Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles
Authors: Peng Wang
Xiang Liu
Peidong Liu
Venue: NeurIPS 2025 poster ()
Keywords: Style Transfer
Instant 3D Reconstruction
Primary area: applications
TL;DR: Given unposed sparse-view images and an arbitrary style image, our method predicts stylized 3D Gaussians in less than a second using a feed-forward network.

Abstract: Stylizing 3D scenes instantly while maintaining multi-view consistency and faithfully resembling a style image remains a significant challenge. Current state-of-the-art 3D stylization methods typically involve computationally intensive test-time optimization to transfer artistic features into a pretrained 3D representation, often requiring dense posed input images. In contrast, leveraging recent advances in feed-forward reconstruction models, we demonstrate a novel approach to achieve direct 3D stylization in less than a second using unposed sparse-view scene images and an arbitrary style image. To address the inherent decoupling between reconstruction and stylization, we introduce a branched architecture that separates structure modeling and appearance shading, effectively preventing stylistic transfer from distorting the underlying 3D scene structure. Furthermore, we adapt an identity loss to facilitate pre-training our stylization model through the novel view synthesis task. This strategy also allows our model to retain its original reconstruction capabilities while being fine-tuned for stylization. Comprehensive evaluations, using both in-domain and out-of-domain datasets, demonstrate that our approach produces high-quality stylized 3D content that achieve a superior blend of style and scene appearance, while also outperforming existing methods in terms of multi-view consistency and efficiency.

Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles
Peng Wang Xiang Liu Peidong Liu
Zhejiang University 2 Westlake University
(wangpeng, 1iae::nnnnlluupeedoonnnneeesee @westlake edu. en, liuxiangnickkgmail.c. com
Project Page
Arbitrary Scenes and Styles
Stylized 3D Gaussians
0.15s
Styl3R
Figure 1: Sty13R. Given unposed sparse- view images and an arbitrary style image, method predicts stylized 3D Gaussians in less than a second using feed-forward network.
Abstract
Stylizing 3D scenes instantly while maintaining multi-view consistency and faith- fully resembling a style image remains a significant challenge. Current state-of-the art 3D stylization methods typically involve computationally intensive test-time optimization to transfer artistic features into a pretrained 3D representation, often requiring dense posed input images. contrast, leveraging recent advances in feed- forward reconstruction models, we demonstrate novel approach to achieve direct 3D stylization in less than a second using unposed sparse-view scene images and an arbitrary style image. To address the inherent decoupling between reconstruc- tion and stylization, we introduce branched architecture that separates structure modeling and appearance shading, effectively preventing stylistic transfer from distorting the underlying 3D scene structure. Furthermore, we adapt an identity loss stylization model through the novel view synthesis task. This strategy also allows our model to retain original reconstruction capabilities while being fine-tuned for stylization. Comprehensive evaluations, using both in-domain and out-of-domain datasets, demonstrate that our approach produces high-quality stylized 3D content that achieve superior blend of style and scene appearance, while also outperforming existing methods in terms of multi-view consistency and efficiency.
to facilitate pre-training
"Equal Contribution;
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advances in 3D reconstruction from 2D images [15,27, 49] have made it increasingly practical to generate high-quality 3D scenes from casual captures. However, integrating artistic features such as the visual style of a reference image into these reconstructions remains technically challenging. This difficulty arises from the fundamental mismatch between style transfer and 3D reconstruction: altering the visual appearance reflect a desired style often conflicts with the need to preserve multi-view consistency and structural coherence in the reconstructed scene.
Creating stylized 3D scenes typically requires professional expertise, artistic creativity, and substantial manual effort. While 2D image style transfer [9, 12, 20] has achieved impressive efficiency and visual fidelity, naïvely extending these techniques 3D often results in nle-iinonssistent stylization. In contrast, recent 3D stylization methods [8, 23, 24, 54] offer improved appearance consistency but rely on dense multi-view inputs, known camera poses, and time-conuuming per-scene or per-style optimization. These requirements render them impractical for casual users or time constrained applications. This leads to a central question: How can we achieve fast, multi-view consistent 3D stylization from few unposed content images and an arbitrary style image without requiring text-time optimization?
To address this challenge, we propose Sty13R, a feed-forward network that jointly reconstructs and stylizes 3D scenes from sparse, unposed content images and arbitrary style image. Our method flexibly handles 2 to 8 input content images, requiring no test-time optimization, no camera supervision, and no scene- or style-specific fine-tuning, making it both practical and accessible for real-world usages. For clarity, we summarize the key differences between prior methods and ours in Table 1.
In particular, Sty13R adopts a dual-branch architecture consisting of a structure branch and an appearance branch. The structure branch predicts the structural parameters of 3D Gaussians from unposed content images by leveraging dense geometry prior. The appearance branch, responsible for determining the color of the 3D Gaussians, consists of transformer decoder layers that blend style features with content features from multiple viewpoints. This design enables the generation of photorealistic and stylized appearances while maintaining multi-view consistency. To further preserve photometric shading capabilities during stylization fine-tuning, we introduce an identity loss by randomly feeding content image into the appearance branch, encouraging the model retain its ability to reconstruct the original appearance.
We evaluate method on both in-domain and out-of-domain datasets. It outperforms prior ap- proaches in terms of both multi-view consistency and efficiency, which producing high-quality 3D stylized content in only 0.15 second.
Our main contributions are:
We introduce a feed-forward network for 3D stylization that operates on sparse, unposed content images and an arbitrary style image, does not require test-time optimization, and generalizes well to out-of-domain inputs making it practical for interactive applications.
We design a dual-branch network architecture that decouples appearance and structure modeling, effectively enhancing the joint learning of novel view synthesis and 3D stylization.
Our method achieves state-of-the-an zero-shot 3D stylization performance. surpassing existing zero-shot methods and approximate the efficacy of style-specific optimization techniques, demonstrated through both quantitative metrics and qualitative results.
2 Related Work
2D Style Transfer.
The task of transferring the visual style of reference image onto another
content image has been extensively studied. The seminal work [9] introduced neural style transfer by iteratively optimizing the stylized image to match the Gram matrices of VGG- VGG-extracted features [36] from both content and style images. Subsequent approaches, such as AdaIN [12], WCT [21],
LST [20], and SANet [31], reformulated the problem into a feed-forward setting by aligning the
feature statistics of content and style. With the emergence of transformer- based architectures [6, 42],
more recent methods like AdaAtNN [25], StyleFormer [47], StyTr2 [5], S2WAT [52], and Master [40]

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Spanse View
Scene Zero-shot
Style Zero-shot
View Consistency
Pose
Fast
Method
Free
Inference
2D
Methods [5, 12, 25]
X
StyleRF [23]
X
x
x
3D
StyleGaussian [24]
X
ARF [24]]
StyI3R (Ours)
Table I: Comparison with existing style transfer methods. 2D methods can instantly stylize images after training without any additional tuning, but they fail to ensure multi-view consistency. Prior 3D methods ensure consistency but rely on dense posed inputs and per-scene or per-style tuning, slowing inference. Our method combines both strengths, achieving view-consistent stylization under a second without further tuning.
leverage attention mechanisms to enhance feature representation. moving beyond the limitations of intermediate features extracted from pretrained VGG networks [36]. Despite progress in visual quality and efficiency. 2D style transfer methods lack geometric awareness and multi- view consistency, often causing artifacts when naïvely extended to 3D. In contrast, our method achieves geometry coonssttcconsiseent, view-coherent stylization with fast inference and rendering.
3D Style Transfer. 3D stylization has been explored using various scene representations. Early methods leveraged explicit forms like meshes [10] and point clouds [11, 17, 28], enabling style transfer via differentiable rendering and geometric warping, but they struggled with complex scenes due to limited expressiveness. Recent work adopts implicit representations like NeRF [27] and 3D Gaussian Splatting (55..)[15. StylizedNeRF[ [1] incorporates a 2D stylizer with view-consistent losses; ARF [54] improves structural fidelity with feature matching: StyleRF [23] enables zero-shot stylization via feature-space transformation; INS [7] disentangles style and content with geometric regularization. For 3DGS, StyleGaussian [24] and [34] embed style features into Gaussian parameters; SGSST [8] introduces multi-scale losses for high-res output; InstantStyleGaussin
[51] stylizes via dataset updates; StylizedGS [53] achieves controllable editing by disentangling content, style, and structure. However, most methods require dense views, known poses, and per-scene optimization, limiting their applicability casual settings. Other recent efforts [30, 38] explore object-centric stylization using attention and diffusion models (e.g. Zero123++ [35]) but suffer from high inference times (~30s per object). contrast, our method does not directly rely on the output of external pretrained models and achieves stylization in under one second.
Feed Forward Generalizable 3D Reconstruction. Though NeRF [27] and 3DGS [15] achieve high-quality view synthesis, they rely on dense, calibrated inputs and costly per-scene optimization, hindering their practical usages for time-constrained applications. To address this, generalizable reconstruction methods [3, 14, 44, 48, 50) have emerged, primarily leveraging geometric priors like cost volumes or epipolar constraints to aggregate multi-view information from sparse inputs. In parallel, feed-forward 3DGS pipelines [2, 4. 39] infer pixel-aligned Gaussians from image features for efficient and high-quality rendering. More recent models [37, 49] advance this direction by directly reconstructing 3D scenes from sparse, unposed images without the need for camera poses, highlighting the potential of learning- based pipelines to generalize in purely ferwafd manner. Building upon this line of work, we propose novel 3D stylization pipeline that disentangles structure and appearance, unifying 3D reconstruction and style transfer in a single network.
3 Method
Given a set of sparse unposed images === [1 (I H where H and W are the height and width of image, superscript represents content) capturing scene along with an arbitrary style image Ie E R H W x3 (superscript s represents style), our task is to instantly get the stylized 3D reconstruction of the scene represented by a set of pixel-aligned Gaussians G = Nx without compromising multi-view consistency and the underlying scene structure. These Gaussians G* are parameterized by {(M,, ajj Sj, , NxH where ,y, aj. rj. s, and oj are the Gaus- sian's 's position, opacity, orientation, scale and stylized color. Alternatively, it is also able to predict the ntn-stylized Gaussians g", which share all the other attributes but have different set of colors N HWW compared to g*.
{c)) N
3

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content tokens
Content Encoder
Stylization Decoder
|style tokens
Decoder
content
SA
O
CA
-
MLP
Structure Head
tokens
weights
cross attention
Structure Decoder
S,
Encoder
Gaussian Params
Stylized 3DGS
style tokens
Style
Stylization Decoder
Color
Encoder
Head
Non-styliizdd
3DGS
Figure 2: Overview of Sty13R. Our model consists of a structure branch and an appearance branch that output different attributes of Gaussians. For the structure branch, sparse unposed images encoded by shared content encoder, then content tokens of each image are separately fed into their structure decoders with information sharing between other views. Attributes that govern the structure of the scene are then regressed from structure heads. For the color branch, style image is encoded by the style encoder, then the output style tokens perform cross attention with content tokens from all viewpoints in the stylization decoder. Finally the color of Gaussians are predicted from these blended tokens output by this decoder, which compose all Gaussian parameters along with the output from structure branch. Apart from style image, the appearance branch can also accept content image which gives the Gaussians their original colors.
Inspired by [5, 49], we propose a dual-branch architecture that separates the network into structure building branch and an uppearance shading branch. In the appearance branch, we employ a stylization decoder that first performs global self-attention across content tokens from all views to ensure multi- view consistency, then injects style tokens to perform cross-attention with content tokens without interfering the structure branch.
An overview of the pipeline shown in Fig. 2. In this section, we first introduce the structure branch that leverage dense geometry prior from DUS(3R [45] (Sec. 3.1). Then illustrate the appearance branch that governs the color of output Gaussians (Sec. 3.2). Finally, we design training curriculum that facilitate the learning of stylization while effectively preserving geometry prior (Sec. 3.3).
3.1 Structure Branch
In order to leverage the dense geometry prior from DUS(3R [45], we employ a ViT-based encoder- decoder architecture estimate the structure of the scene. A of sparse unposed images Ie capturing the scene are first patchified and then encoded by a shared ViT encoder to set of content
Nx "
where p is the patch size. The encoded tokens from one view is then fed into a VIT decoder which perform cross attention with concatenated tokens from all other views to ensure channeling of multi-view information as in [49]. From these output tokens of the decoder for each view, one DPT head [33] is employed to predict the center positions μı of Gaussians, another DPT head regresses other structural attributes: orientations r,, scales s, and opacities oj.
tokens T [t [tt) k=1
3.2 Appearance Branch
To separate the colorization of Gaussians from the estimation of scene structure, we propose an appearance branch that ensures the subsequent stylization would not degrade the learned geometry prior in the structure branch. For ease of alignment between encoded content and style tokens, we leverage the same architecture content image encoder for style image encoder, but with different set of learnable weights. This ViT-based style encoder receives an arbitrary style image I and then
outputs set of style tokens T
{t: pr which are then sent to the stylization decoder.
Stylization Decoder. Within the stylization decoder, content tokens output by the content encoder are stylized by style tokens T Initially, T from multiple views concatenated, and then

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perform global self-attention to get T ensuring multi-view consistency. Then these self-attended content tokens T are used to generate queries while style tokens T are used to generate keys and values for the cross attention that blends these two streams of information as shown in Eq.
T Crosstttention T'W* .TWV)
where Wº,, W K and W V are the projection matrices to generate queries, keys and values for cross attention. After this blending. stylization decoder finally outputs a set of stylized content tokens T
Color Head. From these stylized content tokens T a DPT head is used to predict the stylized color of for each Gaussian, which is adapted according the given style image I. These color components {c NxHW along with the other parameters regressed from the structure branch compose the complete set of attributes {(1y. Ojj (( ry, e NxxxX for stylized Gaussians G
Content as Style. Worth noticing, content image I can also be viewed as special style image that maps the content to its original appearance. This naturally leads the stylization of appearance branch normal photorcalistic shading in novel view synthesis. Thus, inputting content image Iř to style branch will give us the non-stylized Gaussians g This insight is applied to facilitate subsequent training.
3.3 Training Curriculum
Notably, 3D stylization and reconstruction are not inherently well aligned, as optimizing for style loss may degrade the underlying 3D structure of the scene [8]. To address this, we adopt two- stage training curriculum. In the first stage, the model trained to accurately estimate the scene structure and perform standard photorealistic shading. After this stage, we proceed to stylization fine-tuning stage, during which the structure branch is frozen to ensure faithful preservation of the scene geometry.
Novel View Synthesis Pre-training. At this stage, we train the whole model end-to-end for the novel view synthesis (NVS) by solely using photometric loss calculated between novel view images rendered from G and ground truth target RGB images as in Eq. 2. During NVS training, we randomly input one content image I to the appearance branch. This encourages the appearance branch to preserve the original scene color at this stage. After this pre- pre-training phase, given a set of sparsely unposed images, the structure branch can predict intricate 3D structure, while the appearance branch is capable of performing photorealistic shading for Gaussians, which lays a solid foundation for the next stylization fine-tuning stage.
Stylization Fine-tuning. Building upon the preceding novel view synthesis (NVS) pre-training, the model can primarily focus on learning stylize the appearance of the Gaussians. In each forward pass, we input content images Ie and a style image Is to the network, which outputs the stylized Gaussians G to render stylized images at novel viewpoints. These images are used to calculate the losses in Eq. 2 to update the appearance branch. As mentioned in [8], optimizing all Gaussians parameters towards style loss can drastically degrade the structure of reconstructed scene, thus only fine-tune the appeareance branch during this stage, and freeze the structure branch.
In terms of loss functions, we first employ a weighted combination of style and content losses. For the style loss, we measure the differences in mean and variance between novel view images rendered from G and the reference style image Ie, computed over the relui_1. relu2_1, 1, relu3_ 1, and relu4_1 1 feature maps of VGG19 [36]. For the content loss, we compare the relu3_1 1 and relu4_1 _ feature map responses between images rendered from G and the corresponding ground-truth target RGB images. Empirically, we find that incorporating both relu3_ 1_ and relu4_1 in the content loss more effectively preserves the structural fidelity of the original scene, compared to using a single layer as commonly done in previous style transfer approaches [8, 54], as shown in Fig. 7.
Besides, to preserve the model's NVS ability during stylization fine-tuning, we adapt the identity loss from [31]. Apart from the style image Is, we also feed a randomly selected content image I to the appearance branch to obtain the non-stylized Gaussians G Similar to the first stage, we also minimize the photometric loss between novel view images rendered from G and ground truth target RGB images while optimizing style and content losses.
5

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Style
Content
Ours
StyleRF
StyleGanssian
ARF
StyT12
Figure Novel View Transfer Comparision on RE1OK. Despite limited image overlap, our method generates stylized novel views that more faithfully capture style details while preserving the original scene structure. comparison, StyleRF [23] and StyleGaussian [24] tend produce over-smoothed results that deviate from the true color tone of the reference style. ARF [54] suffers from style overflow, leading to significant loss of content appearance. As a 2D baseline, StyTr2 [5] operates directly ground-truth novel views, but fails to retain fine structural details of the scene.
Training Losses
The losses used in two training stages summarized as below.
Spphano (aamn((9))) (9º), C
NVS pre-training
Ae (9*) Ceontent
Cphoro (g"),
stylization fine-tuning
where Lphoto is the photometric loss which is a linear combination of MSE and LPIPS [55] loss with weights and 0.05, respectively, and À = 10 is the weight for style loss.
Progressive Multi-view Training To stabilize multi-view training, first pre-train the model on the 2-view setting for the NVS task, which is then used to initialize the 4-view NVS training and subsequent stylization fine-tuning. Though trained with 4 input views, our model can flexibly handle 2 to 8 views during inference as shown Fig. 8.
Experiments
Datasets.
We use combination of RealEstate 10K (RE10K) [56] and DL3DV [22] scene dataset, covering both indoor and outdoor videos with diverse camera motion patterns. For style supervision, use WikiArt [32], and assign unique style image to each scene in the training and evaluation sets. This setup ensures that neither the test scenes nor styles were seen during training.
To evaluate zero-shot generalization, we test on the Tanks and Temples [16] dataset which is widely used by prior 3D style transfer methods [8, 23, 24, 54].
Baselines. Since no existing methods can instantly stylize 3D reconstructions from sparse, unposed content images and a style reference image (as outlined in Table 1), we carefully select set of representative baselines for comparison. For 2D-based approaches, we adopt two-stage pipeline using AdaIN [12], A AdaAtNN [25], and StyTr2 [5]: we first extract ground-truth novel view images and then apply each 2D stylization model to these images. For 3D-based methods, we compare against ARF [54], StyleRF [23], and StyleGaussian [24], which perform 3D stylization but require dense input views and test-time optimization. To ensure proper functionality, we train these methods

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ARF Figure 4: Cross-dataset generalization Tanks and Temples dataset. Our model achieves superior or comparable zero-shot style transfer on out-of-distributio data, outperforming style-free baselines such as StyleRF [23] and StyleGaussian [24] that require per-scene optimization, and matching the performance of ARF [54], which further demands per-scene and per-style optimization.
Style
Content
Ours
StyleRF
StyleGausian
Consistency
Stylization Time
Method
Short-range IPIPSL RMSEL LPIPSL RMSEL
Long-range
AdaIN [12]
0.163 0.063 0.323
0.111
0.004
AdaAuN [25]
0.224
0.071
0.331
0.098
0.024
StyTr2 [5]
0.167
0.059
0.315
0.098
0.029
StyleRF [23]
0.062
0.021
0.172
0.042
90 mins
StyleGS [24]
0.048
0.022
0.137
0.043
132 mins
ARF [54]
0.093
0.038
0.217
0.070
12 mins
Ref.
StyTY2
Ours
Styl3R(Ours)
0.044
0.022 0.107 0.038
0.147
Figure 5: Visual Comparison. Visualizations of different views produced by StyTr2 [5] and our method. The highlighted regions (lamp and bed sheet) show noticeable color discrep- ancies in Tr2, while approach maintains consistent color across views.
Table 2: Quantitative Results. Performance com- parison of Sty13R with 2D and 3D baselines on REIOK in terms of view consistency. Stylization time refers to processing time excluding IO time.
with dense inputs, acknowledging that this gives them an advantage and makes the comparison less favorable to our approach, which requires only sparse input views. Specifically, ARF [54] requires both per-scene and per-style optimization; while StyleRF [23] and StyleGaussian [24] support shot style transfer. they still depend on per-scene optimization.
Evaluation Metrics. Because of the novel and under-explored nature of 3D stylization, there few metrics for assessing the quality of the stylization. Therefore, we evaluate the multi-view consistency in prior 3D stylization works [8. 23, 24]. We estimate optical flow between sequential images using RAFT [41], then warp the earlier frame with softmax splatting [29]. Consistency is measured by LPIPS [55] and RMSE between the warped and target images over valid pixels. Short- and long- range consistency are computed between adjacent views and those seven frames apart, respectively. We further employed ArtFID [46], metric well aligned with human perceptual judgment by jointly assessing content preservation and style fidelity, together with the RGB-uv histogram from HistoGAN [1] to comprehensively evaluate the quality of color transfer. To evaluate novel view synthesis quality, we report standard image similarity metrics: PSNR, SSIM, and LPIPS [55].

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Method
PSNR
SSIM 1
LPIPS 4
Inpur
w/o identity loss
w/ identity loss
GT
pixelSplat [2]
23.848
0.806
0.185
MVSplat [4]
23.977
0.811
0.176
NoPoSplat [49]
25.033
0.838
0.160
NoPoSplat*
24.836
0.832
0.166
Ours
24.871
0.837
0.165
Ours-stylization
24.055
0.820
0.179
Table 6: NVS comparison on RE10K. * de- notes O-degree spherical harmonics, used in our model, while [49] defaults to 4-degree.
Figure 6: Ablations. NVS results w/o and w/ iden- tity loss during stylization fine- fine-tuning. The former fails to retain the true color tone of the scene.
Input
relu3_ relu4_]
relu3_1
Input
relu3 1 relu4
relu3 1
Figure 7: Ablations. Stylization results of model trained with content loss consist of different layers. Using relu3_1 and relu4_1 in content loss preserve the original scene appearance more faithfully.
User Study. We conducted online user study to evaluate perceptual prefer- ences between and alternative methods. Participants were asked to select the result they per- ceived as exhibiting the most harmonious integration of style and scene. In to- tal, approximately 2,000 votes were col- lected, including 1,274 for REIOK, 294 for Tanks and Temples, and 392 for the ablation study.
Sty13R
ARF
StyleGS
StyleRF
StyTy2
REIOK (%)
53.29
14.13
6.67
1.81
24.10
proposed approach
TnT (%)
38.78
22.11
11.56
3.06
24.49
Table 3:
User Study. Voting results for stylization.
Metric
Sty13R
ARF
StyleGS
StyleRF
StyTr2
AnFFIDRRETOK
35.12
42.95
55.75
46.59
38.93
ARFIDÍTHT
38.05
48.98
55.03
64.81
39.24
Segram_REOOK
0.230
0.313
0.465
0.507
0.241
H-gram,Tut
0.241
0.259
0.379
0.422
0.247
Table 4: ArtFID [46] and Histogram [1] for compari- sions.
Implementation details. We use Py- Torch. The content and style encoder adopts standard ViT-Large architecture with a patch size 16, while the structure and stylization decoder is based on ViT- Base model. We initialize the encoder, decoder, and the Gaussian center predic- tion head with pretrained weights from MASt3R [19], whereas the remaining layers are initialized randomly. The model is trained on images with resolution of 256 x 256. Besides, we use 0 degree spherical harmonics for Gaussians following [8]. Training takes ~1.5 days on NVIDIA A100 GPUs.
ArtFID +
Histogram +
Votes (%)
h3 layer h3+h4 layer
42.01
0.277
35.20
35.83
0.239
64.80
Table 5: Ablations. The ArtFID, Histogram, and voting results for h3 and h3+h4 layer.
4.1 Experimental Results
3D Stylization Results. As shown in Fig. 3. Table 2, Table 3 and Table 4, our method outperforms all baselines qualitatively and quantitatively. Visually, our stylizations achieve a more balanced trade- off between content preservation and faithful style transfer. Among test-time optimization-ban based 3D baselines, StyleRF [23] and StyleGaussian [24] often fail to reproduce the reference style's color tone accurately, resulting in overly whitened darkened outputs. ARF [54], while better capturing style colors, tends to overfit and apply excessive stylization that obscures scene details. For example, in the third row of Fig. 3, furniture in the living room becomes nearly indistinguishable due to heavy sketch-line artifacts. As a 2D baseline, Sty Tr2 [5] generates visually pleasing results on individual ground-truth novel views, but it lacks multi nuew consistency, evidenced in Table 2 and Fig. 5. In contrast, our method consistently produces superior stylizations while maintaining the best short- and long-range consistency metrics, benefiting from our attention mechanism that operates jointly over multi-view content and style tokens. Although StyleRF achieves a slightly lower RMSE in short-range evaluations, this is largely due its over-smoothed outputs, as clearly illustrated in Fig. 3.

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Input
2-view model
4-view model
Input
2-view model
4-weew model
Figure 8: Ablations. Results for 2-view and 4-view models when inputting 2 and 8 content images. The 2-view model fails to align Gaussians across multiple views when provided with 8 input views.
Figure 9: Application. Style Interpolation with 2 style images by interpolating their style tokens. It can be observed that the style the scene smoothly transit from one to another.
Cross-Date Data Generalization. To evaluate the generalization performance of our method, directly apply it to the Tanks and Temples dataset [16], a widely used benchmark prior works. As shown in Fig. 4, our model demonstrates superior performance Garden, Ignatius, and Horse which are object-centric scenes that differ significantly from the RE10K training data, outperforming existing fatte-of-the-eee methods. Notably, even though StyleRF [23] and StyleGaussian [24] are trained per scene, they fail to generalize to arbitrary style inputs. While ARF [54] achieves better results in some scenes, it requires dense, calibrated views along with per-scene and per-style optimization, limiting its practicality for time-constrained applications.
nut-of-distribution scenes such as
Novel View Synthesis. Our final model supports both stylized and standard 3D reconstruction, depending on whether the input to the appearance branch is style or content image. We report two sets of metries: one for the stylized output stulzatty)i and one for standard reconstruction without stylization fine-tuning (Ours). As shown in Table 6, Ours achieves performance comparable to NoPoSplat [49], despite not initializing the stylization decoder with pretrained weights. While Ours- stylization shows a slight drop in performance, it enables simultaneous support for both photorealistic and stylized reconstruction. Our results from 2-view RE10K models, consistent with NoPoSplat.
Stylization Time. We define stylization time as the total time from receiving the input content and style images to producing the final stylized outputs. This metric more practically reflects how quick user can obtain stylized results. For 3D methods, this includes both the reconstruction time and any stylization-related training or optimization. As shown in Table 2, our method achieves significantly faster stylization time than all existing 3D approaches, while approaching state-of-the. art 2D methods in terms of speed.
4.2 Ablation Studies
Identity Loss to Preserve NVS Capability. We explore the necessity of identity loss during stylization fine-tuning. can be observed Fig. that the model fails to recover the original appearance of the scene while performing NVS if we disable this loss.
Content Loss Layers. As in Sec. 3.3, using both relu3_i 1 and relu4_1 1 for the content loss better preserves structural details without sacrificing artistic expression. As shown in Fig. and Table relying solely on relu3_1 1 tends to cause the style to overwhelm the underlying scene structure.
Flexibility Number of Input Views. As discussed in Sec. 3.3, our model trained with content images demonstrates strong generalization, effectively handling between 2 to 8 input views. As in Fig. 8, both the 2-view and 4-view models produce satisfactory stylizations when given only content images. However, when the input is increased to 8 content images, the 2-view model struggles to align Gaussians across views, resulting in duplicated artifacts such as multiple pillars and sofas. In contrast, the 4-view model performs remarkably well, despite never being trained with 8-view inputs.

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4.3 Application
Style Interpolation. We demonstrate an application of our model, style interpolation, in Fig. 9. Specifically, we interpolate the style tokens from two reference style images before passing them to the stylization decoder, producing a blended stylization that smoothly transitions between the two styles. This approach can be easily extended more than two styles by simply computing weighted sum of their respective style tokens.
Discussion
To clarify the motivation of method, provide detailed comparison between two-stage approaches, which first reconstruct 3DGS and then perform stylization, and our single-pass method that achieves 3D stylization in end-to-end manner."
Two-stage methods:
(+) High-fidelity geometry: The 3DGS model is optimized directly to fit the target scene from dense image inputs, resulting in accurate geometry and detailed structures.
(+) Scalable to large scenes: With sufficient optimization time and resources, this approach can be extended to larger-scale environments.
(-) No instant stylization support: There is currently method that directly takes trained
3DGS and outputs a stylized version without additional test-time optimization. Even the closest work (e.g.. StyleGaussian) requires prot-procsssing like VGG feature embedding.
(-) Inefficient: The optimization-hean pipeline is unsuitable for interactive or time- sensitive applications such AR mobile capture.
(-) Requires expert setup: Users must collect dense, accurately posed input images to fit 3DGS properly, limiting accessibility for casual creators.
Single-pass methods (ours):
The model runs in single forward pass with no test-time optimization, enabling nearly real-time 3D stylization iddea for interactive or mobile scenarios.
(+) Fast inference:
(+) User-friendly: The pipeline accepts small set of casually captured images and produces a stylized 3D scene in an end-to-end manner, making it accessible to non-cxperts.
(-) Data-hungry: Training requires large and diverse dataset generalize across various scene types and artistic styles.
(-) Lower reconstruction quality: ferw-forward models typically prioritize speed and generalization over precise geometric fidelity.
(-) Limited scalability: Current implementations may face memory constraints (e.g.,
VRAM bottlenecks) and require architectural adjustments more training data to handle large-scale environments.
Conclusion
This paper introduces a feed-forw forward network for instant 3D stylization from sparse, unposed input views and single reference style image, which generalizes arbitrary scenes and styles without test- time optimization. The network is composed of structure branch and an appearance branch, jointly enabling consistent novel view synthesis and stylization. Extensive experiments demonstrate that our method outperforms existing baselines in zero-shot stylization quality, while achieving significantly faster inference speed, making more practical for real-world and interactive applications. is worth noting that our method currently supports only static scenes; extending it to handle dynamic scenarios an important direction for future work.
A Acknowleggements. grant from the Westlake University-Muuye Joint Research Institute, and in part by the Westlake Education Foundation.
This work was supported in part by NSFC under Grant 62202389, in part by
Throughout this section, "(+)" and "(-)" denote advantages and disadvantages, respectively.
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16. Declaration of LLM usage
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Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: The core method development in this research does not involve LLMs.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, non-standard components.
Please refer to our LLM policy (https htt////eerpppp.pe http///apap..////oeeeeeeeeeeeeee 22/p:///ee.oeeeeeeee/22///////// for what should or should not be described.
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Appendix
In the suppiementary, we provide the following:
more implementation details, including architecture and training hyperparameters for our model in Sec. A;
more technical details training 2D and 3D baselines in Sec. A; more visual results of our model and comparisons with baselines in Sec. B;
limitations of our model in Sec. C.
We highly recommend visiting project website for comprehensive demonstration of our method's 'ss stylization quality and temporal consistency. The website features:
qualitative comparisons with baselines on the RE1OK [56] and Tanks and Temples [16] datasets;
more out-of-domain stylization results the Tanks and Temples and NeRF LLFF [26] datasets;
style interpolation examples showcasing transitions between three different styles.
A More Implementation Details
Training. In terms of optimization, we employ AdamW optimizer. For Novel View Synthesis (NVS) pretraining, we train the stylization decoder, color head and structure head with initial learning rate of 2 x 10 and fine-tune the other parameters with 2 10-5, Then for stylization fine-tuning, we continue optimizing the color head and stylization decoder with initial learning rate of 2 x 10 and fine-tune only the style encoder with 2 10-5, and keep all the other parameters in the structure branch fixed.
Architecture. To expedite the inference of network, we use the flash attention implementation from xFormers [18] in all of encoders and decoders. As in [49], we feed the tokens from the 1-st, 7-th, 10-th and 13-rd block into DPT [33] for upsampling.
Baselines Training. For the 2D methods (StyTr2 [5], AdaIN [12], AdaAtN [25]), we directly utilize their publicly released pretrained checkpoints, available at StyTr2 code. AdaIN code, and AdaAtN code, respectively. As these methods do not support 3D reconstruction from 2D) images, we apply them directly to stylize the ground-truth 2D novel views, bypassing any reconstruction process.
In contrast, the 3D methods (ARF [54], StyleRF [23], StyleGaussian [24]) are unable to reconstruct geometry from sparse, unposed inputs. Therefore, we train them using all available scene images (on average, more than 100 per scene) along with their corresponding camera poses. This setup results in an unfair comparison with our method, which operates on sparse and unposed inputs.
B More Visual Results
We present additional qualitative results in Fig. 10, Fig. 11, Fig. 12, Fig. 13, and Fig. 14, which highlight the superior performance our method compared to prior state-of-the-art style transfer approaches. While existing 3D methods rely densely posed images, approach enables instant 3D stylization across arbitrary scenes and styles without such constraints.
More Comparisons with Baselines. To better showcase the superiority of our method, we visualize more comparison results with 3D bascline methods on different scenes and styles, as shown in Fig. 10.
More Visual Results of Our Method. To validate our method is compatible with arbitrary scenes and styles, we show stylization results with exhaustive combinations from randomly selected scenes and styles, as shown in Fig. 11, Fig. 12, Fig. 13 and Fig. 14.
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C Limitations
Inherited from the base model [49], our method is currently limited 2-8 input views and low- resolution scenarios (256 256). The former limitation could be alleviated by replacing the base model with VGGT [43], while the latter could be partially mitigated by fine-tuning the current model on higher-resolution data. However, ultra-highrresolution scenarios remain challenging due to VRAM constraints.
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DO
Style
Content
OUrS
StyleRF
StyleGaussian
ARF
Figure 10: Novel View Transfer Comparision REIOK. Our method faithfully preserves style and scene structure, even with limited image overlap. In contrast, StyleRF [23] and StyleGaussian [24] produce ovor-smoothed results with inaccurate color tones, while ARF [54] suffers from style overflow.
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Content
Style
Figure 11: Additional Results on RE10K from Our Method.
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Content
Style
Figure 12; Additional Results on RE10K from Our Method.
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Content
Style
Figure 13: Additional Results on RE10K from Our Method.
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Content
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Figure 14: Additional Results on RE10K from Our Method.
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Reviews:
[{"review_id": "zZK9R7pljG", "reviewer": "Reviewer_BqGQ", "summary": "This paper proposes a feed-forward network called Styl3R, which can achieve instant 3D stylization from sparse, unposed input views and a single reference style image. The network includes a structure branch and an appearance branch, and can generalize to arbitrary scenes and styles without test-time optimization. It outperforms existing baselines in zero-shot stylization quality and has a faster inference speed, making it suitable for real-world and interactive applications.", "questions": "Could the authors provide more examples of potential downstream applications? The style interpolation example is interesting, but more applications would strengthen the paper's impact.", "limitations": "The primary concern is the limited novelty of the paper. Many of the techniques employed, such as the two-stage training and the use of content and style losses, were previously introduced in methods like StyleGaussian. The core contribution appears to be the adaptation of these existing stylization techniques to the MASt3R  framework.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The approach presented in the paper is direct and easy to follow.\n2. The proposed model architecture is well-designed, and the experiments are comprehensive.\n\nWeaknesses\n1. The model's experimental results, both qualitative and quantitative, do not show a significant improvement over ARF.\n2. The paper lacks a user study to subjectively evaluate the stylization quality.\n3. The high speed of Styl3R is not attributed to a novel architecture proposed by the authors, but rather stems from the inherent speed of the base models (DUSt3R and MASt3R ). Therefore, this speed advantage does not appear to be a direct contribution of this work.\n4. The effect shown in the ablation study for the Content Loss Layers (Figure 7) is not sufficiently convincing from the visual results alone. Could the authors provide quantitative experiments to better support this claim?", "was_revised": true, "final_justification": "After carefully reviewing the authors' rebuttal and subsequent discussions, I maintain my original assessment that the novelty of the paper is insufficient. While the authors have elaborated on their architectural designs (such as the dual-branch structure and attention-based fusion) and emphasized the \"new problem setting\" of instant 3D stylization with sparse, unposed inputs, these contributions still primarily reflect effective integration of existing reconstruction frameworks (e.g., MASt3R) and mature stylization techniques (e.g., content/style losses), rather than fundamental algorithmic innovations. The authors' responses do not adequately address my core concern regarding the limited novelty of the work. Therefore, I remain inclined to uphold the \"Borderline reject\" rating.", "rebuttal": "We sincerely thank the reviewer for recognizing the *simplicity and effectiveness of our framework*, as well as the *thoroughness of our experimental evaluation*. We have addressed the reviewer’s concerns in detail below and would be happy to clarify further if needed.\n> **W1: The model's experimental results, both qualitative and quantitative, do not show a significant improvement over ARF.**\n\nWhile we appreciate the strength of ARF as a baseline, we emphasize that **Styl3R (ours)** and ARF operate under fundamentally different assumptions, as summarized in *Table 1* in the main paper:\n- ARF requires dense, posed multi-view images, and performs per-scene and per-style test-time optimization, taking approximately 10 minutes for scene fitting and 2 minutes for stylization. If we want to try different stylization images, ARF has to repeat the time-consuming optimization procedure;\n- Styl3R, on the other hand, operates in a fully feed-forward manner, requiring only unposed sparse-view images and a style image. It predicts stylized 3D Gaussians in only 0.15 seconds, without needing any optimization or camera supervision during inference stage. It makes Styl3R better suited for real-world and interactive scenarios;\n- Furthermore, from the results of the main paper (e.g. *Table 2*) as well as following newly conducted comparisons (*Table r1*, *Table r2* as shown below), Styl3R also consistently performs better than ARF. For example, in terms of consistency metric, Styl3R achieves 0.044 vs. 0.093 of ARF (*Table 2*); in terms of run time, Styl3R requires 0.147s vs. 12mins of ARF (*Table 2*). \n\n---\n\n> **W2: The paper lacks a user study to subjectively evaluate the stylization quality.**\n\nThank you for the valuable suggestion. To complement our quantitative evaluation, we conducted a user study to subjectively assess stylization quality with 20 3D style transfer experiments: 16 comparisons against baselines (13 from RE10K and 3 from Tanks and Temple) and 4 ablation comparisons on content loss layers. For the 16 baseline cases, participants viewed seven 256×256 images: a context image, a style image, and five method outputs (Styl3R, ARF, StyleGS, StyleRF, StyTr2) in a blind, randomized order, all rendered from the same target viewpoint. For the 4 ablation cases, each set included four images: context, style, and outputs from models trained with either h3 or h3+h4 content loss layers, also shown blindly and randomly.\n\nParticipants selected the result they found to be the most harmonious combination of style and scene. The study was conducted online with volunteer participants. In total, we collected around 2,000 votes: 1,274 for RE10K, 294 for Tanks and Temple, and 392 for the ablation study.\n\nThe stylized images shown for voting were novel views synthesized by 3D methods (Styl3R, ARF, StyleGS, StyleRF), rather than the training views. For the 2D method (StyTr2), which cannot synthesize novel views, we directly fed the ground-truth novel views as input — this provides an advantage to StyTr2, making the comparison against 3D methods conservative. The result is shown below:\n\nTable r1: The voting results for stylization comparision.\n| |Styl3R|ARF|StyleGS|StyleRF|*StyTr2 (2D)*|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|Re10K votes (%)|**53.29**|14.13|6.67|1.81|24.10|\n|TnT votes (%)|**38.78**| 22.11| 11.56 |3.06|24.49|\n\nIn addition to the user study, we also employed ArtFID [1], a metric known to align well with human judgment, as it jointly considers content preservation and style fidelity. We further used the RGB-uv histogram from HistoGAN [2] to evaluate color transfer performance. The result is shown below:\n\nTable r2: The ArtFID and Histogram metrics for stylization comparison.\n|Metric|Styl3R| ARF|StyleGS|StyleRF|*StyTr2 (2D)*|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|ArtFID ↓ / Re10K|**35.12**|42.95|55.75|46.59|38.93|\n|ArtFID ↓ / TnT|**38.05**|48.98|55.03|64.81|39.24|\n|Histogram ↓ / Re10K|**0.230**|0.313 |0.465|0.507|0.241|\n|Histogram ↓ / TnT|**0.241**|0.259|0.379|0.422|0.247|\n\n➤ *Summary:* Both user voting and quantitative metrics demonstrate that Styl3R achieves better stylization performance aligned with human judgment, even on out-of-domain datasets (*TnT*).\n\n[1] Wright, Matthias, and Björn Ommer. \"ArtFID: Quantitative Evaluation of Neural Style Transfer.\" DAGM German Conference on Pattern Recognition, 2022.\n\n[2] Afifi, Mahmoud, Marcus A. Brubaker, and Michael S. Brown. \"HistoGAN: Controlling Colors of GAN-generated and Real Images via Color Histograms.\" CVPR, 2021.\n\n---\n\n> **W3: The high speed of Styl3R is not attributed to a novel architecture proposed by the authors, but rather stems from the inherent speed of the base models (DUSt3R and MASt3R ). Therefore, this speed advantage does not appear to be a direct contribution of this work.**\n\nWe acknowledge that Styl3R builds upon the architectures of DUSt3R and MASt3R for the structure branch. However, these models were not designed for or applied to the task of 3D stylization. As also agreed by reviewers *EHSJ* and *4c71*, Styl3R tackles the novel and underexplored problem of 3D scene style transfer.\n\nMoreover, our appearance branch is entirely original, designed to enable seamless switching between stylized and original appearances, and is trained from scratch. This demonstrates that arbitrary style-scene blending can be achieved in a feed-forward manner, which has not been explored previously. \n\n---\n\n> **W4: The effect shown in the ablation study for the Content Loss Layers (Figure 7) is not sufficiently convincing from the visual results alone. Could the authors provide quantitative experiments to better support this claim?**\n\nAs noted in *W2*, we conducted a user study (392 votes) and computed ArtFID and Histogram metrics to compare the h3 layer and h3+h4 combination. Results are shown below:\n\nTable r3: The ArtFID, Histogram and voting results for h3 layer and h3+h4 layer ablations.\n||ArtFID ↓|Histogram ↓|Votes (%) |\n|:-|:-:|:-:|:-:|\n|h3 layer|42.01|0.277|35.20|\n|h3+h4 layer|**35.83**|**0.239**|**64.80**|\n\n➤ *Summary:* All three metrics confirm that the adopted h3+h4 configuration yields better performance.\n\n---\n\n> **Q: Could the authors provide more examples of potential downstream applications? The style interpolation example is interesting, but more applications would strengthen the paper's impact.**\n\nWe explore text-conditioned 3D stylization, enabling Styl3R to stylize 3D scenes based on user-provided text descriptions. Because our current appearance encoder is trained solely on images, we adopt a two-stage approach to bridge the gap: first, we use ChatGPT to generate a style image from a text prompt, and then feed the generated image into Styl3R as the style input. In our evaluations, this enables Styl3R to perform 3D stylization using only text descriptions and sparse, unposed views, greatly enhancing its flexibility and real-world applicability. Moreover, extending the encoder to directly support text input is technically feasible and represents a promising direction for future work.\n\nThis line of exploration aligns with recent advances in world models, such as *HunyuanWorld 1.0* [3], which also investigates the connection between language and 3D scene stylization.\n\n[3] Tencent HunyuanWorld Team. \"HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels.\" Technical Report, ArXiv, July 2025.\n\n---\n\n> **Limitation: The primary concern is the limited novelty of the paper. Many of the techniques employed, such as the two-stage training and the use of content and style losses, were previously introduced in methods like StyleGaussian. The core contribution appears to be the adaptation of these existing stylization techniques to the MASt3R framework.**\n\nWe appreciate the reviewer’s concern and agree that some components, such as content/style losses and multi-stage training, have been explored in prior works like StyleGaussian. However, Styl3R differs significantly in both problem formulation and technical design, as outlined below:\n\n- Problem setting is novel: In our humble opinion, Styl3R is the first to address the problem of 3D scene style transfer in a feed-forward manner, which introduces a novel paradigm for 3D scene stylization problem. It is also agreed by reviewer EHSJ: \"*The paper addresses a novel problem of 3D scene style transfor using sparse input, which to the best of my knowledge, has not been explored before... The paper presents the first framework that applies feedforward networks to 3D scene stylization.*\"\n\n- The approach is novel: Styl3R introduces a token-level stylization module using transformer-based cross-attention, directly blending multi-view content and style features for globally coherent results. It is also agreed by reviewer 4c71 & EHSJ: \"*​Novel Architecture: This is the first work that extends feed-forward 3D reconstruction architecture, such as Dust3R, NoPoSplat, to 3D stylization. ​Transformer-based stylization decoder​ ensures multi-view consistency by blending global style and content features.*\" and \"*The approach is both novel and interesting.*\"\n\n- Good performance: Compared to prior works on 3D scene stylization, Styl3R also achieves higher stylization quality, better consistency, and improved efficiency.\n\n➤ *Summary:* Although Styl3R builds upon common stylization losses, its problem setting, technical innovations, and good empirical performance, make it a good step forward from prior test-time optimization based methods like StyleGaussian."}, {"review_id": "HxXWAl0k4f", "reviewer": "Reviewer_KFYQ", "summary": "The paper combines feed-forward reconstruction networks with stylization of the generated output scenes. At the core, there is a dual-branch architectures that decouples appearance and structure modeling in a joint network.\n\n2-8 input images -> stylized 3dgs as output.", "questions": "What is the motivation of combining reconstruction and stylization in a joint architecture? Why not do first reconstruction once, and then independently stylize the respective gaussians? This would have certain advantages in not having to share the capacity of the reconstruction / stylization network.\n\nThis makes it feel a bit like an academic task for a paper but practically speaking I would not expect anyone re-reconstruct everything when a new style query comes in. As outlined, I would rather expect a stylization method that operates on a given reconstruction (that would also allow to work on more complete 3DGS outputs). A discussion here would be great.", "limitations": "There is a short discussion at the end of the conclusion, but it's not very comprehensive and definitely is not a serious attempt to discuss the method's limitations. Generally, it makes it feel a bit that there is not a clear understanding of the bigger picture as the practical limitations would be very obvious.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "Strengths\n- reasonable results for the limited view point changes\n- seems the first method to combine feed-forward reconstruction with stylization \n\nWeaknesses\n- inherits all the weaknesses from early feed-forward reconstruction. Essentially, the inputs are limited to a very small number of views which leads to rather small reconstruction. For instance, in the video there is barely any view point change which makes it look rather than view interpolation than any real reconstruction.\n- the pipeline is a bit incremental; essentially, take feed-forward reconstruction techniques and include a style component. \n\n-> Overall, I'm a bit on the fence, the task is a bit artificial (see below) and the results are limited given the small number of views. On the other hand the results are not too bad, and the inference speed is quite fast.", "was_revised": true, "final_justification": "I appreciate the authors addressing several of my concerns in the rebuttal and following discussions. I'm still somewhat on the fence, but I'm now leaning mildly positive (borderline accept).\n\nHowever, in case the paper gets accepted, I would ask the authors to incorporate the promised experiments and discussions as outlined.", "rebuttal": "We sincerely thank the reviewer for recognizing the originality of our approach in combining feed-forward reconstruction with stylization, and for acknowledging the reasonable results it achieves. We have addressed the reviewer’s comments in detail below, and would be happy to clarify further if needed.\n\n> **W1: inherits all the weaknesses from early feed-forward reconstruction. Essentially, the inputs are limited to a very small number of views which leads to rather small reconstruction. For instance, in the video there is barely any view point change which makes it look rather than view interpolation than any real reconstruction.**\n\nWe agree that reconstruction from sparse, unposed views remains a challenging open problem, and Styl3R inevitably inherits some of the limitations of earlier feed-forward pipelines such as MASt3R. However, Styl3R is the first to enable stylization of arbitrary 3D scenes with arbitrary styles from sparse, unposed input views. This problem setting has not been explored by prior 3D stylization methods. We imagine Styl3R would inspire future follow-up works to handle more input views, as how Dust3R/MaSt3R inspires VGGT (CVPR 2025), which demonstrates promising 3D reconstruction performance even with many input images.\n\nOn the other hand, in our humble opinion, the viewpoint changes are actually not that small from the video. As shown between 6–30 seconds in the supplementary video, the input views have limited spatial overlap, and the time interval between the two views is around 5 seconds, indicating substantial view variation. We further tested Styl3R with more than 8 input views, and in some scenes, it successfully handles view changes up to 180 degrees. We also additionally re-train the model with random 2 to 6 view inputs like VGGT, Styl3R achieves even broader viewpoint coverage. Due to the rebuttal policy, we could not post the qualitative results here, we would include the results in the final version. \n\nAs for \"view interpolation\", Styl3R is not performing view interpolation. The model generates explicit 3D Gaussians, and the outputs are rendered from novel camera viewpoints that are unseen during training, demonstrating 3D reconstruction capability.\n\n---\n\n> **W2: the pipeline is a bit incremental; essentially, take feed-forward reconstruction techniques and include a style component.**\n\nWe acknowledge that Styl3R builds upon the architectures of DUSt3R and MASt3R for the structure branch. However, these models were not designed for or applied to the task of 3D stylization. As noted by reviewers *EHSJ* and *4c71*, Styl3R tackles the novel and underexplored problem of 3D scene style transfer.\n\nMoreover, our appearance branch is entirely novel, designed to support seamless switching between stylized and original appearances, and is trained from scratch. This design enables arbitrary style-scene blending in a purely feed-forward manner, which, to our knowledge, has not been explored before in the context of 3D stylization.\n\n---\n\n> **W3: Overall, I'm a bit on the fence, the task is a bit artificial (see below) and the results are limited given the small number of views. On the other hand the results are not too bad, and the inference speed is quite fast.**\n\nWe respectfully disagree with the comment that \"the task is a bit artificial.\" It has various application scenarios, even for some products:\n- Sparse-view 3D stylization is increasingly relevant for mobile content creation. For instance, Snap Inc. has explored 3D stylization in products like Snapchat, which supports short-form video, AR creation, and visual messaging. These are scenarios where real-time and casual stylization is essential. \n- Styl3R also has the potential to be extended to support text-conditioned 3D stylization, allowing it to generate stylized 3D scenes from user-provided text descriptions. Since our appearance encoder is image-trained, we adopt a two-stage approach as a tryout: using ChatGPT to generate a style image from the text prompt, which is then fed into Styl3R. This enables Styl3R to perform stylization from only text and sparse, unposed views, greatly enhancing flexibility and real-world applicability. It also aligns with recent advances in world models, such as *HunyuanWorld 1.0* [3], which also investigates the connection between language and 3D scene generation or stylization. \n- Since prior test-time optimization based methods require long time to get the stylized 3D content, Styl3R could also serve as a preview approach for the consumers to quickly try different style images which would best fit their tastes. Then they could potentially exploit prior methods (which are time consuming) to get an even larger stylized 3D scene.\n\nThis suggests that Styl3R contributes meaningfully to an emerging and impactful research direction.\n\n[3] Tencent HunyuanWorld Team. \"HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels.\" Technical Report, ArXiv, July 2025.\n\n> **Q1: What is the motivation of combining reconstruction and stylization in a joint architecture? Why not do first reconstruction once, and then independently stylize the respective gaussians? This would have certain advantages in not having to share the capacity of the reconstruction / stylization network.**\n\nWe choose to combine reconstruction and stylization in a joint, end-to-end architecture to better support interactive applications, such as mobile photography and AR filters, where on-the-fly switching of digital effects is often required.\n\nIn these scenarios, casual users may simply hold up their phone with modest movement and wish to apply different visual styles to the 3D scene in real time. Styl3R is currently **the only 3D stylization method** capable of handling this efficiently, as all prior methods require dense inputs and slow optimization procedures. This advantage in real-world interactivity was also recognized by *Reviewer 4c71*.\n\nRegarding network design, since stylization operates directly on 3D Gaussians, allowing the stylization module to be aware of scene structure helps maintain multi-view consistency and spatial coherence. Therefore, sharing certain components between the reconstruction and stylization branches is not only efficient, but also a principled architectural choice.\n\n---\n\n> **Q2: This makes it feel a bit like an academic task for a paper but practically speaking I would not expect anyone re-reconstruct everything when a new style query comes in. As outlined, I would rather expect a stylization method that operates on a given reconstruction (that would also allow to work on more complete 3DGS outputs). A discussion here would be great.**\n\nIf the scene images do not change and only the style image changes (i.e. re-stylization), Styl3R could do the re-stylization without reconstructing the 3D scene again.  For a given scene, we perform a single forward pass through the structure branch and cache the generated 3D Gaussians without appearance attributes. Then, for different style queries, we only need to run the appearance branch, which efficiently updates the stylized appearance while keeping the scene geometry fixed. This also highlights the motivation/advantage behind our dual-branch design, which cleanly separates structure and appearance for efficient re-stylization.\n\nWe agree that a stylization method that operates directly on a complete precomputed 3D Gaussians also has great values for certain scenarios. However, Styl3R has its own advantages for some other application scenarios as described in previous section, especially for time-constrained use cases (e.g. quick 3D content stylization preview etc.). In our humble opinion, we think both problems targets for different applications, they could even complement with each other for certain scenarios."}, {"review_id": "wvG1Gm8fMf", "reviewer": "Reviewer_EHSJ", "summary": "This paper addresses the problem of 3D scene stylization. The authors aim to develop a method that avoids test-time optimization and requires only sparse input images without pose annotations. Building on recent feed-forward reconstruction models, they propose a two-branch architecture that separately models structure and appearance. An identity loss is introduced during pretraining to help the model preserve scene structure when training for stylization. The proposed method achieves high stylization quality and strong multi-view consistency, outperforming prior work on both in-domain and out-of-domain datasets.", "questions": "Why are two DPT heads used—one for predicting the center position and another for other attributes? What is the motivation behind this design choice?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper addresses a novel problem of 3D scene style transfer using sparse input without known camera poses, which, to the best of my knowledge, has not been explored before.\n\n2. While using feedforward networks for fast 3D reconstruction has recently gained attention, this paper presents the first framework that applies feedforward networks to 3D scene stylization. The approach is both novel and interesting. The proposed framework is clean and straightforward.\n\n3. Compared to prior works on 3D scene stylization, the proposed method achieves higher stylization quality, better consistency, and improved efficiency under sparse-view settings.\n\nWeaknesses and Concerns:\n1. The paper demonstrates that the model can be tested with 2–8 views. Does training require a fixed number of input images, or is the model trained with variable input sizes? How does the model generalize across different numbers of views? Was this also explored in NoPoSplat?\n\n2. Since the method operates with only 2–8 input images, the viewpoint diversity in the results is quite limited. The paper could be strengthened by showing that increasing the number of input views leads to more diverse outputs, possibly even covering 360-degree viewpoints.\n\n3. In the qualitative results in Figure 3, the second and third rows contain some dark regions, and compared to the 2D style transfer output from StyTr2, the 3D method appears to produce slightly lower stylization quality. Could the authors explain why the proposed method may fall short of matching 2D stylization quality in such cases?", "was_revised": true, "final_justification": "Thank you to the authors for the detailed response. My questions have been well addressed, specifically: 1) View coverage: the authors demonstrate that their method can generate results covering 180 degrees of a scene; 2) Stylization quality: the qualitative metrics show clear improvements. In my view, the paper leverages existing techniques to tackle a novel task, which constitutes a meaningful form of innovation. I believe this work could be a valuable contribution to the field.", "rebuttal": "We sincerely thank the reviewer for recognizing the *novelty of our problem setting*, as well as the *simplicity and effectiveness of our proposed framework*. We take all comments seriously and have addressed the reviewer’s concerns in detail below. Please let us know if any questions remain — we would be happy to provide further clarification.\n\n> **W1: The paper demonstrates that the model can be tested with 2–8 views. Does training require a fixed number of input images, or is the model trained with variable input sizes? How does the model generalize across different numbers of views? Was this also explored in NoPoSplat?**\n\nOur model is trained with 4 input views and demonstrates flexible generalization to a range of 2 to 8 views. While NoPoSplat briefly mentions generalization with 3 input views in its appendix, it does not thoroughly explore scenarios with more views in their paper.\n\n---\n\n> **W2: Since the method operates with only 2–8 input images, the viewpoint diversity in the results is quite limited. The paper could be strengthened by showing that increasing the number of input views leads to more diverse outputs, possibly even covering 360-degree viewpoints.**\n\nAs suggested by the reviewer, we have tested our pretrained model with 12 input images, the model can effectively cover approximately 180 degrees of viewpoint for some scenes, but is currently unable to handle full 360-degree coverage. This limitation could potentially be mitigated by training the model with an even larger dataset containing richer camera viewpoints. However, due to constraints on the available datasets and time limit during this rebuttal period, we were unable to pursue this direction and would try to include in the final version.\n\n---\n\n> **W3: In the qualitative results in Figure 3, the second and third rows contain some dark regions, and compared to the 2D style transfer output from StyTr2, the 3D method appears to produce slightly lower stylization quality. Could the authors explain why the proposed method may fall short of matching 2D stylization quality in such cases?**\n\nThank you for pointing this out—indeed, the 3D and 2D methods exhibit different characteristics in their stylization results.\nIn the second and third rows, the 3D method (Styl3R) tends to better preserve the original appearance of the scene, including shading and structural details. It could be caused by the the usage of content image as style image (i.e. identity loss)  during training, to enable the model to also have the normal NVS capability. In contrast, the 2D method (StyTr2) does not have such restrictions, and leans more heavily toward the style image’s appearance, which can result in the loss of scene structure like faded edges, as seen in those rows. \n\nWe also exploit more commonly used metrics to compare both methods. In particular, we additionally report quantitative metrics computed specifically for the stylization results shown in the second and third rows. For the 2D method (StyTr2), which cannot synthesize novel views, we used ground-truth novel views as input. This actually gives StyTr2 an advantage and makes the comparison with the 3D method conservative.\n\nTable r4: The ArtFID and Histogram results for the second and third row.\n|             | **Styl3R** | **StyTr2 (2D)** |\n|:------------|:----------:|:-------------:|\n| ArtFID ↓ (2nd)|   **29.42**    |     29.91    |\n| ArtFID ↓ (3rd)|   **26.94**    |     28.73     |\n| Histogram ↓(2nd)|   **0.214**    |     0.216     |\n| Histogram ↓(3rd)|   **0.148**    |     0.231     |\n\nAs shown above, our method performs slightly better than the 2D baseline in both ArtFID and Histogram metrics.\n\n---\n\n> **Q: Why are two DPT heads used—one for predicting the center position and another for other attributes? What is the motivation behind this design choice?**\n\nWe leverage the pretrained weights of MASt3R, thus this separation can make us effectively use the pretrained center head from MASt3R without the need of training a new head from scratch."}, {"review_id": "x3TKBTxNuL", "reviewer": "Reviewer_4c71", "summary": "The paper presents ​Styl3R, a feed-forward network for ​instant 3D stylization​ that operates on ​sparse, unposed input views​ (2–8 images) and an arbitrary style image. ​Styl3R utilizes a pipeline similar to NoPoSplat, reconstructing and stylizing 3D scenes in ​under 0.15 seconds​ without test-time optimization or per-scene fine-tuning. The method employs a ​dual-branch architecture​ to decouple structure (3D Gaussian parameters) and appearance (stylized colors), ensuring multi-view consistency while preserving scene geometry. A transformer-based stylization decoder blends style features with multi-view content tokens, and training involves a two-stage curriculum: pre-training for novel view synthesis followed by stylization fine-tuning with an identity loss to retain photorealistic capabilities. Evaluations on multiple datasets demonstrate superior efficiency and quality over prior 3D methods (e.g., StyleRF, ARF) and 2D baselines.", "questions": "1. Can the method adjust the stylization strength during inference (e.g., interpolating between original and fully stylized content)?\n2. Could the method handle HD/4K resolution inputs, and if so, what architectural modifications would be required?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. ​Speed & Practicality: Achieves stylization in ​0.15s, making it suitable for interactive applications. Eliminates the need for dense inputs, camera poses, or per-scene/style optimization.\n2. ​Novel Architecture: This is the first work that extends feed-forward 3D reconstruction architecture, such as Dust3R, NoPoSplat, to 3D stylization. ​Transformer-based stylization decoder​ ensures multi-view consistency by blending global style and content features.\n3. ​Strong Empirical Results: Outperforms 3D baselines in ​zero-shot stylization​ and matches optimization-heavy methods (e.g., ARF) without their computational overhead. Preserves ​NVS capabilities​ even after stylization fine-tuning (via identity loss).\n\n\nWeaknesses:​​\n\n1. ​Resolution and aspect ratio limitations:​​ The method is constrained to a fixed 256×256 resolution for both input views and stylized novel views, which restricts its applicability to higher-resolution scenarios.\n2. ​Scene scale and camera movement constraints:​​ Based on the presented results, the method appears limited to small-scale scenes and demonstrates only modest camera movements in the rendered stylized views. Unlike approaches such as StyleGaussian, it cannot support free viewpoint exploration of larger environments.", "was_revised": true, "final_justification": "I appreciate their additional experiments on different resolutions, which demonstrate Styl3R's ability to handle higher-resolution inputs. While Styl3R does have limitations—such as being constrained to a limited number of input views and small-scale scenes—I believe the paper's contributions are strong enough to advance the field of 3D stylization. Styl3R's ability to generalize across scenes and styles while performing stylization at high speed is a significant contribution.", "rebuttal": "We sincerely thank the reviewer for recognizing the *practicality of our work in interactive applications*, the *novelty of our architecture*, and the *quality of our results*. We take all comments seriously and have addressed the reviewer’s concerns in detail below. If there are any remaining questions, we would be happy to clarify further.\n\n> **W1: The method is constrained to a fixed 256×256 resolution for both input views and stylized novel views, which restricts its applicability to higher-resolution scenarios.**\n\nThis is not an inherent limitation of our method, but rather a design choice made for faster prototyping. To accelerate development, we trained and tested our model on a lower resolution (256×256). However, our framework is resolution-agnostic and can be scaled up by simply feeding in higher-resolution images.\n\nTo evaluate this, we tested the model (trained on 256×256) with input resolutions of 256×352, 352×352, and 352×640. The model produced reasonable results on the first two resolutions, which are closer to the training resolution, and exhibited only slight degradation at 352×640.\n\nWe then fine-tuned the model on 352×640 images, which are not only higher in resolution but also differ in aspect ratio. After fine-tuning, the model produced high-quality stylization results, comparable to those results achieved at the lower resolution. This demonstrates the scalability of our method and its potential to handle even higher-resolution scenarios. We would include the results into the final version.\n\n---\n\n> **W2: Based on the presented results, the method appears limited to small-scale scenes and demonstrates only modest camera movements in the rendered stylized views. Unlike approaches such as StyleGaussian, it cannot support free viewpoint exploration of larger environments.**\n\nIt indeeds a limitation (limited number of inputs) of our current model, compared to StyleGaussian. However, Styl3R aims for different scenarios, especially for time-constrained applications (e.g. quick 3D content stylization preview), where StyleGaussian struggles to get the stylized scene quickly. Furthermore, benefit from the fast development of feed-forward 3D reconstruction methods (e.g. a concurrent work from this years CVPR, VGGT could handle more than 100 images), we would foresee Styl3R would inspire follow-up works to handle large-scale scenes in an efficient feed-forward manner in future.\n\n---\n\n> **Q1: Can the method adjust the stylization strength during inference (e.g., interpolating between original and fully stylized content)?**\n\nYes, our method allows adjustment of stylization strength at inference time. Similar to style interpolation, we can fix the output of the structure branch and control the stylization level of the appearance by interpolating between the style tokens encoded from the original scene image and the style image.\n\nIn a preliminary test, we performed linear interpolation of the style tokens using equal weights (0.5) between the original scene and the style image. The resulting reconstruction exhibits an appearance that lies between the original and fully stylized scenes, demonstrating the model’s ability to support controllable stylization.\n\n---\n\n> **Q2: Could the method handle HD/4K resolution inputs, and if so, what architectural modifications would be required?**\n\nNo, our current model cannot handle HD or 4K resolution inputs without modifying its configuration. Due to the use of a patch size of 16×16, we are limited to a batch size of 2 at a resolution of 352×640 on an A100 GPU with 80GB of VRAM. Simply feeding HD-resolution images would exceed the memory capacity of commonly available GPUs.\n\nTo scale up to HD or 4K resolutions, one straightforward approach would be to increase the patch size, thereby reducing the number of tokens extracted from the input images and lowering memory consumption. A more advanced solution would involve applying a chunking strategy [4][5], where the high-resolution image is divided into smaller overlapping chunks, processed independently, and then the resulting Gaussians from each chunk are aligned during post-inference.\n\n[4] Galerne, Bruno, et al. \"Scaling Painting Style Transfer.\" Computer Graphics Forum. Vol. 43. No. 4. 2024.  \n[5] Galerne, Bruno, et al. \"SGSST: Scaling Gaussian Splatting Style Transfer.\" CVPR. 2025."}]

Author rebuttal:

Meta-review: Styl3R is a feed-forward network for instant 3D stylization from sparse, unposed input views and a single style image. 
The method employs a dual-branch architecture to disentangle structure and appearance, with a transformer-based decoder and identity loss to ensure multi-view consistency. It achieves fast inference, requires no test-time optimization and demonstrates strong stylization quality and consistency compared to prior baselines.

The work demonstrates clear improvements over existing 3D stylization methods, especially in zero-shot generalization and efficiency, making it suitable for interactive applications. Reviewers found the problem setting novel and impactful, and appreciate the network architecture and strong empirical results. 

Concerns remain around technical novelty (integration of existing reconstruction + stylization components), limited viewpoint diversity due to sparse inputs, and dependency on base model speed.

Nonetheless, the paper proposes a practical pipeline that addresses a novel task, outperforms reasonable baselines, and is likely to influence future 3D stylization research. After rebuttal, reviewers acknowledged that their concerns were largely addressed, and the consensus shifted positively.

Hence, I recommend accepting the submission as a poster.
Decision: Accept (poster)