.
We introduce a voxel-level 3D style loss that aligns aggregated scene features with style statistics, enforeing cross-view coherence and geometry-aware stylization.
We develop Stylos, a fingleforwwardpass pipeline for 3D style transfer from unposed in- puts, scaling from single to hundreds of views with single style image, and achieving zero-shot generalization to unseen categories, scenes, and styles.
2 RELATED WORK
2.1 POSE-FREE 3D RECONSTRUCTION
3D reconstruction from unposed multi-view images or videos has drawn significant attention with
the development of feed-forward models, including NeRF-based methods (Smith al., 2023 Hong
et al., 2024) and 3DGS- based approaches (Li et al. [2024] Kim (225)). DUSI3R (Wang et al.)
2024).
a Transformer- from image pairs. Its extensions (Wang & Agapito 2024 Yang et al. 2025 Wang et al. 2025c
framework, advances this direction by enabling pointmap estimation directly
Cabon et al. 2025) further reduce reliance on global alignment procedures and support a varying number of imput views. VGGT (Wang et al., (025a), recent 3D foundation model, revolutionizes this line of work by jointly predicting camera parameters, depth, point maps, and tracks from one to hundreds of views in single forward pass, without any pose optimization. As follow-up to VGGT, AnySppat (Tiang et al. (025) introduces a rendering head and complements this design with novel feed-forward Gaussian splatting pipeline, enabling the prediction of Gaussian primitives along with depth and camera parameters from uncalibrated images.
2.2
3D STYLE TRANSFER AND LOSSES
3D stylization methods can be classified based geometric representations, such as mesh (Kato
jet al. 2018: Liu et al., 2018), volumetric data (Kim et al.) 2019 2020 Guo et al. 202T), point coulds (Cao et al. 2020 Huang et al. 202I). In addition, neural radiance fields (NeRF) (Mildenhall (ct al.) 2020), using an MLP to implicitly learn a static 3D scene, has inspired a range of styliza- tion methods based on stylized view supervision or auxiliary networks (Nguyen-Phuoc al. 2022
Zhang et al.) 2022 Chiang et al., 2022] Huang et al.] 2022). StylizedNeRF (Huang et al. 2022)
addresses the domain gap between style images and NeRF by introducing a mutual learning frame- work. StyleRF (Liu et al. (023) further enables zero-shot transfer by applying style transformations in radiance-field feature space. However, training NeRFs for scene reconstruction and stylization is
featurr relatively computationally expensive, making NeRe-based 3D stylization far from real-time.
More recently, 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023) has emerged as an efficient alter- native to NeRF for 3D scene modeling. Subsequent works refine 3DGS for efficient stylization (Mci et al. 2024, Kovács et al. 2024, Yu et al. 2024 Zhang et al. 2025 Galerne et al.) 2025 [in al. 2025 StyleGaussian (Liu et al., 2024) extends 3DGS with efficient feature rendering to enable
real-time zero-shot stylization. However, despite being significantly faster to optimize than NeRF, 3DGS-based approaches still require per-scene fitting, which poses a fundamental barrier to achiev- ing truly real-time 3D stylization. Sty13R (Wang et al. 20256), the closest contemporaneous related work to ours, introduces feedforward framework for instant 3D stylized reconstruction. While effective and efficient, StyI3R is primarily designed for 2-8 input views and does not specifically target strong multi-view consistency among the rendered stylization results.
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Published as a conference paper at ICLR 2026
Geometry Backbone
Pose Head
Poses
Patch Embedding DINOv2
VGGT
Depth Head
Depth
Blocks
Geo Head
Unproject
Content Frames
Style Image
(F.F.FF.)
Voxelize
KV
Cross
Blocks
Style Head
Gaussian Adapter
Las antermag Shaee dtlip wippyhiige
Style Aggregator
(Fe)
Depth Poses
[p.a,T,s.c)
Fusion
VGG
3D style
VGG
RGB
Trainable in stage
Trainable in both stages
Frozen in both stages
Rendering
Figure 1: Architecture overview. Given multi-view content inputs and style reference, Stylos enables instant 3D stylization without scene-specific training or post-optimization. Its core Cross- Block module facilitates style injection by integrating a cross-attention layer between self-attention and MLP. The proposed 3D style loss matches voxelized 3D features with 2D style statistics.
Underlying these architectures, style transfer objectives play crucial role. Classical style transfer relies on Gram- Gram-matre matrix correlations (Gatys et al., 2016), while channel-wise statistics such as mean and variance (Li et al. 2017) offer efficient alternatives, forming the basis of AdaIN (Huang & Be- longie 2017 Extensions video and multi-view stylization (Ruder al. 2016 Gupta al. 2017 Nguyen-Phuoc et al. 2022 Zhang et al. 2022) address temporal and cross-view consistency, and CLIP-based losses (Radford al. 2021 introduce semantic alignment with text or image prompts. However, most objectives remain inherenlly 2D and cannot guarantee consistency across complex 3D scenes. To this end, we extend statisties-basee style losses into voxel space, introducing 3D- aware objective that aligns feature distributions after multi-view fusion.
3 METHOD
We propose Stylos, a transformer-based framework for stylized 3D scene reconstruction. Given style reference image and predicts a set of stylized 3D Gaussian primitives together with camera parameters, enabling faithful reconstruction the observed scene while transferring the desired style. We first formulate the problem in Sec. 3.1 then present the network architecture in Sec. 3.2 and finally detail our training
content views (also referred
context views), Stylos
strategy Sec. 3.3 and the used style loss Sec. 3.4
3.1 PROBLEM FORMULATION
We aim to disentangle geometry and style within unified 3D scene representation. The input to Stylos consists of a set of N context views [ scene, with 1 and 4 I2 H x3 together with single style reference image E xxxx3 The context views provide geometric cues, while the style image specifies the desired aesthetic. Formally, Stylos defines a conditional mapping,
fe (1 5) + (G, (o.)
where the scene representation G = {pmm Cm } = is parameterized by M anisotropic 3D Gaus- sians. Each Gaussian has geometry attributes Pm = (Jm, Otys Tmm 8m), comprising 3D position E IR. and anisotropic scale 8m E IR3 as well style-dependent color embedding C.. E I (k++1)2 represented with spherical harmonics of degree k. addition, Stylos jointly predicts camera parameters {g: E N for each input view, leveraging pose cues estimated by the VGGT backbone.
Mm E 3 opacity E I orientation quaternion
li=1
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3.2 NETWORK ARCHITECTURE
Stylos employs geometry backbone that alternates frame-wise and global attention to process mul- tiple input views and infer geometry-related parameters such positions, scales, orientations, and opacities. To enable stylization, we introduce a dedicated conditioning branch, where the Style Ag- gregator fuses content and style features through cross-aad coss-atttntioon and predicts style-aware color em- beddings for each Gaussian. In this way, geometry remains derived solely from the backbone, while style representation conditioned on the style reference. We next detail the geometric backbone in Sec. 3.2.1. the style aggregation module in Sec. 3.2.2 and the prediction heads in Sec. 3,2.3
3.2.1
GEOMETRIC BACKBONE
The geometric backbone follows the internating-attention design of VGGT Wang et al. 2025a), which interleaves frame and global self-attention layers to capture both intra-frame structure and cross-view consistency. This component is kept unchanged to retain strong geometric reasoning, serving as the foundation which integrate style information.
3.2.2 STYLE AGGREGATOR
The Style Aggregator builds on the geometric backbone inherited from VGGT, which is composed of standard Transformer Block containing self-attention followed by feed-forward layers. To enable style conditioning, we replace this standard block with adapted cross fusion block (CrossBlock), which inserts a cross-attention operation between the self-attention and MLP stages (Deng et al. 2022). In CrossBlock, content tokens serve queries (Q) while style tokens provide keys and values (KC)). This structure allows the content representations to be refined by their own spatial context (via the preserved self-attention), while being explicitly conditioned on the target style (via the inserted cross-attention) before the final feed-forward projection.
Cross Fusion Blocks. Let Qb. LR denote the content tokens extracted from the w-th view of the b-th sample, and let KVV, R denote the style tokens. We use CrossBlock( Q, X.V) to denote our block operator that updates Q using K.V as key-value pairs. Depending on how the query is formed, we consider primary topological strategies.
(1) Frame CrossBlock, Each view independently interacts with the style tokens,
2 CrossBllock( Qi,er KVVb).
Since the internal self-attention of the block operates only on 2 this strategy preserves view- specific geometric structure but prevents information propagation between different views.
(2) Global CrossBlock. We first concatenate all views to obtain global sequence Ob V., X by gglobal Concaat V Qs and perform the block operation simultaneously,
Oglobal
Oglobal CrossBlock( Q5 Oglobal KVv)..
We then reshape the updated tokens back to per-view tensors, Øo... by splitting Tglobal along the sequence dimension. This approach enables long-range reasoning: the internal self-ateention ensures multi-view geometric consistency, while the cross-attention broadcasts style information globally.
(3) Hybrid CrossBlocks. We first apply Frame CrossBlock to refine each view independently as in Eq. and then perform Global CrossBlock by concatenating the resulting Ô across views and applying the same operator as Eq. This hybrid design combines per-view refinement with multi-view aggregation. We direct readers to the released codes for implementations.
3.2.3 PREDICTION HEADS
To connect the geometric backbone and Style Aggregator with the Gaussian scene, we introduce prediction heads that translate feature tokens into explicit parameters. These heads serve modular interfaces, keeping geometry and style distinct in feature space while ensuring their integration in the final Gaussian representation. We next describe the individual heads in detail.
Geometry Head. Geometric backbone features are passed through a DPT-style regression head that outputs the Gaussian geometry parameters pm. (Jmm: 8m, T'y Om) ), i.e., position, scale, orientation,
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and opacity (as defined in Sec. 3.1 derived from backbone features alone, without direct influence from style conditioning.
By relying this established design, structural predictions
Style Head. The outputs of the Style Aggregator subsequently processed by color head to predict the apherical-harmonic coefficients Cm that define appearance. This pathway injects style information directly into Gaussian colors while leaving the geometry parameters Pm, unaffected, enabling the two factors to be recombined seamlessly at the Gaussian level.
Auxiliary Heads, Adapter, and Voxelization. We employ the existing VGGT camera head timate camera intrinsics and extrinsics, and a depth head to predict scene geometry cues, which unprojected into 31) anchors for Gaussian placement (Kerbl al., 2023 Ren al. 2025) A Gaussian adapter then consolidates geometry and style outputs into a unified set of primitives (Pm. Gm) )== for differentiable rendering. Finally, reduce redundancy and balance density, we follow the voxelization step introduced by AnySplat (Jiang et al., 2025), nearby points are clustered within discretized 3D grid and fused using confidence-awart weighting. This operation depends only on the unprojected 3D points and features, and is independent of camera parameters.
3.3 TRAINING STRATEGY
We adopt a two-stage training strategy for structure-aware stylization.
Stage 1: Geometry Pretraining, We initialize the geometric backbone of Stylos with VGGT weights (Wang al.] (2255)) and train the network end-to-end to learn geometry and photomet- ric appearance. To avoid trivial identity mapping and improve robustness to color variations, one input view is randomly selected and color-jittered as the style reference. A frozen VGGT teacher provides pose and depth supervision. The objective combines reconstruction and distillation,
Luagel
Lree + Adisuul Cdisill-
Stage 2:
Stylization Fine-tuning, We freeze all geometry-related modules and only update the Style Aggregator and the color head. Following ArtFlow (An al. 2021), we use feature-level style and content losses in VGG space, matching channel-wise statistics for style and feature activations for content. We further extend these objectives with 3D voxel-space style loss for cross-view consistency, and add a CLIP-based loss for semantic alignment. A total variation (TV) regularizer also included suppress high-frequency artifacts and stabilize optimization. The total loss is,
Lstage2 = Liee + Xayle Cuyye + Aent Leontent + Aeli Lelip + Avv Liv.
In our experiments, we set Adisill = 1.0, and use (Matler Aent Actipy )n = {1.0, 0.1, 1.0, 10.0)
3.4 STYLE LOSSES
We denote by R/,y E RCIXHiXW the VGG feature map of the rendered image for the b-th scene and w-th view at layer 1, and by S E Ci the feature map of the single style image. Build- ing on the Batch Normalization statistics method (Li et al. 2017), which interprets BN mean and variance as style descriptors, we progressively extend it matching beyond standard 2D settings. This progression starts from independent image-level matching, moves multi-view feature aggregation that promotes global style coherence across views, and culminates in a voxel-space loss that directly constrains the fused 3D representation. Next, we formulate the three losses studied in this paper.
Image-Leved Style Loss. The simplest baseline aligns each rendered frame independently with the style reference, which encourages framf stylization but does not enforce multi-view consistency,
B
V
iig
1
ΣΣΣ b=1
as (Iu((R).)) ( (lo(R)),) - (
sty
BV
1=1
where ar denotes the weight at each level I, 4 is the mean and a is the standard deviation.
Scene-Level Style Loss. To introduce consistency into multi-view 3D stylization, we concatenate the per-view features W along the spatial dimension to form R/ and calculate statistics on this aggregated map. However, this method still operates in 2D feature space and is computed by,
B
5
nsen sty
an (I1((R)) - ((S)) (lo(R)) - (1)))
(4)
B
1=1
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Algorithm 1 3D Voxel-based AdaIN Style Loss
Require: Rendered features R. Style features S, 3D points P, Confidence C, Valid mask M Ensure: 3D style loss L3D
1: Initialize Lsty t- 0
2: for to do
3: for = to do
4: Resize M.) to match the resolution of Ri
5: Next, fuse all views into a single voxel grid
6:
9h + VoxelizeAndFuse ((Ri (Po (Co.u) {Mo,
7:
Next, compute the BN statistics (mean and standard deviation) of two feature maps (Mg σg) +- MMeanStd( (Ma, σ.) < MeanStd(S))
8:
9:
sty &- Laty + as asllpn Ms + =allo, o,112
10: end for
11: end for
return C3D Naty L3D
Voxel-level 3D Style Loss. Finally, to further enforce multi-view consistency, we fuse multi-view features into a voxel grid using differentiable unprojection, where features from different views are accumulated into spatial bins discretized 3D volume. Let 9 denote the voxelized features for the b-th scene at layer I. We then compute style statistics directly in voxel space.
B B
3D sty
1
(I - +1))) + - -
(5)
1=1
By operating on voxelized features, this loss explicitly encodes geometry and enforces style consis- tency across both views and the underlying 3D structure. We provide pscudo codes in Algorithm
4 EXPERIMENT
Datasets. We evaluate cateoory generalization on the CO3D (Reizenstein et al. 2021) dataset by training on 17 categories and testing on held-out ones, and cross-scene generalization by train- ing on the full DL3DV-IOK (Ling et al. (2024) and testing Tanks & Temples (Knapitsch et al.) 2017). Style images are provided by WikiArt (WikiArl 2025) and DELAUNAY (Gontier et al, 2023
with 50 diverse style images reserved unseen styles that never used during training.
Baselines. We compare Stylos with recent 3D stylization models. The baselines include, (1) per-scene training and zero-shot method StyleGaussian (Liu et al.] 2024), (2) per-scene and per- style optimization approaches G-Style (Kovács et al. 2024). StylizedGs (Zhang et al. 2025). and SGSST (Galerne et al. 2025), and (3) the closest related work Styl3R (Wang al. (025b).
Evaluation Metrics. Our evaluation covers three aspects. (1) To assess geometry reconstruction in the style-free setting, report PSNR, SSIM, and LPIPS (Zhang al. 2018) between content views and predictions, conditioned on the original first frame of each scene as the style input. (2) To measure stylization consistency, following prior work (Chiang al. 2022), we compute LPIPS and RMSE in both short-range and long-range settings. (3) To further evaluate stylization quality, we re- port ArtScore (Chen al. 2024), a recent metric specifically designed for reference-fre free evaluation of artness in generated images, and ArFFI (Wright & Ommer 2022 Chung et al. 2024).
4.1 ABLATION STUDY
In this section, we analyze the impact of each design choice on the CO3D dataset. To ensure fairness, all variants are trained with identical configurations in each experimental round.
Discussions about CrossBlock Designs. Take the pizza scene for example. Table shows that the baseline method, combining Frame and Global CrossBlock, yields PSNR of 19. 78 dB and LPIPS of 0.3326. The frame variant produces slightly lower PSNR and higher LPIPS. By contrast, applying the Global CrossBlock significantly boosts the PSNR value by 0.79 dB and achieves consistently

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Table I: Ablation on the style-content fusion module, comparing Frame, Global and hybrid Cross- Block designs. The first frame of each content scene is used as the pseudo style reference. Recon- struction quality is evaluated with PSNR1. SSIM1, and LPIPS] on the CO3D dataset.
Strategies
Skateboard
Pizza
Donut
Global Frame
PSNR1
SSIM
LPIPS!
PSNRT
SSIM
LPIPS
PSNR
SSIM
LPIPS..
21.12
0.6858
0.2821
19.78
0.5939
0.3326
21.39
0.7198
0.3264
20.93
0.6917
0.2912
19.72
0.5940
0.3405
21.40
0.7167
0.3340
21.68
0.7043
0.2684
20.57
0.6177
0.3110
22.09
0.7362
0.3125
(a) GT
(b) Hybrid
(c) Frame
(d) Global (Ours)
Figure 2: CO3D pizza scene comparing different CrossBlock designs.
improved metrics. Qualitative comparisons in Fig. highlight these differences. Frame CrossBlock produces a poorly defined crust with noticeable artffacts, the hybrid variant recovers part of the structure but leaves the box edges blurred, while Global CrossBlock delivers the most faithful result, with clear toppings and a well-preserved crust boundary.
Image vs. Scene vs. 3D Style Losses. shown in Table both scene-level and 3D style losses clearly outperform the image-level baseline in terms of artistic quality. Overall, the 3D losses ex- hibit superior and more stable consistency performance. These results indicate that enforcing style consistency in voxel-level yields more coherent stylized textures across views. We observe that image-level supervision may fail to transfer style. For example, in Fig. 3 the donut surface not well synthesized with the expected style, whereas the scene and 3D losses successfully apply the target style. The 3D style loss produces sharper boundaries and stronger sense of 3D geometry, as observed in the skatehoard and pizza examples, leading to the most coherent multi-view stylization. We release these model weights to support reproducibility and further analysis.
From Single View to Dozens of Views. While Stylos process up to dozens (even hundreds) of views, we observe gradual decrease in visual quality once the number of views per batch (denoted "# views batch") exceeds 32. As shown in Fig. very small batches (1 view) fail to synthesize parts of the scene like the bench, while overly large batches (64 views) introduce edge artifacts on the tall building, potentially due the gap from our training settings (no more than views). Additionally, we provide A.2 Fig. to show the efficiency trend with respect to the number of views.
Table Comparison consistency and artistic quality among different style loss designs on CO3D. The frame stride is set to 3, and 15 held-out scenes randomly selected for evaluation.
Short-range
Long-range
Style Loss
ArtScore
LPIPS}
RMSEL
LPIPS
RMSE
Image loss (Eq.
0.048
0.038
0.157
0.142
4.78
Scene loss (Eg
0.047
0.036
0.156
0.148
9.12
3D loss (Eq.
0.047
0.034
0.153
0.142
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Style
Content
Image Loss
Scene Loss
3D Style Loss
Figure 3: Comparison of style losses unseen donut, skateboard, and pizza scenes from the CO3D dataset. Both scene and 3D style losses yield cleaner stylized textures compared to image-level matching, while the 3D loss further conveys stronger sense of 3D geometry. We encourage readers to Appendix Fig. 8 10 for more visual comparisons under varing scenes and styles.
Content/Style
view
views
16 views
views
Figure 4: Effect of varying views batch on the Lighthouse scene from Tanks and Temples.
4.2 COMPARISON STATE-OP-THE-ARE
To retrain StyleGaussian Liu et al., 2024).
G-Style (Kováes et al. 2024), StylizedGS (Zhang
et al. 2025), and SGSST GGalrnee et al.) 2025 we strictly follow their released codes. Since the pretrained-weights of StyleGaussian (Liu al.
2024) and Styl3R (Wang al. 2025b) are publicly available, we use them to generate the visual results. Stylos is trained once DL3DV (Ling et al.)
2024], and tested zero-shot manner without prior knowledge of either the scenes or styles.
Stylos achieves strong and stable consistency scores, ranking the first across all consistency metrics and all four scenes. This indicates that Stylos provides markedly improved cross-view stylization consistency. Furthermore, Table 4 shows that Stylos attains either the best or second-best best artistic metric values, as rated by ArtScore and ArtFID, on all four Tanks and Temples scenes (Knapitsch al. 2017), while maintaining the fastest styliza- tion speed. Overall, Stylos demonstrates a favorable balance between visual quality, consistency, and efficiency, suggesting its potential for practical real-time 3D stylization.
Quantitative Evaluation. As shown in Table 3
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Table 3: For consistency comparisons, we report short-range and long-range LPIPS| and RMSE, on the four scenes from the Tanks & Temples dataset. We clarify experiment details in A.4 In the following tables, the best results are highlighted and the second best results are underfined. Each stylization method category is visualized with distinct color. The proposed Stylos demonstrates improved short-range and long-range consistency scores across the four scenes.
Train
Truck
M60
Garden
Method
LPIPSI RMSE
LPIPS
RMSEL LPIPS
RMSEL
LPIPS
RMSE
Short-range consistency
StyleGaussian
2024
0.033
0.038
0.031
0.034
0.038
0.037
0.069
0.061
G-Style 2024
0.042
0.052
0.032
0.035
0.038
0.034
0.066
0.070
SGSST 2025
0.038
0.047
0,039
0.047
0.044
0.049
0.084
0,090
Styl3R
2025b
0.061
0.036
0.066
0.040
0.105
0.067
Stylos (ours)
0.030
0.026
0.028
0.021
0.035
0.024
0.047
0.044
Long-range consistency
StyleGaussian
2024
0.067
0.072
0.086
0.077
0.091
0.091
0.177
0.141
G-Style 2024
0.098
0.120
0.095
0.093
0.104
0.095
0.180
0.175
SGSST 2025
0.087
0.108
0.119
0.120
0.130
0.128
0.221
0.222
Styl3R
20256
0.116
0.100
0.147
0.143
0.146
0.145
Stylos (ours)
0.051
0.056
0.074
0.069
0.083
0.082
0.139
0.134
StylizedGS (Zhang et al. 2025) not included in quantitative comparisons due to its multiple failure cases observed on our test styles. Nevertheless, its quantitative results are reported in Table readers' reference. Reproduced results available https://gttuu..com// htt.://u..ooommaaaoooouuuu//SS
Table 5lnd Toble
Table 4: Stylization quality, measured by ArtScore and ArtFID (abbreviated Score and FID respectively), and stylization time comparisons with recent 3D stylization models. Stylos achieves consistently favorable metric scores across the four scenes. Additionally, we follow StyleID (Chung et al. 2024 and calculate additional metrics reported in A.4 Table and Table
Train
Truck
M60
Garden
Method
Time
Scoret
FID
Scoret
FID
Scoret
FID
Score
FID
StyleGaussian
2024
0.78
52.79
5.76
44.93
8.63
47.48
9.38
41.14
165 m
G-Style 2024
9.52
23.24
9.67
22.15
9.73
22.36
8.98
25.76
14.7 m
SGSST 2025
1.84
38.24
5.34
32.34
5.26
38.73
4.89
33.54
35.2 m
Sty13R 20256
2.94
34.11
2.96
29.86
4.09
38.28
0.16 s
Stylos (ours)
9.50
26.40
9.70
28.71
9.37
27.44
9.34
28.06
0.05 s
For methods with per-scene fitting required, the training phase accounts towards stylization time (see A.4 As single-forward 3D stylization approaches, training not considered for stylization time.
Qualitative Evaluation. Taking the truck scene with the desert-lown style (yellow foreground ob- jects with blue backgrounds) in Fig. 5las an example, we observe that nearlly all existing approaches could reasonably produce structured results. However, their differences are discussed follows. The zero-shot method StyleGaussian fail to generate color-consistent scenes, several portions of the truck retain undesired color blocks. Per-scene and per-style fitting approaches such as G-Style, StylizedGS, and SGSST often fail to achieve complete style transfer, the truck door remaining pre- dominantly blue instead of adopting the target yellow hue. The other feedforward solution Sty13R similarly struggles to propagate the correct yellow color throughout the truck. In contrast, Stylos successfully renders the truck in coherent yellow while preserving clean geometry, and correctly assigns blue tones to the distant background regions, producing appearance that closely resem- bles the intended desert-town aesthetic. These observations underscore the strength of Stylos in achieving both faithful style expression and robust 3D structural preservation.

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Style
Content
StyleGaussian
G-Style
StylizedISS
SGSST
StyL3R
Stylos (ours)
Figure 5: Visual comparisons berween Stylos and recent 3D stylization approaches. Stylos success- fully transfers diverse artistic styles to the scenes while preserving fine structural details.
Figure 6: Evaluation of multi-style blending and controllable stylization. Top: interpolation between Iwo style embeddings. Bottom: interpolation between the content embedding and style embedding.
4.3 EVALUATION OF MULTI-STYLE BLENDING AND STYLIZATION CONTROL
We validate the model's controllable stylization capability through two interpolation experiments. First, by linearly interpolating between the embeddings of two distinct style images, observe smooth, coherent transitions that demonstrate the model's natural support for multi-style blending (see Fig. 6). Second, because the model is trained to reconstruct the original appearance when the style image matches the content image, we interpolate between the reconstruction (content) embedding and a stylized embedding Fig. ( This produces continuous spectrum of outputs with gradually increasing stylization strength, confirming that our approach enables fine-grained, post- inference control over the content-style trade-off. Together, these results show that our method supports both multi-style fusion and adjustable stylization without any additional optimization.
CONCLUSION
In this work, we propose Stylos, feed-forward method for 3D stylization. By keeping geometry predictions on the self-attentive backbone path and conditioning appearance via global CrossBlocks on the style path, together with voxel-space 3D style loss that aggregates multi-view features, Stylos achieves geometry-aware and view-consistent stylization. Our ablations show that the global CrossBlock for style injection better preserves geometric details than alternative style-content fusion modules. Extensive experiments across category-level and large-scale scene datasets demonstrate the generalization ability of our approach, achieving aesthetically pleasing stylization with strong cross-view consistency. In the future, we aim to scale Stylos to support higher-resolution inputs while improving efficiency, paving the way for practical 3D content creation.
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6 ACKNOWLEDGEMENT
This research used both the DeltaAl advanced computing and data resource, which is supported by the National Science Foundation (award OAC 2320345) and the State of Illinois, and the Delta ad- vanced computing and data resource which is supported by the National Science Foundation (award OAC 2005572) and the State of Illinois. Delta and DeltaAl are joint efforts of the University of Illi- nois Birbana-Chappan
and its National Center for Supercomputing Applications. The allocations on Delta and DeltaAl [allocation number CIS250529] were made by the Advanced Cyberinfras- tructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.
REPRODUCIBILITY STATEMENT
We have taken several steps ensure the reproducibility of work. The main paper describes all model architectures in Sec. 3
We provide detailed pseudo-code for the proposed 3D style loss function in Algo. [] and will release the full implementation upon acceptance. All datasets used are publicly available, as described in Sec. 4 These resources should allow researchers to reproduce and extend our findings. In Appendix [A.4 we clarify the training and inference details of the state-of- the-art methods for reproducible comparisons. In addition, we provide extensive qualitative results in Appendix A.6, and analyze efficiency trends with respect to the number of views per batch in Appendix A.2 where efficiency experiments are averaged over 100 iterations per setting.
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A APPENDIX
A.I IMPLEMENTATION
Implementation Details. For the geometry branch, we utilize VGGT as the backbone, compris- ing = 24 Alternating-Attentiin Transformer layers. Both the geometry transformer and the depth DPT head are initialized using pretrained VGGT weights, while all remaining layers are initialized randomly. The style aggregator similarly employs 24 Transformer layers, configured with content- global self-attention followed by content-style cross-attention. For voxelization, each output of Geo Head associates confidence value, which is converted into normalized intra-voxel weight via soft- max over Gaussians within the same voxel; these weights determine each Gaussian's 's contribution when aggregating voxel-level properties (opacity color). The Gaussian Adapter maps the Geom- etry head and Style head predicted vector into explicit 3D Gaussian parameters. The module first splits the predicted vector into (i) isotropic scales, (ii) rotation quaternions, and (iii) degree-d spher- ical harmonic coefficients. Finally, we construct full covariance matrices using the predicted scale and rotation via the standard 3DGS covariance formulation. This yields the final set of Gaussian parameters: means, covariances, SH harmonics, opacities, scales, and normalized rotations.
In the geometry training stage, the entire network is pre-trained. The geometry outputs, poses and depth, are guided by frozen VGGT teacher. The DINO encoder is trained capture color and style features. For stylization fine-tuning, we optimize only the style aggregator and the style head. We set the voxel size to 0.002 for differentiable voxelization. For both geometry pre-tres training and stylization fine-tuning, we train the model using the Adam optimizer for 15k iterations. We employ a cosine leamnin- rate schedule with a peak learning rate of 2 10 and 1k-iteration warmup.
Data & Augmentation. Following Anysplat (Jiang al. 2025 we randomly sample between 2 and 20 frames per clip, maintaining a fixed total of 20 frames per GPU. The input resolution is constrained to 448 pixels the longest side, with the aspect ratio randomized between 0.5 and 1.0. Intrinsic augmentation is applied via random center-cropping 177%-10%% the original size) and random horizontal flipping. During Geometry Pretraining, encourage the DINO encoder better learn color and style tokens by occasionally replacing the content image with a style image and by applying color jitter perturb its appearance, while keeping the original image ground truth to supervise appearance-change learning.
Loss Weights. Style weight Astyle = 1.0, sistency weight Actip 1.0, and total-variance regularization weight A 10.0.
weight Aent = 0.1, CLIP con-
A.2 EFFICIENCY SCALING
We further analyze the efficiency of Stylos by varying the number of views processed per batch. The results in Fig. 7 indicate clear scaling pattern. For very small batches, such as 1-2 views, the average runtime per batch is low, around 0.3 s, but the measurements fluctuate heavily due
10.416
4.829
Time butch (8)
GPU mem. (GB)
10
7,848
6.564
5.914
4.164
4.339
4.829
1.732
0.75
0.307 0.333
0.289
0.418
2
2
16
32
64
8
16
32
64
Views batch
Views
(a) Time per batch vs. # views per batch
(b) GPU memory usage vs. views per hatch
Figure 7: Scaling of inference time and GPU memory with the number views per batch. Time is averaged over 100 iterations, and memory is reported as the peak allocation, without rendering cost.
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to overheads and GPU scheduling. This makes single-view extremely small-batch inference unstable, even though the raw latency is minimal. As the batch size increases, runtime grows roughly linearly, reaching 0.75 at 16 views and 4.83 s at 64 views. Importantly, once the batch size exceeds 4-8 views, the runtime becomes much more stable, showing only minor variation across repeated runs. Memory usage shows similarly consistent trend: GPU memory rises steadily from 4.16 GB at I view to 10.42 GB at 64 views. The growth is close to linear with respect to batch size, which makes resource requirements easy to estimate when scaling to larger workloads.
A.3 ABLATION ANALYSIS
Content-Style Coupling. Stylos consists of style pathway which contributes the color infor- mation for the predicted 3DGS representation, and a content pathway for the other 3DGS parame- ters. In the style pathway, we introduce a style aggregator with attenntttonion between self attenttoooo and MLP to entangle the content and style tokens. Our training pipeline consists of two stages. The first stage focuses on 3D scene reconstruction, where a random content view used as the style ref- erence; Stage 2 performs actual stylization using a real artistic style image. When designing Stylos, it is therefore essential to verify that introducing the style-content fusion blocks does not harm the reconstruction process.
So, we compare three CrossBlock designs (global, frame, and both) by calculating PSNR, SSIM and LPIPS between the rendered multi-view rgbs and their corresponding training, using the first content view as the style reference. As shown in the Table the global- only variant achieves the best reconstruction accuracy. This behavior is expected, because each view attends to the style tokens independently without interacting with the other views in frame attention. Treating a single view as the style reference for all views forces each view match its patch-level representation to the same reference individually, which distorts the rendered images. Fig, 2 shows that the frame attention significantly blurs the pizza box and toppings.
tround-thruth after Stage
Image-, Scene-, and Voxel-level Style Losses. In this section, we provide additional visual com- parisons showing that the proposed scene and 3D style losses significantly outperform the baseline image style loss. Specifically, we observe that the scene- and 3D-level losses produce cleaner and more coherent textures, such as the wooden floor in Fig. [ As shown in Fig. 9 the 3D style loss is also able to stylize larger portion of the table surface compared with the image loss. In Fig. I0 the seams on the tablecloth are well preserved under the 3D loss, while they become partially distorted when using the image or scene losses. We will release our implementations of all three style losses to facilitate further exploration by the community.
A.4 REPRODUCIBLE EXPERIMENTS
Reproduce State-of-the-art Models. For StyleGaussian (Liu et al. 2024), SGSST (Galerne et al.) 2025). StylizedGS (Zhang et al., 2025), and GStyle (Kovács et al. (024), we use their officially re- leased codebases and pretrained weights. We cither run inference directly with the provided models or follow the authors' instructions to train the models when necessary. All of them are evaluated on GH200 GPUs for fair comparisons.
We notice that StylizedGS (Zhang et al. 2025) fail in serveral style cases, producing stylized im- ages with pure colors with barely visible geomtry details. It significantly improves its consistency scores in our experiments. For fair comparisons, we exclude these failure cases when calculating consistency and ArtScore for StylizedGS, as they could lead to very small values.
Stylization Quality Comparisons. In Table and 4 denotes that failure stylization cases for StylizedGS (Zhang et al. 2025) are excluded for fair comparisons. Please refer to our released codes for additional visual examples. aad Tare provided as supplementary quantitative results to Table
Stylization Time Comparisons. We run 48 samples as a hardware warm-up phase, and calculate the average inference speeds over the following 112 samples. For fair comparisons, all methods are evaluated on the same hardware, using an NVIDIA GH200 GPU with identical configurations. The reported stylization time of the per-scene fitting required method considers per-scene training time
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Table 5: Following StyleID CChung et al. 2024), D, we additionally report sylization quality metries.
FID, LPIPS, LPIPS-gray, gray, CFSD, color matching loss (HistoGAN loos), supplementary to Table
Train
Truck
Method
FIDJ
LPIPS
Gray
CFSD]
CM Loss.
FID.
LPIPS]
Gray
CFSD]
CM Loss.
StyleGaussian
34.59
0,483
0.377
0.190
0,418
28.53
0.522
0.367
0.131
0.382
G-Style
14.80
0.471
0.364
0.165
0.185
13.65
0.512
0.353
0.087
0.176
StylizedGS
22.90
0.707
0.648
0.216
0.396
22.65
0.779
0.713
0.084
0.417
SGSST
24.15
0,520
0,409
0.220
0.257
19.50
0.577
0.445
0.177
0.196
Sty13R
19.77
0.670
0.598
0.244
0.364
19.28
0.682
0.575
0.102
0.350
Stylos (ours)
15.30
0.620
0.529
0.223
0.241
16.67
0.625
0.471
0.084
0.237
Table 6: Following StyleID Chung et al. 2024).
we additionally report sylization quality metries,
FID, LPIPS, LPIPS-gra gray, CFSD, color matching loss (HistoGAN loos), supplementary to Table
M60
Garden
Method
FID
LPIPS|
Gray
CFSD]
CM Loss
FID
LPIPS]
Gray
CFSD]
CM Loss.
StyleGaussian
30.54
0.506
0.413
0.138
0.467
25.23
0.569
0.454
0.189
0.480
G-Style
13.93
0.498
0.395
0.092
0.208
16.17
0.500
0.364
0.103
0.179
StylizedGS
28.33
0.815
0.728
0.102
0.443
33.73
0.876
0.827
0.074
0,570
SGSST
23.95
0.552
0.458
0.204
0.264
20.93
0.529
0.415
0.233
0.228
Sty13R
17.14
0.646
0.573
0.124
0.314
22.38
0.637
0.556
0.097
0.335
Stylos (ours)
16.61
0.558
0.457
0.098
0.252
16.26
0.625
0.509
0.080
0.242
besides the rendering time. We strictly follow the original authors' training setups and inference pipelines. All IDGGSbased methods (Liu et al.] 2024 Galerne al. 2025 Zhang al., [025 Kováes et al. 2024) are evaluated on the full-resolution images, whereas single-forward methods, including Styl3R (Wang al. (0255) and Stylos, are evaluated their preset input resolutions, i.e.. 256 x 256 and 448 x 448, respectively.
A.5 LIMITATIONS
Stylos tends to underperform on scenes dominated by high-frequency or highly cluttered structures, such dense foliage, thin branches, wire-like elements, where preserving fine geometric detail particularly challenging. The method also struggles with style categories that introduce strong global lighting shifts or extreme color palettes, which can lead to over-saturation appearance cues. In addition, performance decreases as the number of input views increases. This degradation primarily stems from limitations of the VGGT backbone: as view count grows, its underlying geometric reconstruction becomes less stable, which in turn negatively affects the quality of the stylized outputs. More specifically, the failure cases can be grouped into three categories: (1) Failures caused by inaccurate scene reconstruction, such as missing geometric details or incorrect region colors; (2) Failures caused by generating colors that are absent from the original style images. (3) Failures caused by introducing blurry or over-smoothed style colors.
loss of subtle
A.6 ADDITIONAL VISUAL RESULTS
Figure 15 exhibit all the 50 test style images for quantitative comparisons. Figure 16 25 presents
additional qualitative results of Stylos diverse dyle-content pairs. Across different styles and ob- ject categories, Stylos produces visually pleasing stylizations that respect the input content geometry while transferring the target artistic appearance. These examples further demonstrate the versatility and robustness of our approach beyond the main results in the paper.
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Content
Style
Image Loss
Scene Loss
3D Loss
Figure 8: Visual comparisons of different style losses on the CO3D skateboard scene. It is clear that the 3D style loss provides stronger sense of 3D geometry and cleaner wooden floor textures evaluated on varying style images.
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Content
Style
Image Loss
Scene Loss
3D Loss
Figure 9: Visual comparisons of different style losses on the CO3D pizza scene. clear that the 3D style loss provides stronger artistic stylization to the table surface, while the image style loss often fails impose the expected styles to cover the whole table surface.
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Content
Style
Image Loss
Scene Loss
3D Loss
Figure 10: Visual comparisons of different style losses on the CO3D donut scene. It is clear that the 3D style loss provides faithful stylization to the table cover while preserving the seams on the tablecloth compared both the image style loss and the scene style loss.
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(a) The reconstructed metal bars the gate unclear shown in the mid and the stylized metal bars the top row are barely visible either.
(b) The green colors fail to correspond to the intended regions in the reconstructed seene (at the mid row), and the stylized multi-view images mistakenly interpret the green area as part of the target pattern, leading the model apply stylization (at the top row).
Figure 11: Failures caused by inaccurate scene reconstruction, such as missing geometric details or
incorrect region colors. Top row: The input style image and the stylized multi-vier results; mid row: reference frame from the input contents and the reconstructed multi-view images; bottom row: the input contents.
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(a) The reconstructed scene is generally accurate. However, our model introduces pure white region in the bottom-left area (at the top row), which does not appear in the original style image.
(b) The reconstructed scene generally accurate. However, our model hallucinates pure white region on the side (at the top row), which absent from the original style image.
Failures caused by generating colors that absent from the original style images. Top row: The input style image and the stylized multi-view results; mid row: a reference frame from the input contents and the reconstructed multi-view images; bottom row: the input contents.
Figure 12:
(a) Our model produces blurred stylized region in upper-left area of results (as shown the top row).
Figure 13: Failures caused by introducing blurry weo-she style colors. Top row: The input style image and the stylized multi-view results; mid row: a reference frame from the input contents and the reconstructed multi-view images; bottom row: the input contents.
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Louis Color rong_abbttact
Mition, Avery nude antinn_ru
James Impr
Impressic
Edgar Dagas, Impressiiriie, sanichandd_shie
essionism_gent paceeemm_eeeee_eeeeeeeeeeeeeee parting
Ray_Rose Impressooiim_.bn dacape
Parmglanioo Mannerism Runsissence, design
Petrov Wwwkin_aoonnnnneeeeeeeeee sludy
Kremer, Realiom wite painting
Eugene mmpressionss m. landscapa
Impressioriism andscape
Modem
Jandscape
Nouvesu dlysap
lndacapa
Surraaismmco
usghella,,
John Dedionsm_anndscapp
Post en_marina
Kenstantin iddsmpporroat
Konstartin, Post- piyscape
_eenling
formel abxtrecl
_genre painting
Figure 14: Style images from WikiAr 2025 used for model evaluation in all quantitative compar- isons, while not used for training Stylos.
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Edgar Degia, Incrussioriimn shotch_and stusy
da, Tourousee andruc oskiimgressooibnnooooo portaak
Imma, Stern, Posl-insteeoiirii i_portrall
Magillie Sumeelamm ay mbblk_ppannigg
Hans Hufmenn Abslract Expr esskonen_ebstract
Caaim__Cnii__ioieeeeeeee Romonddinn,, landscape
DobuzNisky_Byymmm ism, pityccyyaapp
Raphaoi, Kirchner,
Reaamo_Rcallsm... me_pahning
Connets, www.dennurgg...iggg.. Imppe seloniem_see selntem_ckyerrpee
ai_Modee ht__Wodemiieerrrr.. _itetryypanninn
Kong__Logh ByggghhsSmmllimmmee scape
Mkoby_Bograano--eee Begieanno---eeety......... allem garee, painting
Samuue_Papice Poer-impeess onain ciyyccae
Jacques www__VVllon_neeeeeeee CuteerC_emeeeee
abslract
Siskey Imprrsssionssm atysoape
Fugene_yon landscape
andscapo
_Nouveau
Constartin
Expressionis m_styscape
Kircherr
Konstantin: Makovskyy m_portratt
sstorism_lnndsceee Jandscape
valorism_goome paairinggggg
Figure 15: Style images from WikiAr (0025) used for model evaluation in all quantitative compar- isons, while not used for training Stylos.
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Content
Style
Figure 16: Additional visual results of Stylos on diverse style-content pairs on Tanks and Temples Dataset (Knapitsch et al.) 2017). Stylos consistently produces visually pleasing stylizations that preserve content geometry while transferring the target style.
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Additional visual results of Stylos on the CO3D dataset. The top left is the style image while the rest of the top row are the rendered images. The mid row are the rendered images condi- tioned on the mid left imamge. The bottom row is the content inputs.
Figure 17:
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Figure 18: Additional visual results of Stylos on diverse style-content pairs.
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Figure 19: Additional visual results of Stylos on diverse style-content pairs.
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Published as a conference paper at ICLR 2026
YONAYA
Figure 20: Additional visual results of Stylos on diverse style-content pairs.
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Published as a conference paper at ICLR 2026
GERKE
SEAL
10 AALD
O DU LEIN
NERER
Figure 21: Additional visual results of Stylos on diverse style-content pairs.
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Published as a conference paper at ICLR 2026
MYSSETTA
IN
POSS TUE LEIS
Figure 22: Additional visual results of Stylos on diverse style-content pairs.
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ARC FEALUE
ARC PEALRS
ABC HESLRE
ABC SEA2UC
ABC PERANM
Figure 23: Additional visual results of Stylos on the DL3DV-10K dataset. The top left is the style image while the rest of the top row are the rendered images. The mid row are the rendered images conditioned on the mid left imange. The bottom row is the content inputs.
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Figure 24: Additional visual results of Stylos on diverse style-content pairs.
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Figure 25: Additional visual results of Stylos on diverse style-content pairs.
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Reviews:
[{"review_id": "b3YMXM2ekA", "reviewer": "Reviewer_YvUR", "summary": "This paper presents StylOS, a single-forward 3D style transfer framework based on 3D Gaussian Splatting. The method employs a Transformer backbone with a dual-pathway design that separates geometry prediction from style injection: the geometry pathway maintains self-attention mechanisms to preserve geometric fidelity, while style is injected via global cross-attention to ensure multi-view consistency.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Proposes the first single-forward-pass 3D style transfer framework that requires no camera pre-calibration, offering significant practical value.\n\n2. The method demonstrates strong scalability, supporting processing from a single view to hundreds of views.", "weaknesses": "1. Lacks fair comparison with per-scene optimization methods under the same computational budget.\n\n2. Critical implementation details are missing regarding the voxelization operation: What is the voxel resolution? How are occlusions handled? What is the specific implementation of confidence weighting? Do these factors have significant impact on the results?\n\n3. The weight parameters for each loss term are not provided.\n\n4. The specific architecture of the Gaussian Adapter is not described in detail.\n\n5. There is insufficient discussion on which scene types or style categories the method performs poorly on.\n\n6. Why does global cross-attention not only improve style consistency but also enhance geometric fidelity? This seems counterintuitive and requires deeper analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uYxv7GePgr", "reviewer": "Reviewer_Y42i", "summary": "The paper introduces a feedforward stylized Gaussian method to achieve efficient, high-quality, and visually pleasing 3D scene reconstruction and stylization in a single step of model inference. The proposed approach is built upon VGGT, taking multi-view scene images and a style exemplar image as input. The model predicts the depth and camera poses for each view, while the predicted pixel Gaussian attributes are merged into voxels using confidence-based weights following the AnySplat framework. The results, as demonstrated in the figures and supplementary video, are remarkable.\n\nThis feedforward stylized Gaussian model has the potential to significantly impact the field. Artistic stylization, a cornerstone of human creativity throughout history, plays a vital role in bridging the technical and emotional aspects of visual representation. In the context of 3D world reconstruction, artistic stylization becomes even more critical, as it encapsulates the essence of reality through the lens of human emotion, emphasizing mood, character, and individuality. By enabling stylized 3D scene reconstruction without the need for test-time training, this model addresses a significant challenge and represents a major step forward in both technical and artistic innovation.", "questions": "1. **Include Inference Speed Comparisons**:  \n   Adding a comparison of inference speed would highlight the advantages of the proposed method and make the paper more compelling. Demonstrating faster processing times would provide a stronger justification for the model's practical contributions to the field.\n\n2. **Compare with StylizedNeRF and StylizedGaussian**:  \n   Include comparisons with StylizedNeRF and StylizedGaussian, as their stylization results are superior to those of StyleRF and StyleGaussian, respectively. The latter two methods primarily focus on zero-shot and fast stylization but rely on a CNN decoder, which means they are not strictly stylized **radiance** fields but rather stylization **feature** fields.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The technical strengths of the paper can be summarized as follows:\n\n1. This work is the first to achieve a stylized 3D world in a fully feedforward manner. From the early days of this field, StylizedNeRF introduced the concept of reconstructing 3D scenes using NeRF, accompanied by a conditional stylization module. While it generalized across styles, it required per-scene optimization. ARF, on the other hand, achieved superior results using a style-matching loss and a deferred backward strategy, but it was not generalized across scenes or styles. Both methods, being NeRF-based, faced limitations due to the high GPU demands during training, requiring solutions like mutual learning and deferred optimization to mitigate these challenges. Similarly, StyleRF was also restricted to per-scene optimization. \n\n   In the era of 3DGS, StylizedGS achieved impressive results in 3D stylization but remained constrained by per-scene fitting. This paper, however, marks a significant milestone by creating a stylized 3D world using a tuned foundation model like VGGT, combined with a generalized stylization module, enabling a fully feedforward pipeline. This eliminates the need for lengthy optimization processes. There is no more waiting hours to transform multi-view images into an artistic 3D world. This breakthrough paves the way for future research to focus on achieving even faster and higher-quality stylizations using similar feedforward approaches.\n\n2. The results presented in the paper are both impressive and inspiring. The examples, particularly in the supplementary video, showcase how realistic 3D scenes can be transformed into artistic styles such as cartoon, sketch, and painting. The stylization maintains consistency across views without any flickering artifacts, which is a significant achievement. Moreover, obtaining such high-quality results in a single feedforward inference is truly remarkable and highlights the potential for real-time applications in this domain.", "weaknesses": "1. The most significant contribution of this work is the introduction of a feedforward 3D stylization model, whose core advantages lie in faster inference and reduced memory usage. However, these critical metrics are not evaluated in the experiments. The paper should include a comparison of stylization time across prior works, from the earliest StylizedNeRF, ARF, and StyleRF to the more recent StyleGaussian and StylizedGaussian. For methods requiring per-scene fitting, the stylization time should account for both training and rendering times. Such comparisons would provide readers with a clearer perspective on how this work advances the field in terms of efficiency and practicality.\n\n2. The comparison between NeRF-based and Gaussian-based stylization approaches is not adequately addressed. While StyleRF focuses on zero-shot stylization, its stylization quality is not the strongest. A detailed comparison with stylization examples from StylizedNeRF and similar NeRF-based methods is essential to highlight the advantages of the proposed approach. Moreover, StylizedGS, which outperforms StyleGS, should be included as the representative Gaussian-based stylization method in the comparisons. Incorporating these comparisons would strengthen the paper by demonstrating the superiority of the proposed method over both NeRF-based and Gaussian-based approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Vck1XYfkQX", "reviewer": "Reviewer_Sv2n", "summary": "The paper introduces StylOS, an efficient framework for view-consistent 3D style transfer using 3D Gaussian Splatting (3DGS). The main technical contribution is a single-forward inference model, built upon VGGT and AnySplat, that directly predicts the stylized 3DGS representation. This approach enables optimization-free, geometry-aware synthesis from unposed content views (from a single image to a multi-view collection) and a reference style image. By bypassing time-consuming per-scene optimization, StylOS achieves fast processing and superior generalization to unseen scenes and styles.", "questions": "1. **Style Control and Blending**: Can the authors demonstrate fine-grained control over the stylization process? Specifically:\na. Can the system adjust the content-style trade-off weight post-inference to control the strength of the stylistic transfer?\nb. Can the model perform multi-style blending by interpolating or averaging the style embeddings from two or more distinct style images?\n\n2. **Input and Output Resolution**: What is the range of resolutions for the input content views (single or multi-view) and the output rendering that the model is designed to support? Are there inherent limitations or scaling constraints related to the Transformer architecture or 3DGS representation density?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. **Single-Forward Efficiency**: The most compelling contribution is the ability to perform high-quality 3D style transfer in a single forward pass without per-scene optimization. This drastically reduces processing time and makes 3D stylization scalable for real-time or large-scale applications.\n\n2. **Transformer Integration and Unposed Capability**: By utilizing a Transformer architecture (built upon VGGT) to directly predict the parameters of the stylized 3D Gaussian Splatting representation, the method bypasses the traditional optimization pipeline and simultaneously enables robust handling of unposed content (single or multi-view). This significantly lowers the barrier to entry for users, as complex camera pose estimation is not required.\n\n3. **Generalization and Robustness**: The method demonstrates excellent generalization capabilities across unseen content scenes, object categories, and style images, suggesting robust learning of disentangled style and geometry features.", "weaknesses": "1. **Limited Technical Novelty in Overall Framework**: The overall conceptual framework of achieving fast, optimization-free 3D stylization by integrating a feed-forward 3D reconstruction model (VGGT/AnySplat) with cross-attention for style injection is highly similar to previous works like Styl3R (NeurIPS 2025, published on arXiv in May 2025), which uses Dust3R. While StylOS's integration of VGGT allows it to process a wider and more flexible range of input views, the core technical contribution of the idea of a single-forward 3D stylization framework is substantially weakened by this conceptual overlap. The authors should better address this overlap and clearly articulate how their specific implementation of the style injection and 3DGS prediction provides unique advantages over Styl3R's architecture.\n\n2. **Style Fidelity vs. Optimization**: Although the speed advantage is clear, a deeper discussion or visual comparison is needed to evaluate the potential trade-off in style fidelity against state-of-the-art optimization-based 3D style transfer methods like 𝒢‐Style: Stylized Gaussian Splatting or ARF. Are there certain styles (e.g., fine-grained texture styles) where the feed-forward approach visibly struggles compared to optimization?\n\n3. **Minor Formatting Concern (Font)**: It appears the authors may have modified the default font used in the ICLR style configuration.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rTKKOsLV4h", "reviewer": "Reviewer_LtqC", "summary": "The paper proposes Stylos, a feed-forward 3D Gaussian stylization framework. Given one or more unposed RGB views of a scene plus a single style reference image, Stylos directly predicts a stylized 3D Gaussian scene in a single forward pass, without per-scene optimization or known camera intrinsics/extrinsics. Stylos explicitly separates geometry and style: geometry is inferred by a VGGT-like alternating-attention backbone, while style is injected through a Style Aggregator that applies global cross-attention from the style tokens to all content-view tokens. The paper also introduces a voxel-based 3D style loss: instead of matching style statistics (mean/variance in VGG feature space) per-frame, Stylos fuses multi-view features into a shared voxel grid and aligns those 3D features to the style distribution, which the authors argue enforces cross-view consistency and preserves geometry. Experiments on CO3D, DL3DV-10K, and Tanks and Temples report improved multi-view consistency (LPIPS/RMSE) and competitive perceptual/artistic quality (ArtScore) compared to per-scene stylization baselines such as StyleRF and StyleGS, and include ablations on cross-attention design and style loss variants.", "questions": "- Failure cases of the 3D style loss.\nAre there any visual failure cases where voxelized style statistics hurt local detail (e.g., oversmoothing, texture bleeding in occluded regions)? Right now we only see success cases and quantitative gains.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Practical significance. Single-forward stylization of an entire 3D Gaussian scene (poses + Gaussians + colors) without per-scene finetuning or known camera parameters could be transformative for content pipelines. This is not just incremental NeRF/3DGS stylization but closer to the real situation, like “instant turn-this-video-into-stylized-3D-assets.”\n- Architectural clarity. Clean separation between a geometry backbone and a Style Aggregator branch (global cross-attention conditioning on the style image). The ablation across “frame-only,” “hybrid,” and “global-only” variants is convincing and shows why global conditioning improves structure and texture coherence.\n- Breadth of evaluation. The paper reports not only reconstruction-like metrics (PSNR/SSIM/LPIPS) but also temporal / multi-view consistency metrics (LPIPS & RMSE at different frame gaps) and ArtScore for perceptual “artness,” and demonstrates cross-category and cross-scene generalization.", "weaknesses": "- 3D style loss justification is empirical. The 3D voxel-statistics loss gives better LPIPS/RMSE consistency, but the paper does not analyze potential side effects such as global color bleeding or loss of local style detail in poorly observed regions. Some intuition or failure-case visualization would strengthen the argument that voxel-statistics matching is fundamentally better than 2D scene-level matching.\n- Scalability limits. Although Stylos is advertised as handling up to “hundreds of views,” the scaling experiment shows visible degradation beyond ~32 views per batch (edge artifacts, instability in the Gaussian representation). The method may still be quite useful, but this practical constraint, probably brought by VGGT, should be communicated more prominently.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)