CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Authors: Kornel Howil
Joanna Waczynska
Piotr Borycki
Tadeusz Dziarmaga
Marcin Mazur
Przemysław Spurek
Venue: NeurIPS 2025 poster ()
Keywords: Style Transfer
3D
4D
Video
Image
CLIP
Gaussian Splatting
Primary area: general_machine_learning
TL;DR: In this work, we propose CLIPGaussians, a novel approach to style transfer as a plug-in model to the Gaussian splatting

Abstract: Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based representations, especially beyond simple color changes, remains challenging. In this work, we introduce CLIPGaussian, the first unified style transfer framework that supports text- and image-guided stylization across multiple modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates directly on Gaussian primitives and integrates into existing GS pipelines as a plug-in module, without requiring large generative models or retraining from scratch. The CLIPGaussian approach enables joint optimization of color and geometry in 3D and 4D settings, and achieves temporal coherence in videos, while preserving the model size. We demonstrate superior style fidelity and consistency across all tasks, validating CLIPGaussian as a universal and efficient solution for multimodal style transfer.

CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Kornel Howil 1.3+
Joanna Waczyńska 1,2+
Piotr Borycki
Tadeusz Dziarmaga'
Marcin Mazur
Przemyslaw Spurek
4D
3D
2D
Video
Figure 1: We present CLIPGaussian, universal model for style transfer that supports wide range of data modalities, including images, videos, 3D objects, and 4D dynamic scenes. Style transfer in CLIPGaussian can be guided using image or a text prompt. Our method leverages Gaussian Splatting representation to model both color and geometric aspects of style transfer.
Abstract
Gaussian Splatting (GS) has recently emerged efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer GS-based representations, especially beyond simple color changes, remains challenging. this work, we introduce CLIPGaussian, the first unified style transfer framework that supports text- and image-guided stylization across multiple modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates directly Gaussian primitives and integrates into existing GS pipelines as plug-in module, without requiring large generative models or retraining from scratch. The CLIPGaussian approach enables joint optimization of color and geometry in 3D and 4D settings, and achieves temporal coherence in videos, while preserving the model size. We demonstrate superior style fidelity and consistency across all tasks, validating CLIPGaussian as a universal and efficient solution for multimodal style transfer.
"qqual contribution
Jagiellonian University, Faculty of Mathematics and Computer Science; "Jagiellonian University, Doctoral School of Exact and Natural Sciences; "IDEAS Rescarch Institute.
Correspondence to: Komel Howil <kornel. how www.hhottwnnnn..ccoooo edu pl>, Przemystaw Spurck
<przemyslav. spurekeuj.edu..
Project page: http:///////eeeeeeooo.oooooooooa htt..l/ggiii.i.ooL//AAaaaaaa
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
The past year has seen an explosive rise in user-driven content generation, particularly in the visual domain. Following the launch of OpenAl's GPT-4o (1. users created more than 700 million images within a single week, highlighting the massive demand for generative tools that allow intuitive, direct manipulation of visual content. Although 2D image generation and edition are rapidly becoming mainstream tasks, editing in higher dimensions, such as video, 3D, and 4D content, is significantly more complex [2]. These domains bring challenges in consistency, geometry, temporal coherence, and user control that existing systems are not yet fully equipped to handle.
Gaussian Splatting (GS) 3 is major advancement in computer graphics, representing 3D scenes as sets of Gaussian components with color and opacity. Its training and rendering are highly efficient and produce realistic images. GS has also been adapted for 4D dynamic scenes 4 | 66. 2D images [7] 8). and videos
[9 [0
using slightly modified Gaussian components to model 2D content.
One of the ways to edit objects represented by 3D Gaussian primitives is style transfer Mл. which alters the global appearance of objects and scenes. A possible approach to address this issue the use of plug-in -n-type models. This refers to family of components that can be inserted into existing architectures without requiring complete retraining [12] allowing them to adapt to new tasks such as style transfer.
In this paper, we introduce CLIPGaussian, a plug-in type model suitable for Gaussian Splatting bassd architectures. Our model stylizes content represented by Gaussian primitives, conditioned on either a reference image or text prompt, across 2D, video, 3D, and 4D data, see Fig. [ As a plug-in component, CLIPGaussian integrates into existing pipelines without requiring retraining of the base model.
When conditioned on text prompt, models based on Gaussian Splatting primarily focus on editing rather than style transfer [13] [4 [5). the context of conditioning on a reference image. models typ- ically concentrate on modifying only color and opacity. Examples of this include StyleGaussian [16]. ReGS 177, InstantStyle
[18]. Style3D 19, and StyleSplat (20. G-Style (21 employs a two-phase process, consisting of stylization followed by refining the scene's is geometry, but increasing the size of the model significantly. Since our CLIPGaussian uses the architecture of the base model, we can optimize all parameters, without changing the model size. By operating directly within the model, our method enables end-to-end optimization of the full set of Gaussian parameters including position and scale, rather than being restricted to coser-based edits, see Fig. 2 Crucially, we retain the original number of Gaussian primitives, thereby preserving the memory and computational characteristics of the object's reconstruction.
architecture of the Gaussian Splatting-bar
In this work, we make the following primary contributions:
Universal Multimodal Stylization: We propose CLIPGaussian, the first plug-in style transfer model for Gaussian Splatting, enabling image- and text-guided stylization across 2D, video, 3D, and 4D data without retraining the base model.
Joint Appearance and Geometry Optimization: Our method allows end-to-end opti- mization of all Gaussian parameters, enabling geometric transformations and temporally consistent results, while preserving the original model size.
Generalizable GS-based Stylization Framework: We demonstrate that Gaussian Splatting is a versatile substrate for style transfer, achieving strong performance across tasks and modalities with unified architecture.
2
Related Works
CLIPGaussian operates across diverse data modalities, including images, videos, and 3D or 4D scenes. To our knowledge, it is the first method capable of style transfer across such wide range. As a result, direct comparison with existing work is challenging, so we evaluate our model separately for each modality.
3D Our model uses Gaussian Splatting to represent various data modalities. Therefore, the closest solutions are dedicated to 3D scenes. In the case of classical Gaussian Splatting, style transfer methods usually work only on colors [16 17 18 19 20). means that such algorithms do not change the geometry of objects. Therefore, the only modification is to the colors and opacities of Gaussian

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Figure 2: CLIPGaussian architecture in the case of 4D dynamic scene. The method operates in two main stages. In the first stage, we train a Gaussian Splatting model tailored to a specific data modality. In the second stage, during training, we leverage training images, randomly sampled patches, and conditioning inputs (either an image or a text) in the feature spaces of VGG-19 and CLIP models. We optimize the Gaussian parameters using a composite loss function with four terms: content preservation, background preservation, local style transfer, and global style transfer. Notably, CLIPGaussian integrates with GS-based systems plug-in module.
primitives. StyleGaussian 16 embeds 2D VGG features into 3D Gaussians, transforms them based on a style image, and decodes them with a novel KNN-based 3D CNN. Such a model can be seen as an adaptation of AdaIN [22] to 3D Gaussian Splatting. StyleSplat [20] segments individual objects,
then fine-tunes their appearance using feature matching with style image, allowing customizable, multi-object stylization with strong visual consistency and efficiency. ReGS [17] introduces a texture- guided control mechanism for fine- grained appearance editing. It enhances style detail by replacing selected Gaussians with denser ones, guided by texture cues and regularized by depth to preserve geometry. G-Style 22 is a 3D scene stylization algorithm that enhances Gaussian Splatting by optimizing both appearance and geometry to match reference style image. It improves visual quality by preprocessing out problematic Gaussians, applying multi-scale style losses, and refining geometry through gradient-based Gaussian Splatting.
Alternatively, we can use a large diffusion model for the style transfer of 3D models. InstantStyleGaus- sian [8] uses diffusion models and an iterative dataset update strategy. It stylizes pre-reconstructed scenes by generating target-style images, updating the training data, and optimizing the scene effi- ciently, achieving high-quality results with improved speed and consistency. Style3D [19] introduces MultiFusion Attention align structural and stylistic features across views, ensuring spatial coherence and visual fidelity, and uses a large 3D reconstruction model for high-quality, efficient stylization. Morpheus 23 introduces a novel autoregressive 3D Gaussian Splatting stylization method that enables controllable stylization of both appearance and geometry in 3D scenes. Instruct-GS2GS (I-GS2GS) 13 performs style transfer by applying liffusion-baser model to each of the input images used to train a Gaussian Splatting. Each splat is then fine-tuned using these stylized images. In contrast CLIPGaussian instead of stylizing the training images, directly optimizes the Gaussian representation using losses derived from CLIP embeddings. This approach enables high-quality style transfer and supports conditioning on both images and text prompts, whereas I-GS2GS supports only text- based conditioning. Importantly, CLIPGaussian does not rely on any pretrained diffusion models for image stylization; the entire process is guided solely by CLIP-based objectives. The key contribution is an RGBD diffusion model allowing users to adjust stylization strength over shape and look. Such task is closer to 3D scene editing than style transfer like in DGE [4]. GaussCltr [15), ProGDF 24 EditSplat 25
4D
One of the most underexplored modalities is the 4D scene stylization which aims to modify the appearance of the 3D scene over time. Existing approaches remain limited. Currently, to our knowledge there are only just a few 4D style transfer models.
4DStyleGaussian (26 uses reversible neural network to train 4D embedded Gaussians, preserving content fidelity while reducing feature distillation artifacts. A learned 4D style transformation matrix enables consistent stylization across views and time. Instruct 4D-to-4D 27 is a NeRF- based method for instruction- -uided editing of dynamic scenes. It enhances 2D diffusion models with 4D awareness and spatial-temporal consistency. Treating 4D scenes as pseudo-3D combines anchor-aware attention, optical flow-guided appearance propagation, and depth-based projection to enable consistent, high- quality edits across time and viewpoints. While both methods focus on scenes, we show that our
3

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method also works very well on objects without backgrounds, see Fig. 3 StyleDyRF [28 is also NeRF-based method. The Canonical Feature Volume and Canonical Style Transformation are introduced to create compact representation of the 4D scene, and learn the linear transformation to reflect the reference style respectively. As opposed to our method, it supports only image based style conditioning.
Images In contrast 4D scenes, 2D image style transfer is a very popular task. The foun- dational work in neural style transfer [29] intro- duced an optimization- based method to match the content features of one image and the style captured using Gram matrices of another, us- ing pre-trained convolutional neural model. This demonstrated that deep features could dis- entangle content and style, but the approach was computationally expensive due to its iterative na- ture. To address speed limitations, feed-forve forward networks were introduced to enable real-time stylization by applying a fixed style in a sin- gle pass [3 B1I. Arbitrary style transfer was later made possible by aligning feature statis- ties using Adaptive Instance Normalization (22), further refined through Whitening and Coloring Transform [32]. More recent methods have ex- plored more flexible conditioning mechanisms. For image-guided style, transformer-based ar- chitectures like StyTr² leverage both local and global context to improve stylization quality and content preservation have emerged as an intuitive way to specify style. Style transfer guided by CLIP embeddings enables stylization from a textual description [34] while more efficient approaches learn lightweight style representations for feed-forware transfer
"Wood"
"Spring"
"Baroque"
"Honey"
"Fur"
"Marble"
Figure 3: Results of text-based 4D style transfer. Our model modifies both the color and geometry of Gaussian primitives.
3
Alternatively, text prompts
35
Videos The majority of image-style transfer methods 36
37
employed for video-style 38
transfer. Linear (36) proposes a fast and flexible style transfer method using a learned transformation matrix. It replaces costly handcrafted operations with feed forward model. CCPL [77 uses novel Contrastive Coherence Preserving Loss to reduce local inconsistencies, maintaining temporal coherence without harming style quality. UniVST (38 a unified, training-free video style transfer framework based on diffusion models that focuses on localized stylization. It uses a point-matching
mask propagation strategy to avoid the need for tracking models. AdaAtN (39] introduces Adaptive Attention Normalization, which addresses the problem of local distortions by performing attentive normalization on a per-porre point basis by jointly considering shallow and deep features of content and style images. VST 40) introduces zero-shot video style transfer framework with novel relaxation of the style loss and a new temporal regularization. ViSt3D (41 introduces the first video style transfer method to use 3D CNNs directly, which works by disentangling motion and appearance features to apply the style only to the appearance before re-adding the motion.
Alternatively, we can use large generative models for style transfer on videos
In 45),
the authors adapt large text-to-image diffusion models for video generation, addressing the challenge of temporal consistency. The framework consists of two parts: key frame translation and full video translation. Style-a-video [46 zero-shot video stylization method that uses pre-trained image latent diffusion model and generative transformer for text-guided style transfer. UniST 47 introduces the Domain Interaction Transformer (DIT), which enables cross-domain learning by sharing contextual information between images and videos. StyleMaster [48] improves style consistency and temporal coherence by filtering content-related patches while retaining style ones. The method also supports image-to-viden stylization by employing a lightweight motion adapter trained on still videos.
Each of the above approaches has issues with optical flow, so a dedicated mechanism is necessary to create smooth style transitions between frames. Our method works directly on Gaussian components, which inherently addresses these problems.

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La Carysber U.30
& Style
1-GS2CS [K]
DGB I
CLIPGassian
Savy
by
by
Figure We conduct a user study, comparing our model against base- line methods. CLIPGaussian achieves scores comparable to G-Style with im- age conditioning and outperforms all models when using text prompts.
Figure 5: Comparison of various 3D style transfer meth- ods involving text conditioning. CLIPGaussian applies style transfer with more significant shape changes. Our model captures details by attending to local regions through patches processing.
3 CLIPGaussin: Universal and Multimodal Style Transfer Based on Gaussian Splatting
This section introduces our CLIPGaussian model, which is designed for style transfer across different data modalities: images, videos, and 3D or 4D scenes. CLIPGaussian a universal method that works as plug-in for Gaussian Splatting- parameters of the Gaussian components using the CLIP model [49], which evaluates the similarity between natural language and images. As a result, our model is agnostic not only to the modality of data, but also to the conditioning mechanism, allowing the style to be transferred from either an image text.
representations. The core idea is to fine-tune the
The core of our approach is represent all data modalities using Gaussian components. This unified representation facilitates style transfer across images, videos, and 3D) or 4D scenes by modifying the corresponding Gaussian primitives. Although each modality introduces specific variations- such as temporal embeddings for videos and 4D scenes our framework supports a single, general architecture for style transfer.
Training Dataset The input data for CLIPGaussian are similar to those of typical style transfer models, but they vary slightly depending on the specific task. When applying a style to image, the input is simply a single image. For videos, the input consists of all frames, which can be treated as a sequence of 2D images. For 3D scenes, views from different camera positions are used. Finally, for 4D objects, temporal indices for these views are also included. Despite the mentioned differences, we can unify our framework by assuming that the training dataset consists of set of Additionally, we have a style given by an image or text prompt, which
images I = (J......I, can be seen as a conditioning factor S.
I&
Base Model: Gaussian Splatting Representation of Various Data Modalities Our model can be seen as plug-in for Gaussian Splatting representations. For this purpose, we work on GS-based general representation consisting of a set of Gaussians:
G = X=Gm, = (W(m,, C,,,,,,,, 0:))..
where m. are the mean positions, Σ, are the covariance matrices, o; the opacities, and e; are the Spherical Harmonics (SH) colors of the Gaussian components- are standard GS parameters. On the other hand, 0, are additional parameters dedicated to specific data modalities related to an applied base model, which can be any of the available GS based architectures. In this paper, we use the classical 3DGS [3] for 3D scenes, D-MiSo (6) for 4D scenes, MiRaGe [6] for 2D images, and VeGaS [10] for videos. A detailed description of each case can be found in Appendix[A
The first stage of CLIPGaussian is to train established base model on the set of input images I, which boils down to optimizing all the parameters occurring Eq. I. Note that this varies depending the chosen data modality. Once training is complete, the resulting pre-trained model
5

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Table 1: Quantitative comparison of style transfer, compared against baseline methods.
Model
CLIP-St CLIP-SIM t CLIP-Ft CLIP-CONS
Memory size
Text-conditionss
I-GS2GS 13
16.80
12.03
99.19
13.53
-36%
DGE 14
17.59
12.27
99.31
12.46
-5%
LLiiGaussin--ight
23.14
22.30
99.17
8,51
+0%
CLIPGaussian
26.86
26.31
98.80
2.34
+0%
Image-conditioned
StyleGaussian 16
63.69
13.07
98.87
1.36
+0% %
SGSST [50]
66.57
16.24
97.54
+0%
ABC-GS [1]
68.68
16.29
99.10
2.11
+0%
G-Style 21
76.94
24.94
98.94
1.31
+126%
CLJPGaussin--Light
69.27
17.02
99.16
5.26
+0% %
CLIPGaussian
72.65
20.72
98.78
1.77
+0% %
becomes the input for the second stage of our algorithm, in which a final style transfer procedure is performed.
Final Style Transfer the subsequent stage, we implement style transfer by further fine-tuning the parameters of the base model (see Eq. (T). The key idea behind CLIP- Gaussian to work on whole objects and small patches at the same time. The training procedure begins by selecting single image from the training dataset I, which is then used along with its reconstruction (I) produced by the base model G, Then, a collection of random patches ...)).............. Pm (Rg(4)) from the rendered output (G) extracted, to which random perspective augmentations are further applied (these cropped patches maintain fixed dimensions). Eventually, the parameters of G are updated according the multi-component loss function, which is elaborated the following paragraph, and the procedure is repeated from the beginning. It is important note that CLIPGaussian does not perform densification alter the number of Gaus- sian components. As a result, the stylized objects retain the same size as the original. Moreover, this property enables style interpolation by linearly interpolating the parameters of each Gaussian component.
CLIPGaussian:
- (Rg (Rg
CLIPGaussian: Loss Function Our loss function contains several compo- nents, which detailed below. How- ever, it should be noted that our ap- proach relies
Style
StyleGanssian
G-Style 21
CLIPGausian
pre-trained archi- tectures: CLIP and VGG-19, which we further denote as ΩCLIP and PyGG, re- spectively. We use the CLIP model for style transfer and the VGG- 19 model to ensure that the stylized output resem- bles the original object.
Content loss (L.) measures the similar- ity between input views and elements after style transfer. To maintain the content information of the input object, following [29], we calculate L. as the mean squared error (MSE) between the conv4_2 and conv5 2 features of the original image and rendered image (I)), extracted from pre-trained VGG-19 model, i.e., Le(Ro (Ii), .t) = MSE ( (Rg(4:), @vaa (4)).
Figure 6: Comparison of 3D style transfer obtained by image conditioning. Results generated by CLIPGaussian are more detailed.
Directional CLIP loss (I.) is responsible for global style transfer. Similar to 52 we define it as follows:
La(Rg 11) 1 (Rg(1)) - PCLIP (cup(() PPcuI("PPotoo" (3)

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It should be noted that in this case, CLIP embeddings of the original image 4 and the reconstructed image RI) in conjunction with outputs produced by the CLIP model for given style factor S
(either style image or style prompt) and a simple universal negative prompt "Photo", are utilized. Both negative and positive text prompts are combined with an ImageNet prompt template [53].
Patch CLIP loss (Lp) is responsible for local style transfer. It is defined as follows:
(R(RR(Ti), Ii) Σ Lalp, (R())(R((T))))))))
However, we emphasize that, as proposed in [4) for all patches (e( E P we apply random perspective augmentations before calculating the CLIP directional loss.
Background loss (Lş) is assigned to fixed background elements. For 3D objects and videos, a mask is typically used to distinguish between the foreground and background. We incorporate background loss to prevent the style transfer process from introducing unwanted background artifacts. Therefore, Ta is defined as the mean I.1 distance between the designated background color and the pixel values corresponding to background regions in the rendered image.
Total loss (Ltotat), which is used to fine-tune the parameters of the base model, is defined weighted sum of the multiple loss components described above. This leads to the following formula:
Listal = AaLa + ApLp + AeLe + AgLa.
(5)
where Ad, Ap. de. and X are empirically chosen weighting parameters. Details of the ablation study are provided in Appendix B For Lp and La we use ViT-B/33 CLIP model [53].
4 Experiments
The experiments section is divided into four parts, each corresponding to specific modal- ity for which the model has been applied. We note that because CLIPGaussian operates across multiple data modalities, baselines differ by task. Within each task, we standardize the setup for fair comparisons. For comparison, we include two versions of our method which differ in hy- perparameters. CIIPGaassian with standard pa- which pro-
"Black marble"
rameters and CLIPGaussian- duces lighter stylization. Details of hyperparam- eters can be found in Appendix A Additional experiments using other datasets and ablation studies are presented in Appendix B For all 2D, 3D, Videos, 4D-objects experiments, we used the NVIDIA RTX 4090 GPU, For 4D-scenes we used NVIDIA DGX A100 GPU. The code is available on GitHub
GGreen crystal'
Figure 7: Style interpolation between "Black mar- ble" and "Green crystal" styles the lego object.
3D As our approach is designed as plug- in model compatible with Gaussian Splatting- based models, we begin our evaluation us- ing the standard, vanilla Gaussian Splatting framework. The experiments evaluate both 3D object-level and scene-level performance, The dataset [55), comprising object- centric synthetic scenes with clean geometry and no background, enables controlled assessment of reconstruction and style transfer on isolated objects. Mip-NeRF 360 (56) provides photore- alistic 360-degree real-world scenes with wide baselines, occlusions, and varying depth scales.
Style 4DStyleGaussian 26
CLIPGaussian
Figure 8: Comparative analysis of style transfer using referenced style image of novel views across various time frames and styles on the Neural 3D Video dataset (DyNeRF) [54].
7

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To assess performance on both image- and text-based stylization, we employ CLiP-based metrics. CLIP Directional Similarity [52] (CLIP-SIM) and CLIP-S [57] evaluate transfer quality, while CLIP Directional Consistency [58] (CLIP-CONS) and CLIP-- (5 assess temporal consistency and content similarity. We also report changes in model size (number of Gaussians) after stylization. Metric definitions and detailed evaluation settings are provided in Appendices B.1.1 and B.2
Tab. D compares our method with Instruct-GS2GS [3] and DGE 14 for text-conditioned style for image-conditioned style transfer. Experiments use two objects (lego, hotdog) and two scenes (garden, bonsai), each evaluated under both image- and text-driven conditions. CLIPGaussian method achieves state-of-the art results under text-guided conditions. It significantly outperforms all baselines with respect to the CLIP-S and CLIP-SIM metrics. We also achieve high-quality results under image-guided conditions. Compared to G-Style, our method offers a balance between performance and efficiency. This makes CLIPGaussian a practical and scalable alternative for style transfer tasks.
transfer and StyleGaussian [T]], SGSST [50], ABC-GS [5T] and G-Style
For selected baseline methods (Instruct-GS2GS, DGE, StyleGaussian and G-Style) and the stan- dard version of CLIPGaussian, we conducted a formal online user study on the same dataset as in the quantitative comparison. It evaluated the quality of the style transfer and identified which method best conveyed the intended style. We conducted four surveys to evaluate differ- ent methods of 3D content stylization: (1) 3D objects stylization with images, (2) 3D scenes stylization with images, (3) 3D objects styliza- tion with text, and (4) 3D scenes stylization with text. Each survey had 30 randomly selected par- ticipants, one user could participate in multiple surveys. The surveys were conducted formally using the CLICKworker platform" A detailed description of the questions is provided in Ap- pendix B.2.3 Fig. shows the results from four evaluated scenarios. In case (I), our method was most frequently selected for producing objects most similar to the reference image. In case (2), G- Style performed better on larger scenes, but our method was rated the least similar to the refer- ence image less often than StyleGaussian. Con- sidering that G-Style uses over twice many Gaussians, our method remains a competitive alternative (see Table J.. In case (3), users most often rated our approach as the best for text- based object stylization. Case (4), involving 3D scene stylization from text, yielded mixed results. Users gave high and low ratings, likely due to the subjective nature of prompt interpretation. A post hoc Conover-Friedman test showed significant differences in "perceived similarity" rankings across methods, except between our method and G-Style for object stylization.
Instruct 4D- 27
CLIPGaussian
What 1f Grigh?
Vioom' Googh petering
Mhsx If was poiteed by Elward Musch ?
Sheard Manch painthy
Aice FauMtr poteting
Figure 9: Comparative analysis of style trans- fer using text condition on the coffee_martin dataset [54].
Fig. 5 and Fig. 6 show a qualitative comparison with selected baseline methods, on a subset of data used for quantitative evaluation and user study. Full comparison is provided in Appendix B.2 Unlike other methods, CLIPGaussian does not change the number of Gaussians, which also enables style interpolation (see Fig. Additional results and train time can be found in Appendix [ B.2
4D To evaluate performance on dynamic 4D scenes, we used D-MiSo (, which integrates the Multi-Gause Multi-Gaussian Splatting representation with a deformation network. All components of the pipeline actively contribute to the learning of style transfer.
hstpp::/www.clicckorrztt
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Style & GT
AdaIN Ai
StyTr' 1
CL.IPGanssian
Style
InstructPix2Pix
ChatGPT
CLI'Gausian
Bekainski painting
Sand
Figure 10: Comparison of 2D style transfer using image condition.
Figure 11: Comparison of 2D style transfer using text condition.
Empirical evaluations were performed on well-established benchmark datasets. The neural 3D video dataset coffee_martini martini (DyNeRF) (54] provides time- symehyynchronized and calibrated multiview video sequences capturing complex 4D dynamic scenes. The D-NeRF dataset [60] consists of seven moving objects, with the constraint that only one camera view is accessible at any given time step.
For comparative evaluation, we examined CLIPGaussian against 4DStyleGaussian [26]. However, the time of submission, the official implementation of this method was not publicly available. Consequently, visual comparisons were made using qualitative results extracted directly from the
paper. Fig. g shows comparison of style editing results produced by both methods, using the referenced image as the style source. Our method captures much more details in the style, especially in patterns and textures. Fig. 9 presents a comparison with Instruct 4D-to-4D (27]. a first instruction- guided 4D scene editing method based on diffusion models and NeRFs, which imposes significant hardware requirements. In this comparison, we evaluate style transfer using text prompt instead of a reference image. Instruct 4D-to-4D focuses mainly on the color palette, while our approach additionally modifies the geometry of the scene. Consequently, the results obtained by CLIPGaussian contain more details. Fig. shows objects from D-NeRF stylization using text prompts, demonstrating that our model performs well not only on 4D scenes. Additional quantitative results, train time comparison and discussion about background loss can be found in Appendix B.3.1
2D
We evaluate performance of CLIPGaussian on 2D images using MiRaGe on a subset of MS-COCO (62), which is commonly used for evaluating style transfer 22 22 Fig. I0 shows a qualitative comparison with AdaIN 22 and Sty'Tr 33 demonstrating image -uige-g style transfer.
Fig. provides visual comparison with InstructPix2Pix [61] and ChatGPT M., using text- base-baeed stylization. While we acknowledge that our method may not achieve the same level of visual quality large-scale diffusion-based models, these models often alter the identity or structure of the stylized subject. In contrast, CLIPGaussian preserves the original content of the image. Additionally, GS-based 2D style transfer benefits from properties such realistic and localized editing 3D space, as demonstrated in [8] Notably, in 2D, repeating structures are more visible. This is particularly evident in Fig. I0 (bottom row) and Fig. [1] (top row). In both cases, stylizations aligned with the chosen references. The patch_size parameter controls the local stylization. For example for patch_siee size = 128, each patch of size 128x 128 is stylized. For this reason, detailed formation can be controlled through the patch_siee parameter. Additional comparisons are provided in Appendix [B4]
Video To evaluate our algorithm on the video style transfer task, employed the VeGaS model 10, which represents videos using 3D Gaussian Splatting. The evaluation was conducted on the DAVIS dataset [63]. high-quality, high-resolution video collection commonly used for video object segmentation. The dataset comprises numerous videos, each containing fewer than 100 frames. Additionally, to test performance on a longer videos we use the Ultra Video Group (UVG) dataset (64). The dataset is composed of 16 versatile high-resolution test video sequences captured at 50/120 fps. Fig. 12 presents qualitative comparison with existing baseline models CCPL (37) and UniST (47) showcasing an example of image-based style transfer. The figure displays the first and last frames of the respective videos illustrate the style transformation over time. Our method produces visually more coherent and aesthetically pleasing results, better preserving the style details while maintaining high fidelity to the video content. Fig. provides a visual comparison with Text2Video [42 and RerenderAVideo 45), illustrating an example of text-conditioned style transfer; here too, the first and last frames are shown. Compared RerenderAVideo, our method demonstrates significantly better temporal consistency, reducing flickering and preserving motion coherence. Compared to

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Style & GT
CCPL Ti
UniST +F
CLIPGausian
Style GT
Tixe2Viden
Rerender
CLIPGGussion
+
S
White wool
Flowers
Figure 12: Comparison of video style transfer using image condition on the DAVIS dataset 63 CLIPGaussian produces more detailed results.
Figure 13: Comparison of video style trans- fer using text condition on the DAVIS dataset [63] CLIPGaussian shows better tem- poral consistency than baseline methods.
Text2Video, we achieve higher visual quality with more faithful adherence to the intended style prompt. All comparisons were conducted using the default settings for each method. Additional comparisons and quantitative evaluations are provided in Appendix B.5
Our experiments show that CLIPGaussian achieves comparable or superior style transfer. Moreover, CLIPGaussian maintains significantly better temporal consistency than prior methods. This because video content is modeled as a set of Gaussian primitíves, where each Gaussian spans multiple frames. As result, style modifications applied to the Gaussians naturally propagate coherently over time.
Conclusions
CLIPGaussian is designed as a plug-in model, compatible with Gaussian Splatting- based architecture. Our method enables effective style transfer from either text prompt an image to reconstructed objects. This flexibility allows CLIPGaussian to operate seamlessly across various data modalities, including 2D images, videos, 3D objects, and 4D dynamic scenes.
In the 3D domain, user study experiments demonstrate strong preference for the stylized outputs produced by CLIPGaussian. This suggests that our approach offers not only technical effectiveness but also practical appeal for creative applications. Quantitative results indicate that our method achieves comparable superior visual quality relative to existing baselines.
Limitations Given the maturity of existing 2D image stylization methods, we acknowledge that our results in 2D may not match the visual quality achieved by large models or diffusion- methods. Although CLIPGaussian is designed for universal applicability, its performance contingent upon the quality of the underlying base models. Moreover, when the selected base fails to reconstruct the scene with high fidelity, style transfer may become intractable or produce perceptually implausible results.
Social impact CLIPGaussian enables cross-modal generality as plug-in-based Gaussian Splatting models. Using CLIP as a multimodal vision and language model allow it to enable high- quality stylization and expand creative possibilities. Gaussian-based representation enables the creation of high-quality real- call-tmme renders which is key challenge in computer vision, especially in broad applications in AR, gaming, and digital content creation.
A Acknowledgments
The work of K. Howil, J. Waczyńska, P. Borycki, and Spurek supported by the project Es- fective Rendering of 3D Objects Using Gaussian Splatting in an Augmented Reality Environment carried out under the First Team programme of the Foundation for Pol- ish Science and co-financed by the European Union through the European Funds for Smart Economy 2021-2021 (FENG). The work of M. Mazur was supported by the National Centre of Science, Poland We gratefully acknowledge Polish
Grant No. 202 221144/B/SS///145555 infrastructure PL.Grid (HPC Centers: ACK Cyfronet AGH, CI TASK, WCSS) for providing computer facilities and support within computational grant no. PLG/2025/018296
computing
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract outlines several key contributions: unified style transfer frame- work for Gaussian Splatting (GS), support for multimodal inputs (2D, 3D, video, 4D), compatibility with text- and image-guided prompts, direct operation Gaussian primitives, integration without retraining, and joint optimization of color and geometry. These claims are well-aligned with the detailed methods and results presented in the paper. The paper substantiates each claim through comprehensive experiments and qualitative results demon- strating style fidelity, temporal coherence, and cross-modal generality. Additionally, the modular design and efficiency aspects are clearly discussed and validated. Therefore, the abstract and introduction provide accurate and honest summary of the paper's 's scope and contributions.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The limitations of CLIPGaussian are clearly stated. While the proposed approach is applicable to multiple modalities, in the case of images, we obtain slightly worse results than approaches using large generative models.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [NA]
Justification: The paper does not include theoretical results.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: The authors provide comprehensive implementation details, including the structure of the CLIPGaussian module, optimization objectives, training procedures, and integration into existing Gaussian Splatting pipelines. The paper clearly specifies the datasets used, the baselines for comparison, and the evaluation metrics applied. These details are sufficient for a knowledgeable reader to replicate the methodology independently, even in the absence of immediate code availability.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: All datasets used in the paper are publicly available.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
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The authors clearly outline the datasets used across different modalities (2D images, videos, 3D objects, and 4D scenes), including data splits for training and evaluation. They provide detailed information on hyperparameters such as learning rates, number of iterations, loss weights, and initialization strategies. The choice of optimizer and training schedule is also specified, along with justification for selected settings when relevant. These details are consistently reported across experiments, enabling readers to understand the experimental setup and reasoning behind parameter choices.
Justification:
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The authors present qualitative comparisons across multiple modalities and user study for 3D objects. This statistical reporting ensures that performance differences are not only visible but also statistically interpretable, helping distinguish between meaningful improvements and potential noise.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: The authors specify the type of compute hardware used, such the GPU models (single NVIDIA RTX 4090; single NVIDIA DGX A100) and the time of execution.
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: The research conducted in the paper conforms, in every respect, with the NeurlPS Code of Ethics.
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: On the positive side, the authors highlight how CLIPGaussian enables more accessible, efficient, and flcxible style transfer across multiple modalities (images, video, 3D, 4D), which can benefit applications in art, design, virtual reality, film production, and educational tools. The ability to stylize content using simple text or image prompts democratizes content creation and lowers the technical barrier for creators.
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper poses no such risks.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: We cited all utilized works and resources according the citing guidelines.
13. New assets
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Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not relate new assets.
14.
Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [Yes]
Justification: We conducted formal survey using the CLICKworker https:// hetps://wwtt //ww wwiccworker.com com/ platform to evaluate stylized objects. Detailed information is included in the Appendix. We paid 1.5EUR for each survey.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
The authors collaborated with an external formal company to design and carry the user study, ensuring professional management of participant recruitment and instructions.
Justification:
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [No]
Justification: LLM was used only for writing, editing, or formatting purposes.
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A Base Model Choice: Implementation Details
3D The vanilla Gaussian Splatting 3 framework represents a scene using a dense set of 3D Gaussians, denoted as G ((Mm;, Ex), oi, ci) where m specifies the mean position, Σ the covariance matrix, a the opacity and с the colors of the Gaussians spherical harmonics. The optimization procedure iteratively renders images from these Gaussians and refines their parameters by minimizing the discrepancy between the rendered outputs and the corresponding ground ground-truth training views.
Our method refines Gaussian primitives by optimizing their position, color, scale, rotation, and opacity parameters, allowing for both appearance and geometry modifications. Our model is trained for 5000 steps without densification or pruning using loss function described in the section
3 If not stated otherwise we set A = 1000, A = 90, A = 5, X = 0.8, patch_sizee = 128, num_ num_patch = 64 and feature_ __rr= 0.01. For the CLIPGaussian-Ligh variant we set feature _lrr 0.002 and Ap = 35. The remaining hyperparameters follow default values of original GS implementation.
4D Many models in the literature are dedicated to 4D (3D dynamic) scenes 4 65 66]. Our approach can be adapted to all models since they all use General Gaussian components and an additional model for modeling time dependence. To present our concept for such models, we choose the D-MiSo model [6]. which extends the Gaussian Splatting framework through a Multi-Gaussian structure and structure uses hierarchy based the relationship between the Core and sub-Gaussian. Core Gaussians are designated to capture and model motion dynamics:
deform networks. A
Geore {(Ncore (m,, R2 St), oi +))
(6)
where m specifies the mean position, the rotation matrix, S the scaling parameters, a the opacity and the colors of the Gaussians. Sub-Gaussians serve to enhance rendering quality, and is defined as
Gsub = ((Nuut + Rot R²', ) c o')))
where m, R is Core-Gaussiag position and rotation; and at are trainable parameters used to define the positions of the Sub- Bub-Gaussian relative to the Core-Gaussian.
CLIPGaussian style transfer jointly optimizes the Gaussian primitive parameters and the deformation networks. Our first stage is a classical D-MiSo framework, which gives us scenes represented by trainable parameters of D-MiSo, see Fig. 2 The second phase is dedicated style transfer. Similar to 3D as default we set ilerations num_patch- = 64. We use feature__e values of original D-MiSo implementation. The background loss weighting parameter As was set to a default value of 0, which is suitable for most styling experiments with text prompts. special cases, it was adjusted to 500.
5000, Ap 90, A 5, Ae 0.8, patch_size 0.025. The remaining hyperparameters follow default
128,
2D Adapting 3D Gaussian Splatting frameworks, originally designed for 3D scene representa- tion, to the domain of single-image 2D reconstruction presents significant challenges 77. Mi- raGe [8] models 2D image / by embedding flat, parametric Gaussian primitives within percep- tually motivated 3D latent space, aligning with human perception. Each Gaussian is defined as (CV(m, R2, S,), .- where R is rotation matrix and S diag(s1, 82, 83) is a diagonal matrix containing the scaling parameters, where $1 = To improve generalization and capture symmetries inherent in natural 2D images, MiraGe employs mirrored input image MMI) during training as form of data augmentation. While the standard 3D Gaussian Splatting framework employs loss function of the form C = (1 AL1 (I, GS(()) + DCCD-SSIM( (T, GS(T)), the MiraGe model extends this by incorporating both the original image I and its mirrored I utilizes cost function C(T) + (IM()))
We optimize position, color, scale, rotation, and opacity parameters of the Gaussian primitives. We train our model for 5000 steps without densification or pruning. using loss function described in the section 3 (modified by using mirror camera as in MiraGe). If not stated otherwise we set À = 0, A 90, A 5. A 0.8. patch_
128, num_patch 64 and feature_I1 0.0025. The remaining hyperparameters follow default values of original MiraGe implementation.
Video For video representation, we use the VeGaS model [10], which models video collection of 3D Folded Gaussians, denoted as
GveGas (F, a.a, f),
(8)
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Here, a and f are temporal folding functions that modulate the Gaussian shape over time. Each video frame is rendered 2D projection of these Gaussians, positioned and shaped according their trained spatial-temporal parameters.
of Gaussian primitives, where each Gaussian spans multiple frames, As a result, style modifications applied the underlying Gaussians naturally propagate coherently over time. To preserve it, our framework optimizes only colors of the Gaussians spherical harmonics. Our model is trained for 5000 steps without densification
CLIPGaussian maintains temporal consisteney. This is because video content is modeled
pruning using loss function described in the section 3 If not stated otherwise we set Xp = 90. Ad 5, A = 0.5, patch_size and feature_ 0.02. For the CLIPGaussian Light variant we set feature_ 1r 0.002 and Ap = 45. The remaining hyperparameters follow default values of original VeGaS implementation.
128, num_patch
64
20

---

B Extended Experimental Results and Evaluation
This section presents additional experimental results and analyses. We begin with a detailed descrip- tion of the evaluation metrics used to assess the effectiveness of our approach. Subsequently, we provide extended results across various data modalities, including images, videos, 3D objects, and 4D dynamic scenes. The supplementary materials contain additional .mp4 files.
B.1 Evaluation Metrics
B.1.1 CLIP-based metrics
In the quantitative evaluation, report four CLIP-based metrics. CLIP Directional Similarity and CLIP-S [57] measure the quality of style transfer while CLIP Directional Consistency [58] and CLIP-F [59] measure the consistency and similarity to the original content. For all metrics, use ViT-L/14 CLIP model [53].
[52]
Let Epos denote the CLIP embedding of style (either style image style prompt), and Eneg denote the embedding
negative prompt. the case of the NeRF Synthetic dataset, we use object names (e.g.. "a lego" for lego object) with ImageNet prompt templates [53]. Let Erender (1) and (i) represent the CLIP embeddings of the stylized render and the ground truth of the i-th test image, respectively. N is the size of test We define the CLIP Directional Similarity and CLIP-S as follows:
N
CLIP-SIM
Σ
cos Erender (1) (ir Epos Eneg),
(9)
N
1 N
CLIP-S
Σ
cos( Cos(EEender (i), Epos)
(10)
Assuming that testing frames come from video, we can also define CLIP Directional Consistency and CLIP-F F as follows:
N cos( Evender
CLIP-CONS
Erender (1), Eg 1) Egg (i))
(11)
N
NN cosl Erender + 1), Erender (i))
CLIP-F
(12)
cos (gg (i + 1), Eg)(
B.1.2 Additional consistency metrics
For additional consistency evaluation, we use short-range and long-range consistency following the implementation and settings from StyleRF [67]. Specifically we warp one view to the other according to the optical flow using softmax splatting, and then compute the masked RMSE and LPIPS scores to measure the stylization consistency, For short-range we average over pairs i-th and (i + 1)-th frames and for long-range
take i-th and (i 7)-th frames.
In addition to these metrics, for evaluation of video consistency, we employ the Farneback optical Flow based metric, following the setup proposed in the ViSt3D 41). Accordingly, in the case of video:
N FoF GT,+k) - (ooi(Style,,Stieeee)))
MF.F.
(13)
N
1=1
where FoF is Farneback optical Flow function, GT; is i-th frame from the original video and Style; i-th frame from the stylized video.
21

---

B.2 3D: Additional Results and Explanation
This section provides detailed description of the quantitative evaluation setup.
During experiments we used two objects (lego and holdog) from the NeRF-Synthetic dataset [55]. and scenes (ganden and bonsat) from the Mip-NeRF 360 dataset [56]. Each was stylized using four text prompts ("Fire", "Mosaic". "Starry Night by Vincent van Gogh", and "Scream by Edvard Munch") and four style images (as shown in Fig. 6
We evaluated CLIPGaussian (our method), DGE 14, SGSST [50], ABC-GS 51 and G-Style 21] using the same base models trained with the original Gaussian Splatting codebase Since Instruct- GS2GS [33 is incompatible with this codebase, was evaluated on models trained using Nerfstudiq Due to the high VRÁM requirements of StyleGaussian [16], it evaluated on models trained with its own training script, which limits the number of Gaussians to 10 As Instruct-GS2GS [13
and DGE [14] employ different prompt templates, we prepended our text prompts with the prefixes used their original papers: "Turn it into a "for Instruct-G$2GS and "Make it look like a" for DGE. Baseline methods
trained using their default configurations. CL.PGaussian models according to the settings described in Appendix [ except for the "Fire" condition, which used higher feature learning rate (0.02). Additionally, for the lego object with the "Fire" condition, we set Abg 100.
trained
B.2.1 Training Time
We evaluate training time using the same dataset as in the user study and quantitative analysis. For each scene, we report the average training time across all styles. Table 2 shows how the number of Gaussian primitives affects stylization time.
Table 2: Number of Gaussian primitives impact on stylization time.
Scene
Number of Gaussians
Avg. stylization time
hotdog 0.14M
11m29s
lego
0.31M
1 11m36s
bonsai
1.35M
11m37s
garden
4.48M
21m03s
B.2.2
Additional consistency evaluation
The consistency of the stylized objects was evaluated using the metrics described in Appendix B.1.2 Table reports the resulting short-range and long-range consistency metrics, demonstrating that achieves results comparable to the original data.
B.2.3 User Study: Details
We conducted formal survey using the CLICK- worker platform This allowed us to recruit demographically balanced participant pool in terms of gender, age. Each participant was pre- sented with questionnaire in which they eval- uated stylized objects or scenes stylized using a text prompt reference image. We wanted the survey for both conditional text and image to be comparable, so chose prompts correspond- ing to images.
Table 4: Friedman statistic test F, and p values for user study Question 1. In case of image condition we consider StyleGaussian G-Style, CLIPGaussian and CLIPGaussian, in case text condition we con- sider I-GS2GS, DGE, CLIPGaussian
condition
F,
object
image
139.3
5,62c-31
scene object scene
image
1178.8
1.44e-39
text
54.70
1.32e-12
The survey was divided into two parts: a main evaluation section and additional question in the
text
8.40
0.014
https://www. http://wwwwccccccoo...rrr [access: 12.05.2025]
22

---

Table 3: Quantitative comparison of style transfer, compared against baseline methods.
Model
Short-range consistency
Long-range consistency
LPIPS 4
RMSE +
LPIPS
RMSE
Original Videos
0.053
0.047
0.140
0.123
cexticonditioed
I-GS2GS 13
0.048
0.043
0.148
0.131
DGE [4]
0.055
0.044
0.148
0.120
0.051
0.043
0.132
0.117
CLIPGaussian
0.057
0.051
0.148
0.125
Image-conditonee
StyleGaussian 16
0.052
0.061
0.145
0.144
SGSST 150
0.040
0.050
0.139
0.142
ABC-GS [1
0.048
0.043
0.141
0.126
G-Style 21
0.054
0.049
0,145
0.136
Light
0.055
0,045
0.139
0.120
CLIPGaussian
0.064
0,055
0.162
0.132
end. The main section included eight comparative examples, each involving stylized outputs from three different methods (see Fig. 5 6). Fig. 18] illustrates a typical evaluation interface. In each example, participants were first shown the reference image text prompt used for styling. This was followed by a GIF animation showing the original object or scene, and then the stylized results from each method. For each evaluation we also asked about stylized factors like Color Palette, Lighting, Detail Level, Mood or Atmosphere, Brushstrokes".
Main evaluation consisted of four questions:
"Please rank the generated results according to how closely they match the style from most similar(1) to least similar(3)" focused on style transfer quality. Participants ranked the three methods based on how well they reflected the reference style. Each method could be selected only once per ranking. For example, Method X might be ranked 3rd, Method Y st, and Method Z. 2nd.
Question I:
Question 2: "On scale from "Very Low" to "Very High", how would you rate the visual appeal of each generated result?" assessed visual appeal. For each method, participants selected one of several options ("Very Low". "Low", "Medium", "High", "Very High" This allowed users express their perception of the aesthetic quality of each result independently.
Question 3: "On scale from "Very difficult" to "Very easy", How well can recognize the original scene content each generated result" focused on recognizability content. The users evaluated how clearly the original object or scene was preserved after stylization. Options ("Very Difficult", "Difficult", "Medium", "Easy", "Very Easy") independently rated for each method.
Question 4: "On a scale from "Not important at all" to "Extremely important" how much does this factor influence your judgment of the styled 3D object?", asked about the factors influencing the user's s decision. Participants chose from predefined options ("Not important all", "Not important". "Important". "Extremely important"). Each factor was rated separately, following the same format as questions and 3.
Additional question:
In the context of "Starry Night by Vincent van Gogh" and "Scream by Edvard Munch". We asked an additional question: "Were you familiar with the following images before?". Users could answer "Yes" or "No".
The poll format was consistent across object/scene and text/image conditions. For evaluations based on text prompts, we wanted to avoid introducing visual bias, and we didn't 't attach any referenced image. Therefore, we included only those participants who were already familiar with the references, such "Scream by Edvard Munch" and "Starry Night by Vincent Gogh".
23

---

Scare 1E2
Obpett
Score 382
10055
10005
D06
Caty
May Caty
CLiGGemtin
CUPGemstee
100I55
000
102255
002
CUPCevatie
Figure 14: User study: Comparison of 3D style transfer using referenced image.
Figure 15: User study: Comparison of 3D style transfer using text condition.
Deleston aetecvoo aoolledd 30
Dubuutoe wheree leten abdect deettaadee
Eve of
20-
18
Figure 16: User study: Comparison of 3D style transfer using text condition. User ratings of fac- tors influencing their judgment styled 3D) ob- jects/scenes by text.
Figure 17: User study: Comparison of 3D style transfer using text condition. User ratings of fac- tors influencing their judgment of styled 3D ob- jects/scenes by image.
Tab. shows Friedman statistic test F, and values for Question user study. For the image condition, we evaluated three methods: StyleGaussian, G-Style, and CLIPGaussian. For the text condition, the evaluated methods were I-GS2GS, DGE, and CLIPGaussian. In all scenarios (1-4) we observe significant differences between methods (reject HO). Additional results for image-based style transfer are presented in Fig. [4] while results for text-based style transfer are shown in Fig. [5] The post hoc Conover Friedman test for user study on stylized objects/scenes by image/text revealed statistically significant differences between methods (p 0.05), indicating if the null hypothesis can be rejected is shown in the Tabs. 5 6 In almost every case we can reject HO, the exception is the comparison of our model with G-Style the case of evaluation objects stylized with reference image.
Fig 16 and Fig. 17 show that the majority of participants identified color and lighting as highly influential factors in their assessment of styled 3D objects/scenes. In comparison, brushstroke details were generally regarded as less important to their judgments. A high-resolution comparison
across object/scenes styled by text-based methods shown Fig. 20 and Fig. 2T A corresponding comparison for image-based methods is presented in Fig. 22 and Fig
23

---

Image "Starry Night by Vincent Goght used style the object.
Hotdog styled by referenced image "Starry Night by Vincent van Cogh"
Original
A
B
gener accor ding to closely they style similar(1) to similar(3).
Tap select
Tap nee
"Very "Very you appeal generated
Gr from "Very 'Very waxy". how can you resagnize the original scenee penerated
articut
Heckor
kazy
verylary
G
On "Not "Extremety
her
Color Kolette
Lighting
O
Octoil Lord
Figure 18: Survey interface view from the Click Worker website, users evaluate stylization of the 3D object hotdog conditioned the painting Starry Night by Vincent Gogh.
25

---

Style
CLIPGausiia--ight
CLIPGaussian
Fire
Starry Night
by Vincent van Gogh
Figure 19: Visual comparison of CLIPGaussian and CLIPGaussia-Liggh in 3D style transfer, conditioned by text, on hotdog and lego objects from NeRF-Synthetic dataset (55)
Table 5: Calculated p-value Posthoc Conover Friedman test for user study in case objects
Table 6: Calculated p-value Posthoc Conover Friedman test for user study case of scene styl- ized by image.
stylized by image.
Stylttanssinn
G-Seyle
CLIPGaneiian
StyleGannsiza
G-Style
CLIPGansain
StyleGausiann
1000+00
15
StyloGausian
8.90e-31
G Style CLIPGunsian
1.45--25
1.0e+000
6.56e 02
G-Style
8.90
100e+000
3.32e-15
5.71e-33
6.6e-002
1.00+0000
CLIPGansson
1668-17
3.32e-15
1.00++00
Table 7: Calculated v-lue Posthoc Conover Friedman test for user study in case of objects stylized by text.
Table Calculated p-vuuee Posthoc Conover Friedman test for user study in case of scene styl- ized by text.
EGS2GS
CT CLIPGasssan
LG52GS
CLJIPGasssin
FOSIGS
1000+000
0,000067
1:
FGSGGS
1.00+000
01000057
2.39 14
G-Style
6.07--05
LU0000
124c-04
DGE
6.67-005
L.00x000
1.24-044
CLUMGansian
2.39-144
0.000125
100e+00
CLUPGanssian
2.39e
0.000125
1.0c+000
B.2.4 Hyperparameters Analysis
We evaluate the visual quality of 3D style transfer with respect to the parameters feature_ Ir and patch_size on three objects from the NeRF-Synthete dataset: lego, hotdog, and mic. These objects are stylized using prompts such "Starry Night by Vincent van Gogh". "Fire", and "The Great Wave off Kanagawa by Katsushika Hokusai". The quantitative impact of feature_1r 1r shown in Tab. 9 while the influence of patch_ size is reported in Tab. TO Our observations indicate that both parameters contribute to enhancing the stylistic expressiveness of the output. However, excessively large values introduce greater flexibility in the spatial distribution of the Gaussians, resulting in noticeable loss of content detail and reduced spatial consistency (see Fig. 24.
Additionally, we evaluate the effect of the weighting factors of the loss components, Ap and Ag- The quantitative impact of Ap is shown in Tab.
II while the influence of X reported in Tab. A higher Xp increases the expressiveness of local style transfer; however, excessively high values may lead to overstylization of the object. Similarly, increasing Xa enhances the global style characteristics. Quantitative evaluations suggest that X parameter has limited impact on spatial consistency and content similarity.
12
Qualitative results are presented in Fig. 25
26

---

Table Effect of feature learning rate on the performance of CLIPGaussian in terms the CLIP metrics.
patch_size
CLIP-S
CLIP-SIM 1
CLIP-F
CLIP-CONS
32
18.53
19.51
98.08
15.02
64
21.73
19.31
97.70
11.06
128
25.60
29.78
97.87
6.93
256
27.45
32.65
97.96
4.42
Table 10: Effect of a feature learning rate on the performance of CLIPGaussian in terms of the CLIP metries.
feature_L lr
CLIP-S
CLIP-SIM
CLIP-F
CLIP-CONS
0.0025
25.24
29.31
97.68
7.91
0,005
25.60
29.77
97.99
6.93
25.61
29.93
97.61
6.26
0.02
26.27
30.09
97.54
6.35
Table 11: Effect of A parameter the performance of CLIPGaussian in terms of the CLIP metries.
Xp
CLIP-St
CLIP-SIM
CLIP-F1 CLIP-CONS
16.00
14.12
98.74
11.03
90
23.67
25.07
97.95
2.36
180
24.36
25.27
97.78
1.48
Table 12: Effect of Xa parameter on the performance of CLIPGaussian in terms of the CLIP metrics.
Xa
CLIP-ST
CLIP-SIM
CLIP-F CLIP-CONS
0
22.20
22.76
97.71
2.52
5
23.67
25.07
97.95
2.32
10
23.55
25.15
98.04
2.61
27

---

Style
I-GS2GS 13
DGE 14
CLIPGaussia
Fire
Starry Night by Vincent
van Gogh
Mosaic
Scream by Edvard Munch
Fire
Starry Night by Vincent
van Gogh
Mosaic
Scream by Edvard Munch
Figure 20: Full comparison of CLIPGaussian (our) and baseline models in 3D style transfer, condi- tioned by text, on hotdog and lego objects from NcRF-Syntheed dataset [55]
28

---

Style
I-GS2GS 13
DGE 14
CLIPGaussian
Fire
 E
Starry Night by Vincent
van Gogh
Mosaic
Scream by Edvard Munch
Fire
Starry Night by Vincent van Gogh
Mosaic
Scream by Edvard Munch
Figure 21: Full comparison of CLIPGaussian (our) and baseline models in 3D style transfer, condi- tioned by text, on garden and bonsai objects from Mip-NeRF 360 dataset [56].
29

---

Style
StyleGaussian [16
G-Style 21
CLIPGaussian
S
Figure 22: Full comparison of CLIPGaussian (our) and baseline models in 3D style transfer, condi-
tioned by image, on hotdog and lego objects from NeRF-Synthetic dataset (55)
30

---

Style
StyleGaussian [66
G-Style 2
CLIPGaussian
Figure 23: Full comparison of CLIPGaussian (our) and baseline models in 3D style transfer, condi- tioned by image, on garden and bonsai objects from Mip-NeRF 360 dataset (56)
31

---

patch_side size
64
128
256
0.04
0.00
0,01
0.04
0002
0.01
Featuly 1T
0.04
000
100
Figure 24: Effect of a patch size and feature learning rate on the performance of CLIPGaussian on lego, hotdog and mic objects from Night by Vincent van Gogh", "Fire" and "The Great Wave off Kanagawa by Katsushika Hokusai" prompts.
dataset [53]. Objects are stylized with "Starry
32

---

X
90
180
10
10
Ad
10
Figure 25; Effect of a A and a Xa rate on the performance of CLIPGaussian lego, hotdog and mic objects from NeRF-Synthetic dataset (55] Objects are stylized with "Starry Night by Vincent van Gogh", "Fire" and "The Great Wave off Kanagawa by Katsushika Hokusai" prompts.
33

---

B.3 4D: Additional Results and Explanation
This section delves deeper into our experimental analysis of 4D scenes. We begin with detailed exploration of stage 2 hyperparameters, followed by demonstrating the adaptability of our method on an additional multi-camera dataset.
Fig. presents style transfer on selected objects from the D-NeRF dataset. These results illustrate that CI.PPGasssan enables style conditioning via both text prompts and reference images, highlighting its versatility across multiple guidance modalities.
Text condition
Style image
Fire
Starry Night
by Vincent van Gogh
Scream by Edvard
Munch
Figure 26: Style transfer results on samples from the D-NeRF dataset [60] Our CLIPGaussian model accommodates both text-based and image-based style inputs.
B.3.1 Hyperparameters Analysis
For dynamic 3D scenes using the D-MiSo model, the background parameter value of 0 in most cases, However, using the prompt "Summer" we could observe the background stylization as well. Fig. 27 shows the effect of background loss on the Jumpingjacks object: without background loss (Ang 0) and with background loss (Ang 500). More precisely, we used the alpha channel from the original images available D-NeRF dataset to create background mask m for A (1m(T)) m (I))))) The effect of using the background loss is also shown Fig. 31 and Fig. 32
the default
each view. In this case La
We evaluate the visual quality of style transfer in 4D with respect to feature_ and the number of patches n. Fig. 288 shows two representative objects from the D-NeRF dataset, each condi- tioned on distinct text prompt (Hellwarrior "The Great Wave of Kanagawa by Katsushika Hokusai". Jumpingjucks "Spring"). feature_I, influences primarily the color saturation of Gaussians. We observe that increasing this pa- rameter enhances the stylistic expressiveness of the output. A higher generally improves styles details. In Hellwarrior example for the larger see more "water swirls" appropriate to the style. However, excessively large values (e.g., n=128) introduce increased freedom in the Gaussians' spatial distribution, leading to notable loss of content detail. This effect is particularly pronounced in the Jumpingjacks example, where reconstruction of the hand visibly deteriorates.
Abg
Abg
500
Figure 27: Effect of background loss Abg on styl- ization Jumpingjacks object with the prompt "Sum- mer".
Fig. shows that increasing batch size and the number of patches leads to noticeably improved visual fidelity in style transfer. Increasing the batch size enables the preservation of finer visual details.
34

---

Patch size
32
64
128
0.025
0.0025
feature 1r
0.025
0.0025
Figure 28: Visual comparison of style transfer results on D-NeRF objects under two text prompts "The Great Wave of Kanagawa by Katsushika Hokusai" and "Spring". showing the impact of feature_lr and number of patches.
We check the influence of batch size hyperparameter from the chosen base model (D-MiSo). larger batch size prevents deformation of the subject's head. This highlights that our method faithfully inherits and reinforces the structural integrity imparted by the base architecture.
Table 1 presents a comparison of numerical training time and CLIP-S, CLIP-SIM metrics for the selected object under batch size, the text condition "Starry Night by Vincent van Gogh" and number of patch=32. The training time results reflect dependency on the base architecture. The CLIP-S suggests that models trained with smaller batch strategies achieve marginally higher. However, the differences in CLIP-S between models remain relatively minor. In particular, despite the small numerical variance, visual comparison (Fig. (9) reveals substantial qualitative differences in output fidelity, highlighting the limitations of current metrics in capturing perceptual quality.
B.3.2 Multi-Cament Setup
Fig. in main paper shows experiment on The Neural 3D Video dataset (DyNeRF) 54 consists of videos captured by 21 cameras for each scene. The multi-view inputs were time synchronized and the images were extracted at 30FPS. In our experiments we use the first 24 frames following the data loader provided in (5] to show the capabilities of CLIPGaussian in real 4D scenes. In this dataset only the frontal views the scenes are shown. contrast the PanopticSports datasets [68] is full 360 view dataset, which comprises dynamic scenes featuring significant object and actor movements. Each scene was recorded using 31 cameras over 150 timesteps. Fig. 30 the transferred style is prominently expressed on both the primary actor and the background.
35

---

Table 13: Numerical comparison of training time and CLIP score, CLJP-SIM metrics for the selected object under the text condition "Starry Night by Vincent van Gogh" and number of patch=32, background white, checking influence of batch size hyperparameter from the chosen base model (D-MiSo).
Hook
Jumpingjacks
Trex
BouncingBalls
Batch
CUP-
CLIPS
SIM
time
SIM
time
time
time
27.73
22.68
10:12
23.15
18,32
10:35
28.00
23.57
10:41
24.77
22.81
11:43
2
27.75
23.11
20:59
23.19
18.68
20:32
27.54
22.94
20:53
25.27
24.36
21:49
26.88
23.42
39:31
22.05
17.64
42:25
27.47
22.13
42:01
26.35
25.37
43:21
Batch
Hcllwarrior
Mutant
Standup
CLIP-S
CLIP-
CLIP-S
CLIP-
CUP-
SIM
24.58
23.52
10:17
29.76
21.58
10:43
24.55
21.43
10:28
2
25,40
24.53
20:56
29.76
21.99
20:08
24.21
21.42
21:18
26.02
25.85
39:37
29.43
21.86
39:57
23.02
20.70
39:17
Number of patches 32
2
64
Batch size
Figure 29: Effect of Batch Sizes and Number patches using D-MiSo base model. Mutant from
D-NeRF is stylized with "Starry Night by Vincent van Gogh".
Text condition
Styled images
Fire
Starry Night by Vincent van Gogh
The Great Wave of Kanagawa by Katsushika Hokusai
Figure 30: Qualitative style transfer results samples from the PanopticSports dataset 68
36

---

B.3.3 Artifacts
The styling of objects may be incorrect may not meet the user's visual needs, especially in concepts that are difficult to represent, such as hope, country names, random or beauty
Based on our experience, we have also identified common categories of failure cases mostly related text prompts:
Too general concepts, less known concepts Since we are using CLIP representation, the concept should be well represented by CLIP embedding. We considered 3 prompts: We observed that for general prompts such as "Imprresionism"" "Painting by Claude Moner" "Woman with a Parasol Madame Monet and Her Son". Where: CosineSimillrrtyyprrompp prompt2) 0.802, CosineSimilartyypronpp., prompt3) = 0.531, CosineSimilarity(roooppp prompt3) = 0.382.
We observed that for general prompts such "Impressionism" the model tends fill the background, and hollow spaces (e.g. between fingers). This improves the quantitative results, but in our opinion decreases the visual perception of the stylization. We support this claim using the CLIP-S metric, the difference between choosing the Abg parameter is shown in the Tab. 14 and Fig.
37.332 32
The concepts of "Impressionism" and "Painting by Claude Moner" are well represented by the CLIP model. addition, they are closely related, which is visually supported by the models trained using those prompts. On the other hand, concepts such "Woman with(...)" are heavily influenced by details like as an umbrella, which spoils the visual effect in this case. This pattern is shown by the CLIP-SIM metric.
"Impressionism"
"Painting by Monet"
Claude
"Woman with a Parasol Madame Monet and Her Son"
Ang
Abg 500
Figure 31: Visual comparison of object styling using three prompts, ranging from the most general to the most specific. A highly general prompt tends to produce background artifacts, which can be mitigated through background loss. Conversely, overly specific prompt may be poorly represented in the CLIP space, leading to features such as face-like patterns (suggesting Madame) or umbrella-like shapes.
Table 14: The impact of prompts on the quality of styling using the CLIP-SIM and CLIP-Smetric
prompt1: "Impressionism" ", prompt2: "Painting by Claude Monet", prompt3: "Woman with a Parasol Madame Monet and Her Son".
Jumpinjacks
prompt1
prompt2
prompt3
CLIP-S
CLIP-SIM
CLIP-S
CLIP- SIM
CLIP-S
CLIP-SIM
Xbg 0
23,04
18.63
17.77
12.18
18.97
10.88
Abg 500
21.51
16.02
18.02
13.45
18.35
10.41
Abg = 1000
21.81
16.59
18.20
14.27
18.52
10.65
Length and detail of the general concept prompts We conducted an experiment using Jumpingjacks in which we considered three prompt lengths, see Tab. [3], We noticed that if shorter prompt considered, the CLIP model finds certain representation of specific word/short prompt related to
37

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general concept, see Fig. B longer prompt is used, we can expect an averaged representation of the concepts found the prompt, which may cause some inaccuracies in the stylized image, see Fig. 32
In both cases, the styling emphasizes artifacts, especially in the background. We mitigate the artifacts in the background using Ang and patch_size, This is particularly evident in detailed areas, Visual assessment very difficult and subjective.
e.g. the hands (see Fig. 27 28
"Lotus flower"
soft pink lotus flower with delicate petals and bright yellow center, rest- ing gently on pond"
close-up of a bloom- ing pink lotus flower, its intricate petals radiating around a vivid yellow core, floating gracefully the water with gentle reflection beneath and soft green lily pads surround- ing it"
Abg
500
Abg
Aug = 1000
Figure 32: Visual comparison of the impact of length and detail of the prompt on the style. Using more detailed prompt tends to create artifacts in the background. It is worth noting that some areas tend to change more, e.g. a lotus almost always appeared on the Hook's s back. When prompt is too long observe inaccurate with good representation using clip.
Table 15: The impact of length and detail of the overall concept of the prompt. promptl: "Lotus flower". prompt2: "A soft pink lotus flower with delicate petals and a bright yellow center, resting gently on pond": prompt3: "A close-up of blooming pink lotus flower, its intricate petals radiating around a vivid yellow core, floating gracefully on the water with gentle reflection beneath and soft green lily pads surrounding it".
Jumpinjacks
prompt1
prompt2
prompt3
CLIP-S
CLIP-SIM
CLIP-S
CLIP- SIM
CLIP-S
CLIP-SIM
Ang 0
24.97
22.24
23.36
19.57
24.03
21.88
Aag 500
24.85
20.78
19.46
15.87
20.34
19.15
Ang 1000
24.71
20.82
19.25
15.81
20.90
19.00
38

---

The D-NeRF dataset provide GT as only one time point for each camera. For a set of time steps we used the cameras from WeRF-Synthetic (200 views) to generate reference images using the base D-MiSo model. Then, we generated the corresponding stylized B.3.4 Time
0.1. i 10, ..... 10).
images and computed the mean CLIP-F and CLIP-CONS metries for time steps, see. Tab [6 From this experiment, we can see that it is difficult to indisputably determine which styling is best based on the selected metrics.
Table 16: Numerical comparison of the influence of batch the quality of styling using CLIP-F and CLIP-CONS metrics. We used default parameters and the prompt "Starry Night by Vincent van Gogh" " for the experiment. D-Miso only means reconstructions of the 4D object.
dataset
Hook
Jumpingjacks
Trex
Bouncingballs
Hellwarrior
Mutant
Standup
CLIP-F
batch=1
97.56
98.41
97.36
98.73
98.41
98.01
98.03
2
97.37
98.15
97.34
98.66
98.23
97.95
97.87
97.66
98.35
97.68
98.33
98.26
97.65
98.19
CLIP-COR LIP-CCONS
2.1
2.31
2.25
0,66
5.64
1.78
2.60
1.7
2.80
3.24
1.03
5.72
1.92
3.02
4
2.7
2.97
2.97
0.53
6.29
2.43
3.68
For each test camera from the D-NeRF dataset, we generated stylized images at 0.05 i i {0, 1, 20} time points. Next, we calculate the average metrics to evaluate the temporal consistency time for each camera; the Tab. 17 An interesting observation is that in almost every case, when using selected hyperparameters, we see an increase metrics compared to the base model. Fig. B3 presents visual comparison of objects with respect to camera angle and time. The results show that the styling remains consistent and that temporal variations do not introduce artifacts, long the object accurately reconstructed.
Table numerical comparison effect of batch size on temporal consistency over time for each camera, using the prompt "Starry Night by Vincent van Gogh".
dataset
Hook
Jumpingjacks
Trex
Bouncingballs
Hellwarrior
Mutant
Standup
Short-range consistency: LPIPS
0.017
0.019
0.012
0.013
0.023
0.007
0.009
0.017
0.018
0.012
0.012
0.025
0.007
0.010
0.017
0.017
0.012
0.013
0.026
0.007
0.009
D-MiSo
0.009
0.011
0.008
0.007
0.018
0.006
0.006
Short-range consistency: RMSE
0,046
0.061
0.046
0.021
0.055
0.028
0.031
0.045
0.058
0.044
0.020
0,056
0,028
0,032
0,044
0.055
0.043
0.021
0.057
0.028
0.031
D-MiSo
0.023
0.036
0.029
0.018
0.043
0.017
0.018
Long-range consistency: LPIPS
0.028
0.062
0.049
0.043
0.027
0.044
0.031
0.025
0.063
0.050
0.043
0.026
0.043
0.030
0.026
0.063
0.048
0.040
0.027
0.039
0.029
D-MiSo
0.016
0.046
0.031
0.028
0,020
0,031
0.021
Long-range consistency: RMSE
0,049
0.043
0.031
0.028
0.062
0.027
0.044
0.050
0.043
0.030
0.025
0.063
0.026
0.043
0.048
0.040
0.029
0.026
0.063
0.027
0.039
D-MiSo
0.031
0.028
0.021
0.016
0.046
0.020
0.031
39

---

= 0
== 0.25
= 0.5
t=
t t= 0.25
t = 0.5
"Fire"
"Lotus...
"... Wave...
Figure 33: Visual comparison of objects across camera angles and time, demonstrating consistent styling. We used Aag 1000, batch=4.
40

---

B.4
2D: Additional Results and Explanation
This section presents additional qualitative results for 2D style transfer. Fig. 34 shows qualitative comparison with similar text-based methods: CLIPstyler [3 and FastCLiPstyler [35]. Fig. 35
presents an additional comparison with AdaIN [22] and Sty'r² 33 CLIPGaussian is a competitive alternative to these methods. We see that our method mainly focuses on details.
Style
Original
CLIPstyler 34
FastCLIPstyler
335
CLIPGaussian
Fire
Starry Night
by Vincent van Gogh
Mosaic
Scream by
Edvard Munch
Figure 34: Comparison of image style transfer using text condition.
Style
Original
AdaIN 22
StyTr² 33
CLIPGaussian
Figure 35: Comparison of image style transfer using image condition.
41

---

B.5
Video: Additional Results and Explanation
This section provides detailed description of the quantitative evaluation setup.
During experiments we used four videos (camel, bear, train and blackswan) from the DAVIS dataset [63] and four videos (beauty. bospho, shake and yacht) from the UVG dataset [64]. For DAVIS dataset we used videos with resolution 854px x 480px and for UVG we downscaled videos to 960px x 540px. Similarly evaluation of 3D style transfer, each stylized using four text prompts ("Fire", "Mosaic", "Starry Night by Vincent van Gogh", and "Scream by Edvard Munch") and four style images (same in Fig. 36).
As Text2Video 42 and RerenderAVideo 45 employ different prompt templates, we use "Make it (style) style" for Texe2Video and "A object] in (style) style" for RerenderAVideo, where style is a CLIPGaussian style prompt and object is a video name i.c. bear, blackswan, camel or train. Baseline methods were trained using their default configurations. CLIPGaussian models were trained according to the settings described in Appendix A On average CLIPGaussian took around 11 minutes style video from DAVIS dataset.
We provide example comparisons on test subset for image (Fig- 36 and text (Fig. conditioning. Quantitative evaluations using CLIP-based metrics presented for the DAVIS (Tab. 18 and UVG (Tab. 19 datasets. Furthermore, consistency is evaluated using RMSE and LPIPS (Tab. 20 and
Tab. 21 while temporal consistency is measured using Farneback optical flow at different time
intervals (Tab.
Style & GT
CCPL 37
UNiST 5
CLIPGausiian
Style
Texc2Vidoo
Rerender
CLIPGaussian
4S
by Vinsent Van Gogh
Edhand Munch
Figure 36: Comparison of video style transfer using image condition DAVIS dataset (63
Figure 37: Comparison of video style transfer
using text condition on DAVIS dataset (33
42

---

Table 18: Quantitative comparison of video style transfer on the DAVIS dataset 63 based metrics. Larger values are better.
using CLIP-
Model
CLIP-S' CLIP-SIM
CLIP-Ft
CLIP-CONST
Text-conditioned
Rerender 45
19.40
9.83
98.23
-0.03
Tex
E2
26.05
24.99
93.63
0.03
CLIPGaussian
26.25
24.53
99.00
1.92
Imnge-conditioned
VISI3D 41
55.92
2.75
99.18
3.28
AdaAuN
39
S7.71
-1.04
97.56
1.08
RSReVST 66
61.50
2.12
98.85
2.51
UniST 47
65.93
3.85
99.36
5.16
CCPL [37
68.89
8.20
97.92
-0.02
H..PGaussian-I.ige
63.60
8.06
99.50
11.99
Iauusiann
74.31
17.60
99.18
1.27
Table 19: Quantitative comparison of video style transfer on the UVG dataset 64
using CLIP-based
metries. Larger values are better.
Model
C CLIP-SIM CLIP-Ft CLIP-CONS f
Text-conditioned
Rerender 45
14.08
7.11
74.38
0.01
Video 42
19.02
13.18
70.51
0.18
CI.PGaussian
23.88
18.38
100.39
18.38
Image-conditione
ViSt3D 41
55.98
0.70
99.54
3.78
AdaAttN
39
56.27
1.24
98.1 11
1.22
RcRcVST
(69
Out of memory
UniST 47
55.75
2.04
99.44
5.29
CCPL 37]
59.14
2.19
98.50
1.45
HL.IGaussian-Lighe
63.83
10.61
100.38
4.25
CausGaus
71.64
16.68
100.42
0.94
Table 20: Quantitative comparison video style transfer consistency on the DAVIS dataset using RMSE and LPIPS. Smaller values are better.
Model
Short-range consistency
Long-range consistency
LPIPS $
RMSE 4
LPIPS
RMSE
Original Videos
0.042
0.034
0.070
0.055
Text-conditioned
Rerender
45
0.062
0.040
0.132
0.077
Texe2Video
42
0.261
0.183
0.235
0.166
CLIPGaussian
0.084
0.057
0.152
0.095
Image-conditioned
ViSt3D
0.081
0.043
0.121
0.063
AdaAttN
39
0.087
0.059
0.116
0.083
ReReVST
69
0.072
0.047
0.100
0.068
UniST 47
0.062
0.047
0.088
0.066
CCPL
37
0.102
0.065
0.132
0.093
I..PGaussian-Lightt
0.049
0.035
0.083
0.061
CLIPGaussiar
0.086
0.057
0.157
0.090
43

---

Table 21: Quantitative comparison of video style transfer consistency on the UVG dataset 644 RMSE and LPIPS. Smaller values are better.
using
Model
Short-range consistency
Long- range consistency
LPIPS 4
RMSE +
LPIPS
RMSE
Original Videos
0.045
0.025
0.068
0.049
Text conditioned
Rerender
45
0.027
0.025
0.064
0.049
Text2 Video
12
0.223
0.153
0.150
0.105
CLIPGaussian
0.017
0.018
0.053
0.054
Image-condtioneed
ViS13D 41
0.047
0.031
0.103
0.059
AdaAuNN
39
0.056
0,046
0.096
0.075
RST
699
Out of memory
UniST
0.043
0.038
0.077
0.065
CCPL
0.072
0.052
0.106
0.081
0.019
0.017
0.049
0.044
CLIPGaussian
0.020
0.019
0.060
0.056
Table 22: Quantitative comparison video style transfer consistency on the DAVIS dataset [63],
using absolute difference of Farneback optical Flow, for different intervals (number of frames k).
Model / k
Text-cunditioned
Rerender
45
1.08
2.07
4.40
7.95
11.34
Text2 Video
5.06
5.91
7.68
9.93
11.34
CLIPGaussian
0.41
0.79
2.02
4.00
6.58
Image-conditioeed
ViSt3D
41
0.75
1.19
2.30
3.59
4.54
AdaAtIN
F2
0.69
1.09
2.57
3.86
5.26
ReReVST
69
0.46
0.84
2.30
3.43
4.94
UniST
41
0.43
0.73
1.75
3.21
4.98
CCPL
1.40
2.36
4.62
6.86
8.45
LLIPGaussia--Lihht
0.20
0.35
0.83
1.46
2.32
CLIPGaussian
0.41
0.79
2.12
4.06
7.00
44


Reviews:
[{"review_id": "grOIZ8VOV3", "reviewer": "Reviewer_ed8o", "summary": "This study presents CLIPGaussian, a plug-in module for style transfer that can be applied to various data types such as 2D images, 3D objects, videos, and 4D dynamical scenes. The core idea is to represent various data types as Gaussian Splatting (GS) representations, and design a unified framework to manipulate this representation to transfer the reference style. The proposed approach incorporates two main steps: (1) authors train the base model which learns how to represent the object or scene using Gaussians (i.e., normal Gaussian Splatting training using existing baselines). Then, CLIPGaussian modifies the properties of the Gaussians so that the final result looks similar to the target style (i.e., a given text or reference image). Authors conduct both qualitative and quantitative evaluations. The evaluation across multiple domains (2D, 3D, 4D scenes) was impressive and comprehensive. Results suggest that this method outperform existing studies in various aspects without increasing initial model size or complexity.", "questions": "**1. [Control over geometry]:** Authors can show how much their model is capable of controlling or imitating diverse geometries. Adding diverse examples for geometry control might help to understand model capabilities. \n\n**2. [Failure cases]:** Adding failure cases can help to further understand how this model can be improved and which reference styles can lead to failure. Based on qualitative results, it slightly seemed that the method can better imitate styles given by the text prompts (in comparison with reference images).\n\n**3. [Diversity of reference images or text prompts]:** Text-guided stylization might be subjective and dependent on prompts used for evaluation. What was the diversity of text prompts or reference images used in the evaluation? How much method is sensitive to vague prompts? Is model capable of imitating diverse geometry constraints or is limited to simple control over geometry? \n\nI am on the positive side for this paper, and I am willing to improve my initial score after reading authors comments and replies.", "limitations": "It can be improved if authors add more failure cases (e.g., vague prompts, diverse reference styles, sparse reference images, etc.)", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Thanks for the authors effort to prepare this draft. This paper was among the papers that I really enjoyed reviewing it; considering its clear representation, comprehensive evaluation, detailed supplementary, and innovative ideas behind it. \n\n**- Strengths:**\n\n**1. [Impressive and comprehensive evaluation]:** Authors provide comprehensive qualitative and quantitative evaluations across various modalities (2D, 3D, and 4D scenes). Results suggest that the proposed method outperforms existing ones or it is among top performing ones in various modalities. \n\n**2. [Unified framework across multiple modalities]:** It is interesting how the proposed simple framework can be applied to various data types and outperform existing methods in each domain. It is promising to have a unified framework across various domains. \n\n**3. [Innovative idea]:** I like the basic idea behind the paper. It enables cross-modal generality by focusing on GS representation, converting various domains to a single unified representation, and then using well-known CLIP and VGG losses to transfer the style.\n\n**4. [Clear presentation]:** Paper is well-written. Steps are clearly explained. Method and results are nicely communicated. \n\n**5. [Efficient model]:** The model works as a plug and it does not increase the model size. \n\n-----\n **- Room for improvement:**\n\n**1. [Performance on 2D images]:** 2D image stylization might not as well as other modalities, considering the performance of 2D diffusion models. \n\n**2. [Inherited dependence on base GS model]:** This model performs as a plugin and built upon the base model. If base model fails, style transfer might also fail. \n\n**3. [Adding domain specific loss terms]:** While this method is generalizable across various modalities, the performance in each domain can be improved if loss terms specific to that domain would be added to ensure successful style transfer. For example, for 4D dynamic scenes, it might be beneficial to add loss terms that ensures temporal consistency.", "was_revised": true, "final_justification": "Thanks for the authors effort to provide the rebuttal. The rebuttal tries to address my concerns, providing explanations and examples on geometric control (e.g., “fur” and “cubism” styles), systematic failure case analysis, and prompt diversity; with these additions, I remain positive and support my initial positive acceptance score.", "rebuttal": "We sincerely thank the reviewer for their thoughtful comments and interesting questions. We are especially pleased that the overall pipeline was well understood and that its contributions were clearly recognized. We hope the responses below will address any remaining concerns and help us further improve the quality of the paper.\n\n**W2, W3  \\[Inherited dependence on base GS model, Adding domain specific loss terms\\]:**\n\nIn this work we used background loss as an example of a domain specific loss term. It was only used in case of 3D and 4D objects to ensure that the background won’t be affected by the stylization. In future work, it would be a good idea to provide additional support for the base model, especially in terms of domain-specific consistency. We believe that adding the appropriate loss (for example time related in case of 4D/Video) would contribute to the correctness of the base model and stage 2: the styling itself. We will highlight this as a future work direction. \n\n**Q1 \\[Control over geometry\\]:**\n\nOur model is based on the Gaussian Splatting representation. We can consider editing the object as in the case of the D-MiSo/VeGaS/MiraGe model (all of these base architectures support object editing), because the stylized representation fully retains the properties of the base model. When it comes to styling, we can modify the position as well as the scaling and rotation of corresponding Gaussian Splats. Hence, It is possible to achieve the effect of “detaching” the Gaussians from the reconstructed object. An interesting example is the stylized object using the text prompt `Fur`, see Fig. 1, Fig. 3, which can result in the effect of fluffy fur. \n\nAnother interesting prompt is `cubism`, which can be seen in supplementary materials under /supplementary\\_material/4D/examples/jumpingjacks\\_cubism.mp4.  \nWe can see that the character's face has become more angular, typical to the style, and is obtained by changing the geometry of the head (face and hair). \n\nWe will add visual examples to the manuscript.\n\nIn our work, we focused on hyperparameters related to the base model, but in future work, it is possible to consider a loss responsible for controlling, among other things, the position of the gaussians, which would control the degree of freedom the splats are allowed to move in space; this would allow for greater control over stylization using prompts such as `Fire`. \n\n**Q2 \\[Failure cases\\]:**\n\nIt is difficult to say definitively that “the method can better imitate styles given by the text prompts (in comparison to reference images)”. We believe it strongly depends on the prompt/image and object/scene. For example, in our opinion the “mosaic” effect on the bonsai scene works better when it is conditioned by the selected image (Fig. 21\\) than when it is conditioned by (general) text “Mosaic” (Fig. 20). Nevertheless, an important element is how a given text is represented by CLIP. In general it is interesting how the model represents a given textual/visual concept.\n\nBased on our experience we have identified three common categories of failure cases mostly related to text prompts\n\n1\\. Too general concepts, less known concepts:\n\n Since we are using CLIP representation, the concept should be well represented by CLIP embedding. We considered 3 prompts:\n\n**TABLE ed80.A**\n\n| object: jumpingjacks | Impressionism |  |Painting by Claude Monet | | Woman with a Parasol – Madame Monet and Her Son | |\n| --- | --- | --- | --- | --- | --- | --- |\n|| CLIP-S | CLIP-SIM | CLIP-S | CLIP-SIM | CLIP-S | CLIP-SIM |\n|lambda_bg = 0 |23.04| 18.63 | 17.77 | 12.18 | 18.97 | 10.88 |\n|lambda_bg = 500 |21.51| 16.02 | 18.02 | 13.45 | 18.35 | 10.41 |\n|lambda_bg = 1000 |21.81| 16.59 | 18.20 | 14.27 | 18.52 | 10.65 |\n\n\nCosineSimilarity(prompt1, prompt2) \\= 0.802  \nCosineSimilarity(prompt1, prompt3) \\= 0.531  \nCosineSimilarity(prompt2, prompt3) \\= 0.382\n\nWe observed that for general prompts such as “Impressionism”, the model tends to fill the background, and hollow spaces (e.g. between fingers). This improves the quantitative results, but in our opinion it decreases the visual perception of the stylization. We support this claim using the CLIP-S metric, see the difference between lambda_bg=0 and lambda_bg=500 in Tab. ed80.A and Fig. 26 in the manuscript.\n\nThe concepts of “Impressionism” and “Painting by Claude Monet” are well represented by the CLIP model. Additionally, they are closely related, which is visually supported by the models trained using those prompts. On the other hand, concepts such as “Woman with(...)” are heavily influenced by details like an umbrella, which spoils the visual effect in this case. This pattern is shown by the CLIP-SIM metric. Due to the rebuttal rules we are not allowed to include visual comparison in our response, we will add it to the manuscript when possible.\n\n2\\. Length and detail of general concept prompts:   \nWe conducted an experiment using `jumpingjacks` in which we considered three prompt lengths:\n\n**TABLE ed80.B**\n\n|  | Lotus flower |  | A soft pink lotus flower with delicate petals and a bright yellow center, resting gently on a pond |  | A close-up of a blooming pink lotus flower, its intricate petals radiating around a vivid yellow core, floating gracefully on the water with a gentle reflection beneath and soft green lily pads surrounding it |  |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n|  | CLIP-S | CLIP- SIM | CLIP-S | CLIP- SIM | CLIP-S | CLIP- SIM |\n| lambda\\_bg \\= 0 | 24.97 | 22.24 | 23.36 | 19.57 | 24.03 | 21.88 |\n| lambda\\_bg \\= 500 | 24.85 | 20.78 | 19.46 | 15.87 | 20.34 | 19.15 |\n| lambda\\_bg \\= 1000 | 24.71 | 20.82 | 19.25 | 15.81 | 20.90 | 19.00 |\n\n\nWe noticed that if a shorter prompt is considered, the CLIP model finds a certain representation of a specific word/short prompt related to a general concept. Creating a nice and consistent style, similar to Fig. 2\\. If a longer prompt is used, we can expect an averaged representation of the concepts found in the prompt, which may cause some inaccuracies in the stylized image. We will add a visual comparison to the manuscripts. \n\nIn both cases (1, 2), the styling emphasizes artifacts, especially in the background. We can mitigate the artifacts in the background using `bg_loss` and `patch_size`. This is particularly evident in detailed areas, e.g. on the hands (see Fig. 26, 27). But, visual assessment is very difficult and subjective.\n\n3\\. Concepts that are difficult to represent, such as `hope`, country names, `random` or `beauty`.\n\n We will add a comparison and additional experiments to the manuscripts.\n\n**Q3 \\[Diversity of reference images or text prompts\\]:** \n\nFor the user study, we prepared prompts and corresponding images, which we presented in Fig. 19, Fig. 20 (text prompts), Fig. 21, Fig. 22 (image prompts). The number of object-stylization pairs was prepared so that the average time to complete the survey was 10 minutes. A detailed description of the user study can be found in B.2.2. For evaluations based on text prompts, we wanted to avoid introducing visual bias, and we did not attach any reference images. In the context of “Starry Night by Vincent van Gogh” and “Scream by Edvard Munch”. We asked an additional question at the end of the survey: “Were you familiar with the following images before?” Users could answer “Yes” or “No.”\n\nHowever, in our work, we indicate very different text prompts. In particular, supplementary\\_material/4D/examples/trex\\_many\\_styles.mp4 and supplementary\\_material/4D/examples/lego\\_many\\_styles. mp4, you can find over 26 different prompts; we agree that there are fewer for image conditioning (about 8; see Figs. 6, 8, 10, 12\\)\n\nWe will add this information to the main paper to improve the clarity of the work.\n\nWe thank the reviewer once again for feedback. We strongly believe that the additional experiments have further strengthened the contribution of our work. If there are any remaining questions or points requiring clarification, we will be happy to address them."}, {"review_id": "92rDoPk5dE", "reviewer": "Reviewer_XWe5", "summary": "This paper aims to handle the style transfer problem of Gaussian Splatting (GS) based representations. This paper proposes CLIPGaussian, the first unified style transfer framework that supports text-/image-based style guidance and multiple modalities, including: 2D images, videos, 3D-based GS and 4D-based GS. The experimental results demonstrate superior style fidelity and consistency across all tasks (image/video/3D/4D style transfer).", "questions": "1. **[Metric of 3D Consistency].** In line 270-274, the authors briefly introduce the utilized metrics: CLIP-SIM, CLIP-S, CLIP-CONS, and CLIP-F. CLIP-CONS is used to evaluate the temporal consistency. From Appendix B.1, I find that the cross-frame consistency is evaluated via the CLIP embeddings. However, it is not convincing enough because the consistency of local regions or pixels should also be considered. Could the authors provide short-range / long-range consistency following [1,2]?\n2. **[Quantitative Evaluation of Consistency in 4D Style Transfer].** As discussed in Weakness #2, the quantitative evaluation of temporal consistency in 4D style transfer is important for proving whether CLIPGaussian can effectively preserve the temporal consistency.\n3. **[Spatial-Temporal Consistency].** For 4D style transfer, both the spatial and temporal consistency should be considered. In practical, the temporal consistency might be a troublesome issue. Could CLIPGaussian effectively handle this issue? From the methodology part, I can only find the modified multi-view rendering loss which can ensure the spatial consistency, but the temporal consistency among different time in local regions lack explicit regularization.\n\n[1] StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields.\n\n[2] StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. CLIPGaussian can be simply extended to style transfer tasks of different modalities including: images, videos, 3D and 4D scenes.\n2. CLIPGaussian can support both text-based style guidance and image-based style guidance.\n3. The authors conduct solid experiments on 3D, image, video style transfer, demonstrating the great performance of CLIPGaussian.\n\n**Weakness:**\n1. The quantitative metrics of temporal consistency (line 270-274) are based on CLIP embeddings. The evaluation of temporal consistency in local regions/pixels should also be considered.\n2. Though the authors provide the quantitative evaluation of consistency in 3D style transfer, but the consistency evaluation in 4D style transfer is missing. Since 4D style transfer is also an important task that CLIPGaussian can solve, it is not convincing enough with only the results of 3D style transfer.", "was_revised": true, "final_justification": "The authors have addressed my previous concerns about the consistency issues. I have no further questions and keep my positive rating for this work.", "rebuttal": "We would like to thank the Reviewer for the kind words and interesting indication of gaps, we believe that the newly designed experiments will indeed help to improve the work.\n\n**W1/Q1 \\[Metric of 3D Consistency\\]**\n\nAs suggested, we evaluated the CLIPGaussian using short-range and long-range consistency following the implementation and settings from StyleRF. Specifically we warp one view to the other according to the optical flow using softmax splatting, and then compute the masked RMSE and LPIPS scores to measure the stylization consistency. For short-range we average over pairs of i-th and (i+1)-th frames and for long-range we take i-th and (i+7)-th frames. We evaluate 3D-CLIPGaussian and baselines on the same data as in the paper. Additionally, as asked by the reviewers **8XY8** and **xZpT** we evaluate against two more baselines, SGSST (CVPR 2025\\) and ABC-GS (ICME 2025). Moreover, we have included results obtained using CLIPGaussian with an alternative set of hyperparameters (lambda\\_p​=35, feature\\_lr \\= 0.002), which produces lighter stylization. We call this variant “**CLIPGaussian-Lite**”. It is important to note that this model still outperforms other methods in terms of stylization quality (CLIP-S and CLIP-SIM), with the exception of G-Style, which achieves slightly higher scores at the cost of more than doubling the model size (see table Tab. 8XY8.A in response to reviewer 8XY8).\n\n**TABLE XWe5.A: Consistency metrics for 3D**\n\n| Model | Short-range consistency |  | Long-range consistency |  |\n| ----- | :---- | :---- | :---- | :---- |\n|  | LPIPS | RMSE | LPIPS | RMSE |\n| Original Videos | 0.053 | 0.047 | 0.140 | 0.123 |\n|  |  |  |  |  |\n|  |  | Text-conditioned  |  |  |\n| I-GS2GS | **0.048** | **0.043** | 0.148 | 0.131 |\n| DGE | 0.055 | 0.044 | 0.148 | 0.120 |\n| **CLIPGaussian-Lite** | 0.051 | **0.043** | **0.132** | **0.117** |\n| **CLIPGaussian** | 0.057 | 0.051 | 0.148 | 0.125 |\n|  |  |  |  |  |\n|  |  | Image-conditioned |  |  |\n| StyleGaussian | 0.052 | 0.061 | 0.145 | 0.144 |\n| SGSST | **0.040** | 0.050 | **0.139** | 0.142 |\n| ABC-GS | 0.048 | **0.043** | 0.141 | 0.126 |\n| G-Style | 0.054 | 0.049 | 0.145 | 0.136 |\n| **CLIPGaussian-Lite** | 0.055 | 0.045 | **0.139** | **0.120** |\n| **CLIPGaussian** | 0.064 | 0.055 | 0.162 | 0.132 |\n\nAdditionally we also measure short-range and long-range consistency for video style transfer. The table can be found in response to reviewer **8XY8 (TABLE 8XY8.C)**.\n\n**W2/Q2 \\[Quantitative Evaluation of Consistency in 4D Style Transfer\\] / Q3 \\[Spatial-Temporal Consistency\\]:**\n\nCLIPGaussian is built on top of specialized architectures tailored to each modality. For 4D data, our framework is implemented on top of D-MiSo. As input, we use a fully trained D-MiSo model, which already addresses temporal consistency through a dedicated deformation network that models changes in the Gaussians throughout time. During stylization, we keep the weights of this network fixed to ensure that the stylization process does not directly affect temporal consistency. We assume that D-MiSo (or any other 4D model adapted for use with CLIPGaussian) handles this aspect. Nevertheless, in response to the reviewer's suggestion, we evaluate two types of consistency in 4D data:\n\n1\\. The first is consistency across different views at a fixed moment in time. This is similar to the 3D setup, as described in Equations 11 and 12\\. To evaluate this GT is required for each view at a given time  \n2\\. The second is temporal consistency, where we fix the camera and analyze how the object changes over time.\n\nIn the supplementary material, we included visual examples that reflect both camera changes and temporal change for example:  \nsupplementary\\_material/4D/examples/jumpingjacks\\_cubism.mp4 \n\nUnfortunately, the D-NeRF dataset does not provide GT across time for each camera. Only one time point is available in the data for each camera. However, we agree with the reviewer that in both cases, considering the metrics is very important. Therefore, we decided to conduct two experiments, supplementing Tab. 13: \n\n1\\. For a set of time steps {0, 0.1, 0.2, ..., 1}, we used the test cameras from NeRF-Synthetic (200 views, as in the jumpingjacks\\_cubism.mp4) to generate reference images using the base D-MiSo model. Then, we generated the corresponding stylized images and computed the mean CLIP-F and CLIP-CONS metrics for time steps\n\n**TABLE XWe5.B:** \n\n| dataset | hook | jumpingjacks | trex | bouncingballs | hellwarrior | mutant | standup |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n|  |  |  |  |  |  |  |  \n|  |  |  | CLIP-F |  |  |  |  |\n| batch=1 | 97.56 | 98.41 | 97.36 | 98.73 | 98.41 | 98.01 | 98.03 |\n| 2 | 97.37 | 98.15 | 97.34 | 98.66 | 98.23 | 97.95 | 97.87 |\n| 4 | 97.66 | 98.35 | 97.68 | 98.33 | 98.26 | 97.65 | 98.19 |\n|  |  |  |  |  |  |  |  \n|  |  |  | CLIP-CONS  |  |  |  |  |\n| 1 | 2.1 | 2.31 | 2.25 | 0.66 | 5.64 | 1.78 | 2.60 |\n| 2 | 1.7 | 2.80 | 3.24 | 1.03 | 5.72 | 1.92 | 3.02 |\n| 4 | 2.7 | 2.97 | 2.97 | 0.53 | 6.29 | 2.43 | 3.68 |\n\nFrom this experiment, we can see that it is difficult to indisputably determine which styling is best based on the selected metrics. We emphasize this in line 778 that visual comparison (Fig. 28\\) reveals substantial qualitative differences in output fidelity. Fig. 8 shows that with a larger batch, the shape of the object is better preserved (this is clearly visible for patch=32; where the mutant's head was more accurately reproduced only when we used batch=4 ). We will add additional metrics to the manuscript and describe our observations clearly.\n\n2\\. For each test camera from the D-NeRF dataset, we generated stylized images at {0, 0.05, 0.1, ..., 1} time points. Next, we calculated the average metrics to evaluate temporal consistency across time for each camera.\n\n**TABLE XWe5.C**\n\n| dataset | hook | jumpingjacks | trex | bouncingballs | hellwarrior | mutant | standup |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n|  |  |  |  |  |  |  |  |\n| Short-range consistency: LPIPS |  |  |  |  |  |  |  |\n| 1 | 0.017 | 0.019 | 0.012 | 0.013 | 0.023 | 0.007 | 0.009 |\n| 2 | 0.017 | 0.018 | 0.012 | 0.012 | 0.025 | 0.007 | 0.010 |\n| 4 | 0.017 | 0.017 | 0.012 | 0.013 | 0.026 | 0.007 | 0.009 |\n| D-MiSo | 0.009 | 0.011 | 0.008 | 0.007 | 0.018 | 0.006 | 0.006 |\n|  |  |  |  |  |  |  |  |\n| Short-range consistency: RMSE |  |  |  |  |  |  |  |\n| 1 | 0.046 | 0.061 | 0.046 | 0.021 | 0.055 | 0.028 | 0.031 |\n| 2 | 0.045 | 0.058 | 0.044 | 0.020 | 0.056 | 0.028 | 0.032 |\n| 4 | 0.044 | 0.055 | 0.043 | 0.021 | 0.057 | 0.028 | 0.031 |\n| D-MiSo | 0.023 | 0.036 | 0.029 | 0.018 | 0.043 | 0.017 | 0.018 |\n|  |  |  |  |  |  |  |  |\n| Long-range consistency:  LPIPS |  |  |  |  |  |  |  |\n| 1 | 0.028 | 0.062 | 0.049 | 0.043 | 0.027 | 0.044 | 0.031 |\n| 2 | 0.025 | 0.063 | 0.050 | 0.043 | 0.026 | 0.043 | 0.030 |\n| 4 | 0.026 | 0.063 | 0.048 | 0.040 | 0.027 | 0.039 | 0.029 |\n| D-MiSo | 0.016 | 0.046 | 0.031 | 0.028 | 0.020 | 0.031 | 0.021 |\n|  |  |  |  |  |  |  |  |\n| Long-range consistency: RMSE  |  |  |  |  |  |  |  |\n| 1 | 0.049 | 0.043 | 0.031 | 0.028 | 0.062 | 0.027 | 0.044 |\n| 2 | 0.050 | 0.043 | 0.030 | 0.025 | 0.063 | 0.026 | 0.043 |\n| 4 | 0.048 | 0.040 | 0.029 | 0.026 | 0.063 | 0.027 | 0.039 |\n| D-MiSo | 0.031 | 0.028 | 0.021 | 0.016 | 0.046 | 0.020 | 0.031 |\n\nAn interesting observation is that in almost every case, when using selected hyperparameters, we see an increase in metrics compared to the base model. In our case, a lot depends on stage 1; a good idea for future work would be to check the performance of colors represented using RGB instead of spherical harmonics in order to avoid light inconsistencies between views. We will include this observation in the manuscript as well. \n\n**TABLE XWe5.D** \n\n|  | Fire |  |  |  | Lotus flower  |  |  |  | The Great Wave of Kanagawa by Katsushika Hokusai |  |  |  |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n|  | Short-range Consistency |  | Long-range Consistency |  | Short-range Consistency |  | Long-range Consistency |  | Short-range Consistency |  | Long-range Consistency |  |\n| batch=4; num\\_crops=64; | LPIPS | RMSE | LPIPS | RMSE | LPIPS | RMSE | LPIPS | RMSE | LPIPS | RMSE | LPIPS | RMSE |\n| hellwarrior | 0.022 | 0.054 | 0.053 | 0.120 | 0.023 | 0.052 | 0.058 | 0.120 | 0.026 | 0.058 | 0.061 | 0.128 |\n| hook | 0.015 | 0.040 | 0.043 | 0.097 | 0.014 | 0.040 | 0.044 | 0.096 | 0.016 | 0.048 | 0.045 | 0.102 |\n| jumpingjacks | 0.015 | 0.051 | 0.035 | 0.096 | 0.015 | 0.049 | 0.036 | 0.093 | 0.018 | 0.057 | 0.037 | 0.095 |\n| trex | 0.010 | 0.037 | 0.025 | 0.071 | 0.010 | 0.036 | 0.026 | 0.070 | 0.011 | 0.043 | 0.027 | 0.079 |\n\nIt is interesting that despite differences in the (well-represented) prompt, the metrics usually behave very similarly for the selected object and the changes are minor. This emphasizes the importance of object reconstruction (stage 1). Nevertheless, it also shows that our model yields consistent results across different prompts.\n\nWe sincerely thank the reviewer once again, as the new experiments provide additional insights into our model. We will include both the quantitative and qualitative results in the paper. To further highlight the effect of time and viewpoint changes, we will also include visualizations for additional objects, showing reconstructions and stylized versions at selected moments and camera positions."}, {"review_id": "CMdVLOs2tF", "reviewer": "Reviewer_8XY8", "summary": "The paper introduces a universal stylization module, CLIPGaussian, capable of performing style transfer across multiple modalities, including 2D images, video, 3D objects, and 4D dynamic scenes. The authors instantiate their approach using Gaussian Splatting (GS) as the underlying 3D representation. For content preservation, they employ a perceptual loss computed using the conv4_2 and conv5_2 layers of VGG-19. The style supervision is two-fold: (i) a Directional CLIP loss, which captures global style alignment by enforcing directional similarity between CLIP embeddings of the content and style prompts, and (ii) a Patch CLIP loss, which encourages local style fidelity by aggregating directional CLIP losses over spatial patches of the stylized output. This modular formulation enables consistent and flexible stylization across diverse input domains.", "questions": "Can you provide stronger cross-modality comparisons—especially in 2D, video, and 4D domains?\n The claim of universal stylization across 2D, video, 3D, and 4D is central to the paper. However, the empirical support is heavily skewed toward 3D, with limited or missing comparisons in other domains. For 2D, please consider comparisons with AdaAttN, StyTr², Style Injection in Diffusion. For 4D, results for image-based style transfer seem missing. For video, methods like ViSt3D, ReReVST, MCCNet could serve as strong baselines.\n\n ➤ Clarifying this and extending comparisons could significantly improve the perceived quality and significance of the work.\n\n\nCan you report standard temporal consistency metrics (e.g., optical flow-based) for video stylization?\n Relying solely on CLIP directional similarity may overlook frame-to-frame consistency issues. Reporting temporal smoothness metrics, averaged over 15–20 video examples (as is standard in video stylization literature), would help substantiate the method's applicability in real-world video scenarios.\n\n ➤ A strong quantitative temporal evaluation would likely lead to a higher score in “Quality.”\n\n\nCould you improve the user study design and provide clearer analysis of results?\n The current user study includes only the 3D modality and fewer than 15 examples, which is insufficient given the paper’s multi-modal scope. Also, rating-based user scoring is hard to interpret compared to preference voting, which is standard in prior work. A clearer textual interpretation of plots (e.g., Fig. 14–17) is also missing.\n\n ➤ An improved and more interpretable user study across all modalities could raise both “Quality” and “Clarity.”\n\n\nDo you plan to address the hallucinated artifacts that conflict with content preservation claims?\n In some stylized results (e.g., Fig. 10 and 11), there appear to be hallucinated face-like structures that contradict the claim of content preservation. Is this a limitation of CLIP guidance or an artifact of the Gaussian Splatting representation? Could it be mitigated with an additional loss or mask?\n\n ➤ Clarifying this would strengthen the reliability of your approach and could improve the score.\n\n\nCan you correct the figure presentation inconsistencies and table caption errors?\n Figures showing stylized results often omit or downscale the content image, making visual assessment difficult. Also, the captions of Table 9 and 10 are reversed. These issues hurt clarity and reader experience.\n\n ➤ Addressing these would improve the “Clarity” rating of the paper.\n\n\nScore Revision Criteria\nIf the authors provide thorough comparisons across all modalities, improve quantitative evaluation (especially for video), and clarify or resolve content preservation concerns, the “Quality” and “Significance” scores could be raised.\n\nImprovements in user study design and presentation clarity may lead to a higher “Clarity” score as well.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "### **Strengths**\n\n* The paper proposes an interesting and non-trivial idea of **universal stylization** using **Gaussian Splatting** that supports multiple modalities, including **2D images, video, 3D objects, and 4D dynamic scenes**. This unified formulation is both timely and impactful.\n\n* The experimental setup demonstrates that the proposed method is **easy to train and reproducible**, with results obtainable on **reasonable hardware**. This enhances the practical usability of the approach.\n\n* The **supplementary results in the 3D domain** are qualitatively strong, especially under both **text- and image-based style prompts**, suggesting the method’s effectiveness in this modality.\n\n### **Weaknesses**\n\n#### **Major Weaknesses**\n\n1. **Lack of consistent and appropriately scaled content visualization in figures**: In most figures (e.g., Fig. 1, 5, 6, 10, 11), the content image is shown at a reduced scale compared to the stylized output, and in others (Fig. 3, 7, 8, 9), it is completely missing. A direct visual comparison is difficult, undermining the reader’s ability to evaluate content preservation and style transfer quality.\n\n2. **Contradiction between content preservation claims and visual evidence**: At line 327, the authors claim that CLIPGaussian preserves the original content. However, in Fig. 10 (bottom row) and Fig. 11 (top row), the stylized outputs contain **hallucinated human-face-like structures**, which clearly contradict the content fidelity claim.\n\n3. **Insufficient modality-wise comparisons**:\n\n   * **2D**: The paper compares with only two methods on very limited examples. A comprehensive comparison with **SOTA methods** such as *AdaAttN*, *StyTr²*, and *Style Injection in Diffusion* is missing.\n   * **3D**: No comparisons are provided against relevant works such as *SGSST* (which is directly related), *StylizedNeRF*, *Style-NeRF2NeRF*, or *MM-NeRF*.\n   * **Video**: The evaluation is limited to **short, low-motion clips** (e.g., from DAVIS), with no comparison to methods such as *AdaAttN*, *StyleMaster*, *ViSt3D*, *MCCNet*, or *ReReVST*.\n   * **4D**: The supplementary includes **only text-guided** results; **image-guided stylization results are missing**, and comparisons with *StyleDyRF*, *Instruct 4D-to-4D*, etc., are not discussed.\n     This undermines the core claim of **universal applicability** and requires a much more thorough evaluation across all supported modalities.\n\n4. **Limited and insufficiently designed user study**: The user study is based on **fewer than 15–20 examples** and is restricted to the **3D object domain**, ignoring other modalities. Additionally, the methodology (rating-based scoring) deviates from **standard forced-choice voting** in literature, making the results harder to interpret. Moreover, no **qualitative discussion** is provided for the results in Fig. 14–17.\n\n5. **Inadequate evaluation of temporal consistency in videos**: The paper relies on **CLIP directional similarity**, which suffers from key limitations:\n\n   * **Semantic granularity loss** (loss of fine details),\n   * **Motion insensitivity**, and\n   * Inability to capture **localized artifacts**.\n\n   To ensure temporal coherence, authors should report **optical flow-based metrics** averaged over **15–20 examples**, as is common in the video stylization literature.\n\n#### **Minor Weaknesses**\n\n6. **Caption mismatch in tables**: The captions of **Table 9** and **Table 10** are swapped, which could lead to reader confusion.", "was_revised": true, "final_justification": "Based on the newly reported **quantitative results** and the authors’ **commitment to address visual and qualitative aspects in the final submission**, I find the rebuttal satisfactory and convincing. I have accordingly **improved my score** to reflect this update.", "rebuttal": "Thank you for your very insightful review.\n\n**Q4/W2 [hallucinated human-face-like structures]:**\n\nThank you for this very interesting question. The appearance of human-face-like structures in the output is an expected outcome, resulting from the combination of the chosen `patch_size` and the specific style prompt.\n\nFor example:  \n- in Fig. 10 (bottom row), the scream-face-like patterns are a natural result of the characteristic visual elements in the refereed image \"The Scream by Edvard Munch\".\n- in Fig. 11 (top row) the prompt “Beksinski painting” caused the creation of human-like structures.  This is consistent with Beksinski’s style, known for dark landscapes and ghostly, human-like characters.\n\nIn both cases, these structures are not hallucinations but rather stylizations aligned with the chosen references. The`patch_size` parameter controls the local stylization. For example for  `patch_size=128`, each patch of size 128x128 is stylized. For this reason, detailed repetition can be controlled through the patch_size param. This effect is illustrated in Fig. 23 (Lego scene). Increasing the`patch_size` results in more star-like structures caused by the influence of the prompt “Starry Night by Vincent van Gogh.”\n\nWe will add a clarifying note to the experimental description\n\n**Q1/W3:**\n\n- **2D:** The paper compares **only two methods**...\n\nWe compare the performance of our model on 2D images with AdaIN and **StyTr²** in Fig. 10 and 31, with InstructPix2Pix and ChatGPT (DALL·E) in Fig. 11, and with CLIPStyler and FastCLIPStyler in Fig. 30. In total, **we compare our model against six other methods, not just two**. Moreover, in the manuscript, we explicitly acknowledge that, given the highly developed state of 2D image stylization methods, our results in 2D may not match the visual quality achieved by large or diffusion-based models (lines 358–359). AdaAttN (2021) is an older method and can no longer be considered state of the art. While we do not directly compare to \"Style Injection in Diffusion\", we do evaluate our model against ChatGPT (DALL·E), which is also based on diffusion.\n\n- **3D:** No comparisons are provided against relevant works such as **SGSST** (which is directly related), StylizedNeRF, Style-NeRF2NeRF, or MM-NeRF.\n\nOur goal was to compare style transfer on fully trained 3D objects. NeRF-based models have significantly higher computational requirements and cannot be used in the same setting as Gaussian splats. For these reasons, we decided to compare CLIPGaussian only with other GS-based techniques. We emphasize that the base models were identical across all evaluated methods (with the exception of Instruct-GS2GS, which was incompatible). However, in response to the reviewer's suggestion, we additionally compare our model against two recent baselines: **SGSST (CVPR 2025)** and **ABC-GS (ICME 2025)**. Moreover, we have included results obtained using CLIPGaussian with an alternative set of hyperparameters (lambda_p​=35, feature_lr= 0.002), which produces lighter stylization. We call this variant **CLIPGaussian-Lite**.\nThe updated results are shown in the table below.\n\n**TABLE 8XY8.A**\n\n|Model|CLIP-S|CLIP-SIM|CLIP-F|CLIP-CONS|Memory Size|\n|-----|:----|:----|:----|:----|:----|\n|||Text-conditioned||||\n|I-GS2GS|16.80|12.03|99.19|**13.54**|-36%|\n|DGE|17.59|12.27|**99.31**|12.46|-5%|\n|**CLIPGaussian-Lite**|23.14|22.30|99.17|8.51|+0%|\n|**CLIPGaussian**|**26.86**|**26.31**|98.80|2.34|+0%|\n|||Image-conditioned||||\n|StyleGaussian|63.69|13.07|98.87|1.36|+0%|\n|SGSST|66.57|16.24|97.54|0.91|+0%|\n|ABC-GS|68.68|16.29|99.10|2.11|+0%|\n|G-Style|**76.94**|**24.94**|98.94|1.31|+126%|\n|**CLIPGaussian-Lite**|69.27|17.02|**99.16**|**5.26**|+0%|\n|**CLIPGaussian**|72.65|20.72|98.78|1.77|+0%|\n\nCLIPGaussian, in both the base and light variants, outperforms SGSST and ABC-GS in stylization quality metrics (CLIP-S and CLIP-SIM). Notably, CLIPGaussian-Light achieves the highest scores across all consistency metrics. It is important to highlight that G-Style significantly increases the model size; typically by more than a factor of two, whereas CLIPGaussian offers competitive results without altering the underlying model size.\n\n- **Video:** ...no comparison to methods such as AdaAttN, StyleMaster, ViSt3D, MCCNet, or ReReVST.\n\nSince MCCNet (2020), ReReVST (2020), and AdaAttN (2021) are relatively older methods, we chose to compare our model with more recent baselines. We acknowledge that StyleMaster would be a valuable baseline; however, as its code was unavailable at the time of the submission, it was not possible to include it in our comparison. Nevertheless, we will add them to the related work section. In response to the reviewer's suggestion, we additionally compare our model with ReReVST and ViSt3D using the same dataset and experimental setting as described in the manuscript. The updated results are shown in the table below.\n\n**TABLE 8XY8.B**\n\n|Model|CLIP-S|CLIP-SIM|CLIP-F|CLIP-CONS|\n|-----|:----|:----|:----|:----|\n|||Text-conditioned|||\n|Rerender|19.40|9.83|98.23|-0.03|\n|Text2Video|26.05|**24.99**|93.36|0.03|\n|**CLIPGaussian**|**26.25**|24.53|**99.00**|**1.92**|\n|||Image-conditioned|||\n|CCPL|18.89|8.20|97.92|-0.02|\n|UniST|15.93|3.85|99.36|5.16|\n|ViSt3D|55.92|2.75|99.18|3.28|\n|ReReVST|61.50|2.12|98.85|2.51|\n|**CLIPGaussian-Light**|63.60|8.06|**99.50**|**11.99**|\n|**CLIPGaussian**|**74.31**|**17.60**|99.18|1.27|\n\n- **4D:**...image-guided stylization results are missing, and comparisons with StyleDyRF, Instruct 4D-to-4D, etc., are not discussed\n\nInstruct 4D-to-4D has been discussed in main manuscript lines 114-121 in section Related Works and line 315-318 in section Experiments. Instruct 4D-to-4D focuses mainly on the color palette, while our approach additionally modifies the geometry of the scene. Consequently, the results obtained by CLIPGaussian contain more details, which in our opinion better resembles the referenced style. We will add this discussion. The supplementary also includes image-guided results for 4D, see Fig. 25. We will add more examples to the main manuscript.\n\nWe thank the reviewer for pointing out the StyleDyRF method, which is a relevant NeRF based zero shot style transfer approach that stylizes dynamic scenes represented by Dynamic NeRF. In contrast to our method, it supports only image based style conditioning. In our work we compared against more recent img-conditioned zero shot approaches such as 4DStyleGaussian, which similarly to our method works on a GS scene representation. We will add the discussion to the related work section.\n\n**Q2/W5 [optical flow-based metrics for video]:**\nWe conducted an additional evaluation of CLIPGaussian using short-range and long-range consistency metrics, following the implementation and settings from StyleRF. Specifically, we warp one view to another using optical flow and softmax splatting, and then compute the masked RMSE and LPIPS scores to assess stylization consistency. For short-range consistency, we average the scores over pairs of the i-th and (i+1)th frames; for long-range consistency, we use pairs of the i-th and (i+7)th frames. We evaluate Video-CLIPGaussian and the baselines on the same dataset used in the paper. The results are presented in the table below.\n\n**TABLE 8XY8.C**\n\n|Model|Short-range consistency||Long-range consistency||\n|-----|:----|:----|:----|:----|\n||LPIPS|RMSE|LPIPS|RMSE|\n|OriginalVideos|0.042|0.034|0.070|0.055|\n|||Text-conditioned|||\n|Rerender|0.062|0.040|0.132|0.077|\n|Text2Video|0.261|0.183|0.235|0.166|\n|**CLIPGaussian-Light**|**0.049**|**0.036**|**0.085**|**0.061**|\n|**CLIPGaussian**|0.084|0.057|0.152|0.095|\n|||Image-conditioned|||\n|CCPL|0.102|0.065|0.132|0.093|\n|UniST|**0.062**|0.047|0.088|0.066|\n|ViSt3D|0.081|0.043|0.121|0.063|\n|ReReVST|0.072|0.047|0.100|0.068|\n|**CLIPGaussian-Lite**|0.049|**0.035**|**0.083**|**0.061**|\n|**CLIPGaussian**|0.086|0.057|0.157|0.098|\n\nThe interpretation of these metrics is not trivial. Consistency metrics are maximized when the output is identical to the original object, which suggests poor stylization. Notably, only Text2Video deviates significantly from the other methods in terms of consistency. It is also worth noting that while Rerender achieves low metrics values, the stylization and content of the videos decreases in time **(see suplementary_material/Video/comparisons/comparison_white_wool.mp4)**. CLIPGaussian offers superior stylization quality compared to the baselines, and we believe it achieves high-quality style transfer without compromising the consistency of the object or video.\nAdditionally in the response to the reviewer **XWe5**, we evaluate 3D spatial consistency **(Table XWe5.A)** as well as 4D spatial and temporal consistencies using the same metrics **(Tables XWe5.B-D)**.\n\n**Q3/W4 [user study]:** \n\nThe user study is described in detail in App. B.2.2. It was conducted carefully using the CLICKworker platform, which enabled us to recruit a balanced group of participants (lines 641-644). The structure of the survey closely follows that used in the G-Style paper[21], including the preference ranking question described in line 660. The questions were constructed identically to the reference work. We additionally proposed question 4 (line 674) and additional question (line 680). This alignment allowed us to make meaningful comparisons while building on validated recent prior work.\nWe would like to emphasize that the user study is a supporting evaluation, not the core of our contribution. The primary contribution of our work lies in presenting a general and flexible stylization framework, which can be applied to any Gaussian-based model and supports both text- and img-driven conditioning. To our knowledge, no other existing method offers this level of adaptability\n\n**Q5, Q6/W1, MW1[Paper formatting]:**\nThank you for highlighting the editorial corrections and typos we will address them carefully.\n\nWe would be happy to address any further questions and provide clarifications."}, {"review_id": "FbefIA92Pz", "reviewer": "Reviewer_xZpT", "summary": "The paper introduces CLIPGaussian, which leverages 3D Gaussian splatting (3DGS) as its base model to edit 3D, 4D, and videos based on a simple text prompt or a style image.  The authors achieve this goal by jointly optimizing all the 3D Gaussian attributes. Additionally, the authors include various training supervision such as content loss, directional CLIP loss, patch CLIP loss, and background loss to help obtain the final edited results. Both qualitative and quantitative experiments have been conducted to validate the performance and effectiveness of the proposed method.", "questions": "The reviewer am convinced by the quality of the results. However, the reviewer still doesn't get the main contribution of the paper or how to obtain the results based on the materials provided in the submission. More analysis about (1) how each component will affect the results, (2) their motivations, and (3) the differences between previous methods will be needed. From the perspective of the reviewer, it's more like a unified model of Instruct-GS2GS.\n\nTherefore, the reviewer would rate the paper as ''borderline accept'' at the current stage.", "limitations": "Limitations have been discussed in the paper, while the paper doesn't provide potential societal impact.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n+ The results produced by the proposed method demonstrate superior quality over existing methods.\n\n+ The paper is well-written and easy to follow.\n\nWeaknesses:\n\n- Lack many evaluations. (1) While the CLIPGaussian is claimed to perform well for 3D, 4D, and videos, the authors provide comparisons with just the very baseline in each field. More comparisons with more recent methods would be required to demonstrate the performance. (2) Lack of sufficient quantitative evaluations. The two quantitative evaluations are provided in Tables 1 and 2. However, the compared methods are limited. And, the results are not obviously better than existing methods. As for the user studies provided in the appendix, maybe it's also beneficial to rate based on the quality. (3) Ablation studies regarding the loss terms would also be required.\n\n- The introduction is a little bit confusing. The authors introduce texturing, local editing, insertion, and style transfer. It would lead to a conclusion that the proposed method can address these four tast with the proposed plug-in type models. However, the paper only shows  results with style transfer.", "was_revised": true, "final_justification": "The rebuttal addresses most of my concerns. I would like to keep my original rating and encourage the authors to check the issues mentioned in the comments/conversations below.", "rebuttal": "Thank you to the reviewer for pointing out inaccuracies and offering suggestions. We’re glad the work is considered interesting and easy to follow. We hope our responses and revisions will improve clarity for future readers.\n\n**W1 [More comparisons]:**\n\nWe thank the reviewer for the suggestion. We conducted additional comparison with **SGSST** (CVPR 25) and **ABC-GS** (ICME 25) (for 3D) as well as **ViSt3D** and **ReReVST** (for video).  Moreover, we have included results obtained using CLIPGaussian with an alternative set of hyperparameters (lambda_p​=35, feature_lr = 0.002), which produces slightly lighter stylization. We call this variant CLIPGaussian-Lite. As a 4D style transfer is a new field, we already compared CLIPGaussian with all relevant baselines. The updated results for 3D and Video are shown in the tabs below.\n\n**TABLE xZpT.A**\n|Model|CLIP-S|CLIP-SIM|CLIP-F|CLIP-CONS|Memory Size|\n|-----|:----|:----|:----|:----|:----|\n|||Text-conditioned||||\n|I-GS2GS|16.80|12.03|99.19|**13.54**|-36%|\n|DGE|17.59|12.27|**99.31**|12.46|-5%|\n|**CLIPGaussian-Lite**|23.14|22.30|99.17|8.51|0%|\n|**CLIPGaussian**|**26.86**|**26.31**|98.80|2.34|0%|\n|||Image-conditioned||||\n|StyleGaussian|63.69|13.07|98.87|1.36|0%|\n|SGSST|66.57|16.24|97.54|0.91|0%|\n|ABC-GS|68.68|16.29|99.10|2.11|0%|\n|G-Style|**76.94**|**24.94**|98.94|1.31|+126%|\n|**CLIPGaussian-Lite**|69.27|17.02|**99.16**|**5.26**|0%|\n|**CLIPGaussian**|72.65|20.72|98.78|1.77|0%|\n\nCLIPGaussian, in both the base and light variants, outperforms SGSST and ABC-GS in stylization quality metrics (CLIP-S and CLIP-SIM). Notably, CLIPGaussian-Light achieves the highest scores across all consistency metrics.  It is important to highlight that G-Style significantly increases the model size; typically by more than a factor of two, whereas CLIPGaussian offers competitive results without altering the underlying model size.\n\n**TAB. xZpT.B**\n\n|Model|CLIP-S|CLIP-SIM|CLIP-F|CLIP-CONS|\n|-----|:----|:----|:----|:----|\n|||Text-conditioned|||\n|Rerender|19.40|9.83|98.23|-0.03|\n|Text2Video|26.05|**24.99**|93.36|0.03|\n|**CLIPGaussian**|**26.25**|24.53|**99.00**|**1.92**|\n|||Image-conditioned|||\n|CCPL|18.89|8.20|97.92|-0.02|\n|UniST|15.93|3.85|99.36|5.16|\n|ViSt3D|55.92|2.75|99.18|3.28|\n|ReReVST|61.50|2.12|98.85|2.51|\n|**CLIPGaussian-Light**|63.60|8.06|**99.50**|**11.99**|\n|**CLIPGaussian**|**74.31**|**17.60**|99.18|1.27|\n\nCLIPGaussian also outperforms ViSt3D and ReReVST. Additionally version with lighter stylization has the best consistency while still outperforming baselines.\n\n**W2 [Lack of sufficient quantitative evaluations]:**\nPlease find quantitative evaluation for 3D in: Tabs. 1-12, 4D: Tab. 13, Video: Tabs. 14, 15. However, as suggested by reviewers **8XY8** and **XWe5**, we conducted additional quantitative evaluation using flow-based consistency metrics for 3D, Video and 4D as well as CLIP-based consistency metrics for 4D.\n\n**TAB. xZpT.C**\n\n|Model|Short-range consistency||Long-range consistency||\n|-----|:----|:----|:----|:----|\n||LPIPS|RMSE|LPIPS|RMSE|\n|OriginalVideos|0.053|0.047|0.140|0.123|\n|||Text-conditioned|||\n|I-GS2GS|**0.048**|**0.043**|0.148|0.131|\n|DGE|0.055|0.044|0.148|0.120|\n|**CLIPGaussian-Lite**|0.051|**0.043**|**0.132**|**0.117**|\n|**CLIPGaussian**|0.057|0.051|0.148|0.125|\n|||Image-conditioned|||\n|StyleGaussian|0.052|0.061|0.145|0.144|\n|SGSST|**0.040**|0.050|**0.139**|0.142|\n|ABC-GS|0.048|**0.043**|0.141|0.126|\n|G-Style|0.054|0.049|0.145|0.136|\n|**CLIPGaussian-Lite**|0.055|0.045|**0.139**|**0.120**|\n| **CLIPGaussian** | 0.064 | 0.055 | 0.162 | 0.132 |\n\n**TAB. xZpT.D**\n\n|Model|Short-range consistency||Long-range consistency||\n|-----|:----|:----|:----|:----|\n||LPIPS|RMSE|LPIPS|RMSE|\n|OriginalVideos|0.042|0.034|0.070|0.055|\n|||Text-conditioned|||\n|Rerender|0.062|0.040|0.132|0.077|\n|Text2Video|0.261|0.183|0.235|0.166|\n|**CLIPGaussian-Light**|**0.049**|**0.036**|**0.085**|**0.061**|\n|**CLIPGaussian**|0.084|0.057|0.152|0.095|\n|||Image-conditioned|||\n|CCPL|0.102|0.065|0.132|0.093|\n|UniST|**0.062**|0.047|0.088|0.066|\n|ViSt3D|0.081|0.043|0.121|0.063|\n|ReReVST|0.072|0.047|0.100|0.068|\n|**CLIPGaussian-Lite**|0.049|**0.035**|**0.083**|**0.061**|\n|**CLIPGaussian**|0.086|0.057|0.157|0.098|\n\nIn response to the reviewer XWe5, we evaluate consistency of CLIPGaussian in 4D setting (see Tabs. XWe5.B-D).\n\nIt is important to note that the interpretation of style transfer metrics is not trivial. CLIP-S and CLIP-SIM scores would be maximized if the generated object were identical to the style image or prompt, which would clearly be an undesirable outcome. On the other hand, consistency metrics are maximized when the output is identical to the original object, which also indicates poor stylization. This trade-off can be easily seen in Tab. 11. We believe that CLIPGaussian strikes a meaningful balance between these two objectives, achieving high-quality style transfer without compromising the consistency of the object or video. Another important factor, which is not measured by any metrics in universality. All other methods work on only one medium (3D, 4D, 2D or video) and support only one modality (text or image). CLIPGaussian is the first method that works in all of these settings while being either superior or at least comparable in quality.\n\nIn case of user study, we evaluated “quality” or “visual appeal” in the Question 2: \"On a scale from \"Very Low\" to \"Very High\", how would you rate the visual appeal of each generated result?”. We will highlight it in the revised version of the main manuscript.\n\n**Q0 Main contribution of the paper**\n\nOur main contribution is a unified framework for various data modalities such as 2D, 3D, 4D, and videos etc. This broad applicability was positively highlighted by the reviewers (e.g., Rev. ed8o and Rev. XWe5). Our framework demonstrates competitive performance across modalities, which can be especially seen in the case of 3D and 4D, where it outperforms existing approaches. We acknowledge that our performance is not yet on par with current state of the art specialized approaches in the case of images, which are often based on large diffusion models or ChatGPT-like architectures. In contrast, our method is the first to propose a unified multimodal GS based style transfer approach.\n\n**Q1/ Q2 Motivation of each component and its effect**\n\nCLIPGaussian operates by directly optimizing gaussians (either in 3D, 4D, Video or Images) using losses based on CLIP embeddings. Analysis of loss terms can be found in the Appendix B.2.3 **(lines 722-736)**. Description of the losses can be found in Section 3 **(lines 217-252)**. The loss consists of four main parts:\n\n1. **Content Loss (Eq. 2)**: This is a classical loss computed using VGG-19 embeddings. During each optimization step, it encourages the generated view to preserve the content of the ground truth. In the case of video, it ensures that each generated frame retains the original semantic content; e.g., that the subject remains a camel or a bear (see Figs.12 and 13).  \n2. **Directional CLIP Loss (Eq. 3):** This component is responsible for global style transfer. It compares the CLIP embedding of a rendered view with that of the corresponding original view, both conditioned on the style prompt or style image. This encourages the overall appearance of the object to align with the desired style.  \n3. **Patch CLIP Loss (Eq. 4):** This loss is equivalent to the Directional loss but is applied to random patches of the rendered view rather than the entire image. It ensures that each patch of the rendered view is stylized. This loss is responsible for the texture of the stylized object, as it operates on smaller fragments.  \n4. **Background loss (bg loss):** This loss is applied only for 3D and 4D objects. In these cases, we aim to stylize the object itself without affecting its bg. For example, this loss ensures that the white bg behind the Lego set in Fig. 6 remains unchanged. In contrast, StyleGaussian does not enforce bg consistency on the same image. \n\n**W3 [Ablation studies regarding the loss terms]:**\nWe have already conducted an ablation study in the context of 3D stylization. The results are presented in Fig. 24 and Tabs. 12 and 13 of the manuscript. The parameters `lambda_d` and `lambda_p` correspond to the weights of their respective loss terms (directional loss and patch loss). In both cases, we examined the effect of setting these weights to zero. The effect of the bg loss can be seen on Fig. 26. We will highlight this fact in the manuscript. We believe that this will improve understanding of losses.\n\n**Q3 [The differences between previous methods will be needed] Instruct-GS2GS:**\n\nCLIPGaussian is not a unified version of Instruct-GS2GS (I-GS2GS). While I-GS2GS stylizes training images using a diffusion model before fine-tuning the Gaussian splats, CLIPGaussian directly optimizes the Gaussian representation using CLIP-based losses. This allows high-quality style transfer with both image and text conditioning, unlike I-GS2GS, which supports only text prompts. Additionally, CLIPGaussian does not rely on any pretrained diffusion models  and is applicable across modalities, whereas I-GS2GS is limited to 3D. A direct comparison with I-GS2GS is provided in Fig. 19 and 20. We will add a description of I-GS2GS to the related work section. Thank you for pointing out this omission.\n\n**L1 [Social Impact]:**\n\nClipGaussian enables cross-modal generality as a plugin based on GS models. Using CLIP as a multimodal vision and language model motivated  it to enable high-quality stylization and expanding creative possibilities. Gaussian based representation enables the creation of high-quality real-time renders which is a key challenge in computer vision, especially in broad applications in AR, gaming, and digital content creation. We will add this to manuscripts. \n\nWe are grateful for your careful review, which contributed to improving the clarity of our paper. If anything remains unclear, we would be glad to answer any questions."}]

Author rebuttal:

Meta-review: After rebuttal, all reviewers have consistent positive opinions. The AC also carefully read all comments and agree with all reviewers for acceptance.
Decision: Accept (poster)