Title: CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Venue: NeurIPS 2025
Accepted: yes
Keywords: Style Transfer

3D

4D

Video

Image

CLIP

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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nThe 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.\n\nLIMITATIONS:\nLimitations have been discussed in the paper, while the paper doesn't provide potential societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### **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.\n\nQUESTIONS:\nCan 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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).\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. **[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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThanks 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.\n\nQUESTIONS:\n**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.\n\nLIMITATIONS:\nIt can be improved if authors add more failure cases (e.g., vague prompts, diverse reference styles, sparse reference images, etc.)", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}