Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles

Jiabao Lei; Kui Jia; Yangkai Lin

REVIEW 6 major objections 5 minor 14 references

Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles

T0 review · 6 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A 3D Gaussian Splatting scene can be stylized with multiple local styles by assigning each semantic region its matching style image through bipartite matching and optimizing a per-region nearest-feature style loss that combines VGG and…

desk verdict A sensible, incremental method for multi-style local 3D Gaussian Splatting stylization, let down by an incomplete experimental write-up rather than by a broken core idea. read the letter →

arxiv 2506.06846 v1 pith:V3BJXD4Z submitted 2025-06-07 cs.CV

Yangkai Lin , Jiabao Lei , Kui jia This is my paper

classification cs.CV

keywords 3DGaussianSplattingmulti-stylestylizationlocalstyletransferbipartitematchingsemanticsegmentationmulti-viewconsistencyDINOv2featuresneural

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a 3D Gaussian Splatting scene can be stylized with several different artistic styles at once by attaching a trainable semantic feature to every Gaussian, grouping the Gaussians into semantic regions, and using bipartite matching to pair each region with the most similar style image. The key algorithmic move is a semantic multi-style loss that applies a nearest-neighbor feature-matching style loss separately to each region, using both VGG local detail features and DINOv2 global features to keep the same object looking the same from different viewpoints. The paper reports that this design reduces GPU memory during stylization because each iteration optimizes only the Gaussians of one region, and it enables manual re-assignment of styles to objects after training. The reason to care is that it gives explicit, local, editable control over the appearance of 3D scenes, a step beyond global stylization methods that apply one pattern everywhere.

What carries the argument

The central object is the semantic multi-style loss (Eq. 12) together with the per-Gaussian semantic feature $e_i$ and the bipartite matching between semantic point sets $\{G_i\}$ and style images $\{S_j\}$. The loss is a region-wise extension of the nearest-neighbor feature matching loss: for each Gaussian class assigned a style image, it minimizes the cosine distance from every rendered feature to the closest style feature, on features formed by concatenating VGG19 and DINOv2 embeddings. Bipartite matching (Hungarian algorithm) supplies the class-to-style assignment either automatically, from feature similarity between rendered region images and style images, or by manual designation; the VGG+DINOv2 concatenation is what carries the multi-view consistency claim, since DINOv2's global features are what stabilizes the VGG local matching across viewpoints.

What would settle it

Render the same semantic object (for example, the truck in the Tanks and Temples scene) from two widely separated viewpoints after multi-style stylization, and compare the stylized texture on that object. If the VGG+DINOv2 matching works, the object must show the same style, color, and detail in both views; if it shows different colors or blurred, averaged details, the multi-view consistency claim fails. A second check is to corrupt the segmentation labels by swapping the masks of two objects and re-running the pipeline, which should visibly swap the assigned styles if the whole method is riding on label accuracy.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that local multi-style transfer on 3D Gaussian Splatting can be made accurate and memory-efficient by separating the scene into semantic point sets and treating style transfer as a bipartite assignment problem between those sets and the style images. Each Gaussian carries an extra trainable feature decoded by a tiny MLP into a semantic label; after the scene is reconstructed, the labels partition the Gaussians into classes, and a Hungarian-algorithm matching assigns each class a style image. A style loss is then computed per class with cosine-distance nearest-feature matching over concatenated VGG and DINOv2 features, and, because each class is optimized separately, the number of Gaussians touched per iteration drops. The paper also introduces KNN smoothing, negative-entropy regularization, and a semantic importance filter to keep the pseudo-label segmentation reliable enough for the matching to land on the right objects.

Load-bearing premise

The method succeeds only if the automatically generated semantic labels, produced by SAM and a zero-shot video tracker, are accurate and consistent across views; a wrong label on a Gaussian sends the wrong style to that object.

Editorial extensions

If this is right

A single Gaussian Splatting scene can be re-styled with different artistic patterns on different objects, and the styles can be re-assigned manually after an initial automatic match without redoing the segmentation.
Because each semantic region is optimized separately, the stylization step uses fewer Gaussians per iteration, which is what allows the reported GPU-memory reduction; larger scenes with more distinct objects will partition into smaller subsets.
The concatenation of VGG and DINOv2 features for nearest-neighbor matching is presented as the mechanism that keeps stylized details consistent across viewpoints, a direct response to the blurring and color shifts in VGG-only matching.
The method supports single-style as well as multi-style input, and treats the single-style case as one region matched to one style image, so the local-segmentation machinery does not hurt the single-style setting.
Changing to a new set of styles requires retraining the appearance of the Gaussians; the method is not an instant or zero-shot style transfer.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

We infer that the reported memory reduction is tied to the balance of semantic regions: a scene in which one class contains most Gaussians will see little savings, since the per-class optimization still has to touch nearly all points.
We infer that the method inherits the failure modes of its pseudo-labelers, so scenes with thin structures, reflections, or transparency will likely produce ambiguous labels and consequently mis-styled objects; testing on such scenes would reveal the method's ceiling.
The paper leaves implicit that DINOv2 is the load-bearing ingredient for multi-view consistency; an ablation that replaces DINOv2 with another global descriptor, or drops it entirely, would tell how much of the improvement comes from this choice alone.
A natural extension the paper does not pursue is to refine the bipartite assignment during stylization, so the cost matrix would reflect the emerging stylized appearance rather than the original content features; this could fix cases where an initially ambiguous region becomes easier to match once partially styled.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Punchline: this is a genuinely useful incremental method for multi-style local stylization of 3D Gaussian Splatting, but the experimental write-up is not complete enough to fully back the claims. The core idea—per-Gaussian semantic features plus bipartite matching between semantic regions and style images—is sensible, and the DINOv2+VGG feature matching addresses a real multi-view consistency problem in prior work. The memory savings from optimizing only the Gaussians in the matched category are concrete and clearly demonstrated.

What's new: no prior work does multi-style local stylization on GS with automatic region-style assignment via bipartite matching. The combination is new even though each component is known, and the paper gives proper credit to Gaussian Grouping, LSNeRF, ARF, and El Banani et al. No self-citation issues.

Soft spots, in order of severity: 1. SIFID is promised in the metrics section and abstract, but no SIFID table or values appear anywhere. That is not a style choice; it is a missing evaluation. 2. The central claim about correct local assignment is gated by SAM+tracker pseudo-label quality, and the paper offers no quantitative measurement of that quality—no IoU against manual labels, no sanity check on matching accuracy. The qualitative ablation in Fig 19 helps but does not close the gap. 3. Quantitative tables have no error bars or statistical tests, and the reproducibility checklist says statistical tests were used, but none are reported. The checklist also says 'no' to seeds, infrastructure, and number of runs. 4. ref-npr is excluded from the train-scene consistency table because of poor performance. That exclusion is disclosed, which is honest, but it weakens the comparison. 5. No comparison with a GS-based baseline such as StyleGaussian, which is directly relevant. The baselines are all NeRF-based, so the advantage over GS-specific stylization is not established.

None of these sink the method. The losses are plausible, the qualitative results look meaningfully better than the NeRF baselines, and the memory table is concrete. The paper is also honest about its limitations: no instant style transfer, retraining required.

Bottom line: this deserves a serious referee. The right outcome is probably major revision—add the missing SIFID numbers, run a label-quality check or report segmentation accuracy on a few manual labels, add error bars, and compare with at least one GS-based method. I would not desk-reject it.

Referee Report

6 major / 5 minor

Summary. The paper proposes Multi-StyleGS, a two-stage method for stylizing 3D Gaussian Splatting scenes with multiple reference styles. In the reconstruction stage, each Gaussian is augmented with a learnable semantic feature and a mask attribute; the semantic features are trained with SAM/tracker pseudo-labels via a cross-entropy loss and three regularizers (KNN smoothness, negative entropy, and semantic importance filtering). In the stylization stage, the Gaussians are partitioned by semantic class, a bipartite matching (Hungarian algorithm) maps classes to style images either automatically or manually, and appearance is optimized with a semantic multi-style loss that concatenates VGG and DINOv2 features for nearest-neighbor matching. Experiments on LLFF and Tanks-and-Temples compare against snerf, ARF, ref-npr, and LSNeRF, reporting multi-view consistency scores, memory usage, styling time, and a user study.

Significance. If the claims are substantiated, Multi-StyleGS would be a useful extension of 3D Gaussian Splatting to controllable multi-style editing, with practical value from the explicit representation and reduced per-iteration point count. The paper has clear strengths: a public code link, a user study, ablation studies for the regularizers and the VGG+DINOv2 matching, and a concrete memory-usage table. However, the current experimental validation is incomplete in several load-bearing places, and the central claims of superior stylization quality, multi-view consistency, and memory efficiency are not yet fully supported by the reported evidence.

major comments (6)

[Experiments, Evaluation Metrics] The paper states that SIFID is used to evaluate stylization similarity, but no SIFID results appear in the main text or in the supplementary material. Since the abstract and introduction claim that the method produces better stylization quality, the absence of the stated metric leaves the central quality claim unsupported. Please add a SIFID comparison table, or explicitly state why the metric was omitted.
[Quantitative and Qualitative Comparisons, Table 1 and supplementary Table 3] The multi-view consistency results are reported as point averages over random samples of frames, but no standard deviations, confidence intervals, per-run values, or statistical tests are provided. The text also refers to 'metrics from (Chiang et al. 2022a)' without defining them, so the reader cannot verify the measurement. With small reported differences (e.g., 1.33 vs 1.06 in supplementary Table 3), distributional evidence is needed to support the claim that the method achieves the best consistency.
[Gaussian Splatting with Semantic Features, Eq. (13)] The per-Gaussian semantic features are supervised exclusively by SAM+tracker pseudo-labels through L_seg, and the regularizers in Eqs. (5)-(8) can smooth, prune, or sharpen but cannot correct systematically wrong or view-inconsistent labels. The paper reports no quantitative evaluation of pseudo-label quality or of multi-view label consistency; Figure 19 is qualitative only. Because the bipartite matching in Eq. (12) and the local style assignment depend entirely on these labels, a quantitative label-quality evaluation (e.g., IoU against manual masks, or agreement between automatic and manual style assignment) is necessary to support the central claim.
[Additional Experiments, Table 3 note on ref-npr] The consistency evaluation explicitly excludes ref-npr on the train scene because ref-npr performs poorly there. Since one of the paper's central claims is improved multi-view consistency over baselines, dropping a baseline on a difficult scene biases the comparison in the authors' favor. Please report the excluded values or define an explicit inclusion criterion before evaluating.
[Ablation Study, Table 4] The paper claims memory-efficient training, but Table 4 reports only the average number of Gaussians optimized per iteration, not actual GPU memory consumption. Point counts are indirect evidence; a reader cannot infer peak memory from them because the per-iteration rendering cost also depends on feature dimensions, image resolution, and the number of style regions. Please report measured GPU memory (e.g., peak training memory for each method) to substantiate the memory-efficiency claim.
[Semantic Multi-style Loss, Eqs. (11), (12), (14)] The role of L_NNFM in the final objective is unclear: Eq. (11) defines L_NNFM, but Eq. (14) only lists lambda_cont L_cont + lambda_style L_style. The notation in Eq. (12) is also ambiguous ('min_{k=M(j)} g in CS_k'), and it is not clear whether L_NNFM is a component of L_style or a separate term. This should be clarified, as it directly affects reproducibility of the method.

minor comments (5)

[Reproducibility Checklist and Training Details] The stylization-stage weights lambda_cont and lambda_style, and the values of epsilon_0, epsilon_1, k, and sigma, are not reported; please list all final hyperparameters for both stages.
[Supplementary Table 4 and Table 2] Table 4 contains the typo '301,322,7' instead of '301,322.7', and the in-text references to tables are inconsistent (e.g., 'Table. 3' in the main text corresponds to a supplementary table while Table 1 is the main consistency table).
[Figure 15 caption] The caption contains the typo 'DINIv2' instead of 'DINOv2'; similar minor typos exist elsewhere, such as 'comparisions' and 'our mothed'.
[Additional Metrics] The user study collects scores from 27 participants and averages them, but no per-participant variance or inter-rater statistics are reported; a brief description of the questionnaire format would help.
[Datasets and Baselines] For the LSNeRF baseline, the paper notes that it only works on LLFF; this is a useful caveat, but the text should state explicitly that LSNeRF is therefore not compared on Tanks-and-Temples, and why it remains a valid baseline on LLFF.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: Multi-StyleGS is an optimization method evaluated on external benchmarks, and no fitted input is renamed as a prediction or justified by a load-bearing self-citation.

full rationale

The derivation chain in Multi-StyleGS is self-contained as an optimization pipeline rather than a predictive claim built on its own outputs. The semantic features per Gaussian are trained against SAM/tracker pseudo-labels through L_seg and regularizations (Eq. 13, Eqs. 5-8), and the stylization stage uses bipartite matching followed by the multi-style loss (Eq. 12). Using the same VGG/DINOv2 features for both matching and style transfer is the intended mechanism of the method, not a circular reduction: the mapping M is computed once from feature similarity and then used to guide optimization, and the final stylized images are evaluated against external benchmarks (llff, tnt) and compared with independent baselines (arf, snerf, ref-npr, lsnerf). There are no self-citations by the present authors in the reference list, no uniqueness theorem is imported from prior work by the same authors, and no fitted parameter is relabeled as a prediction. The acknowledged limitations, including reliance on pseudo-label quality and the need to retrain for each style, are correctness/configurational constraints rather than circular dependencies. The reproducibility checklist notes missing implementation details and metrics motivation, but those omissions affect reproducibility, not the circularity of the derivation.

Assumptions & free parameters 11 free parameters · 5 assumptions · 2 invented entities

The central claim rests on several hand-chosen hyperparameters (some not reported), the assumption that pseudo-label segmentation is reliable, and the assumption that DINOv2 provides 3D-consistent features. The per-Gaussian semantic feature and mask are learned model components without independent external evidence.

free parameters (11)

lambda_seg = 0.02
Weight for segmentation cross-entropy loss in reconstruction stage (Eq. 13).
lambda_KNN = 0.005
Weight for KNN smoothness regularization (Eq. 13).
lambda_NE = 0.005
Weight for negative entropy regularization (Eq. 13).
epsilon_0
Threshold for mask binarization in semantic importance filter (Eq. 7).
epsilon_1
Threshold for semantic confidence in mask binarization (Eq. 7).
lambda_cont
Weight for content loss in stylization stage (Eq. 14).
lambda_style
Weight for multi-style loss in stylization stage (Eq. 14).
lambda_mask
Weight for mask regularization (Eq. 8).
num_categories
Number of semantic categories C used to partition Gaussians for style assignment.
knn_k
Number of nearest neighbors for KNN regularization (Eq. 5).
knn_sigma
Influence radius in KNN weighting (Eq. 5).

assumptions (5)

standard math The Gaussian Splatting rendering equations (Eqs. 1-3) are valid and unchanged when adding semantic features.
Inherited from Kerbl et al. 2023; not re-derived here.
domain assumption DINOv2 features provide sufficient 3D awareness to improve multi-view consistency in feature matching.
Relies on El Banani et al. 2024; the paper does not independently validate this for stylization.
domain assumption SAM and the video tracker produce pseudo-labels that can serve as ground truth for the segmentation loss.
The paper uses these pseudo-labels as supervision (Eq. 13, L_seg) without quality control beyond the proposed regularizations.
domain assumption Neighboring Gaussians should have similar semantic features (locality assumption).
Used to justify KNN regularization (Eq. 5).
domain assumption Semantic categories correspond to the desired stylization regions in the scene.
The bipartite matching assigns styles to semantic point sets; if semantics do not align with the user's intended style regions, the result is wrong.

invented entities (2)

Per-Gaussian semantic feature e_i
purpose: Encodes the semantic category of each Gaussian for local style assignment.
Learned during reconstruction; no external falsifiable handle.
Per-Gaussian mask attribute m
purpose: Assesses semantic importance and enables pruning of non-semantic Gaussians.
Learned; no external verification.

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Cite this review

Pith. "Pith review of Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles." pith.science (2026). https://pith.science/paper/V3BJXD4Z

@misc{pith2026250606846,
  author       = {Pith},
  title        = {Pith review of: Multi-StyleGS: Stylizing Gaussian Splatting with Multiple Styles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V3BJXD4Z}},
  note         = {Machine review of arXiv:2506.06846}
}

read the original abstract

In recent years, there has been a growing demand to stylize a given 3D scene to align with the artistic style of reference images for creative purposes. While 3D Gaussian Splatting(GS) has emerged as a promising and efficient method for realistic 3D scene modeling, there remains a challenge in adapting it to stylize 3D GS to match with multiple styles through automatic local style transfer or manual designation, while maintaining memory efficiency for stylization training. In this paper, we introduce a novel 3D GS stylization solution termed Multi-StyleGS to tackle these challenges. In particular, we employ a bipartite matching mechanism to au tomatically identify correspondences between the style images and the local regions of the rendered images. To facilitate local style transfer, we introduce a novel semantic style loss function that employs a segmentation network to apply distinct styles to various objects of the scene and propose a local-global feature matching to enhance the multi-view consistency. Furthermore, this technique can achieve memory efficient training, more texture details and better color match. To better assign a robust semantic label to each Gaussian, we propose several techniques to regularize the segmentation network. As demonstrated by our comprehensive experiments, our approach outperforms existing ones in producing plausible stylization results and offering flexible editing.

Figures

Figures reproduced from arXiv: 2506.06846 by the authors.

**Figure 2.** Overview of our pipeline. It firstly reconstructs a GS model from multiple training images, and then stylize the scene [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The VGG features do not ensure consistency [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (17 more)

**Figure 4.** Figure 4: Qualitative comparisons with snerf (Nguyen et al. 2022), arf (Zhang et al. 2022), ref-npr (Zhang et al. 2023) and lsnerf [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Comparing the stylized results, from various per [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Qualitative comparisons with snerf(Nguyen et al. 2022), arf(Zhang et al. 2022) and ref-npr(Zhang et al. 2023) on tnt [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 7.** Figure 7: Qualitative comparisons with snerf (Nguyen et al. 2022), arf (Zhang et al. 2022) and ref-npr (Zhang et al. 2023) on [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

**Figure 8.** Figure 8: Qualitative comparisions with ref-npr (Zhang et al. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 4.** Figure 4: single style transfer on llff datasets with two [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 8.** Figure 8: multiple styles transfer on llff datasets. lsnerf [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: Ablation study of texture. Second row is multi-style loss with local-global matching, first row is nnfm loss(Zhang [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: Ablation Study of texture. Left image is multi [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: Figure. 4 style images [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

**Figure 12.** Figure 12: Ablation Study of multi-style transfer of Multi-Style loss. [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]

**Figure 13.** Figure 13: The colors present in the style image can al [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]

**Figure 14.** Figure 14: Ablation study of consistency, in the truck scene, from various perspectives, the same object may correspond to different VGG features, averaging out the details (as seen in subfigures with orange borders) or displaying varying colors (as seen in subfigures with gree…

**Figure 16.** Figure 16: Aditional experiments results on train scene in single style setting. Blue and purple box: clearer geometry structure; Black and green box: texture and details. score truck horse Flower avg. snerf 3.4 3.3 4.0 3.56 arf 7.2 8.0 5.6 6.93 ref-npr 5.2 4.4 7.6 5.73 our(sing…

**Figure 17.** Figure 17: Aditional experiments results on Family scene in single style setting. r e fn p r o u r r e fn p r o u r Foreground: Background: Foreground: Background [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]

**Figure 18.** Figure 18: Aditional experiments results on train and Family scene in multiple style setting. segmentation map w/o knn novel view w/o knn segmentation map with knn novel view with knn style w/o negative entropy and semantic filter style with negative entropy and semantic filter …

**Figure 19.** Figure 19: Ablation study of regularization. Our regulariza [PITH_FULL_IMAGE:figures/full_fig_p016_19.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

14 extracted references · 12 canonical work pages

[1]

When the ground truth is not that precise, correct segmenta- tion cannot be achieved

Their semantic segmentation only uses cross-entropy loss, so it requires accurate ground truth masks. When the ground truth is not that precise, correct segmenta- tion cannot be achieved

work page
[2]

InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18342–18352

Stylizednerf: consistent 3d scene stylization as styl- ized nerf via 2d-3d mutual learning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18342–18352. Jung, H.; Nam, S.; Sarafianos, N.; Yoo, S.; Sorkine- Hornung, A.; and Ranjan, R. 2024. Geometry Transfer for Stylizing Radiance Fields. InProceedings of the IEEE/CVF C...

work page 2024
[3]

It can only work on llff(Mildenhall et al

Technical problems: we conduce experiments with lsnerf offical codebase. It can only work on llff(Mildenhall et al. 2019) datasets. We try replica(Straub et al. 2019) and tnt(Knapitsch et al

work page 2019
[6]

Therefore, in most cases, the results generated by lsnerf can only exhibit color diversity and cannot exhibit diversity in texture and material

In general, different regions of the style image are of the same style (the same material and the same texture), but with different colors. Therefore, in most cases, the results generated by lsnerf can only exhibit color diversity and cannot exhibit diversity in texture and material

work page
[9]

snerf is capable of learning to transfer the color from a style image, as it relies on statistical methods of image analysis to calculate the loss, which may limit its abil- ity to capture more complex stylistic elements(texture) beyond color

work page
[10]

In some cases, blurring may occur due to geo- metric errors

snerf refines the geometry but lacks of efficient super- vision. In some cases, blurring may occur due to geo- metric errors. •arf(Zhang et al. 2022): arf proposes nnfm loss to perform precise texture transfer, and deferred back-propagation for memory reduction. However,

work page 2022
[11]

nnfm loss uses the VGG(Simonyan and Zisserman 2015a) features of the style and rendering for match- ing. The VGG features of the same object in different view may match different style feature due to the VGG feature lacks of 3D awareness, lead to the same object displaying different styles from different views

work page
[12]

•ref-npr(Zhang et al

When rendering the full-resolution image, the deferred back-propagation method still consumes a significant amount of GPU memory. •ref-npr(Zhang et al. 2023): ref-npr first styles a refer- ence view, then transfer the style of reference view to other views. We use SANET(?) to style reference view with style image. ref-npr introduces the Template-Based Sem...

work page 2023

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[13]

VGG features have no 3D awareness and using VGG for nearest search across different view can lead to multi-view inconsistency issues
[14]

Discussion of Controllable Style MethodsThere are some controllable style transfer methods: StyleRF(Liu et al

If the stylied reference image contain complex texture details, pixels from certain perspectives may not match to the pixels in the reference view, hence style transfer cannot be performed, resulting in white noise spots. Discussion of Controllable Style MethodsThere are some ...

2023
[2017]

•snerf(Nguyen-Phuoc, Liu, and Xiao 2022): snerf pro- poses a novel training scheme to reduce GPU usage and several loss to transfer style

datasets and it doesn’t work. •snerf(Nguyen-Phuoc, Liu, and Xiao 2022): snerf pro- poses a novel training scheme to reduce GPU usage and several loss to transfer style. However,

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IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(7): 8143–8158

Continuous conditional generative adversarial net- works: Novel empirical losses and label input mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(7): 8143–8158. El Banani, M.; Raj, A.; Maninis, K.-K.; Kar, A.; Li, Y .; Ru- binstein, M.; Sun, D.;...

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Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A

3D Gaussian Splatting for Real-Time Radiance Field Rendering.ACM Transactions on Graphics, 42(4). Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.- Y .; Doll´ar, P.; and Girshick, R. 2023. Segment Anything. a...

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[2024]

Luan, F.; Paris, S.; Shechtman, E.; and Bala, K

StyleGaussian: Instant 3D Style Transfer with Gaus- sian Splatting.arXiv preprint arXiv:2403.07807. Luan, F.; Paris, S.; Shechtman, E.; and Bala, K. 2017. Deep photo style transfer. InProceedings of the IEEE conference on computer vision and pattern recognition, 4990–4998. Mia...

2017 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

When the ground truth is not that precise, correct segmenta- tion cannot be achieved

Their semantic segmentation only uses cross-entropy loss, so it requires accurate ground truth masks. When the ground truth is not that precise, correct segmenta- tion cannot be achieved

work page

[2] [2]

InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18342–18352

Stylizednerf: consistent 3d scene stylization as styl- ized nerf via 2d-3d mutual learning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18342–18352. Jung, H.; Nam, S.; Sarafianos, N.; Yoo, S.; Sorkine- Hornung, A.; and Ranjan, R. 2024. Geometry Transfer for Stylizing Radiance Fields. InProceedings of the IEEE/CVF C...

work page 2024

[3] [3]

It can only work on llff(Mildenhall et al

Technical problems: we conduce experiments with lsnerf offical codebase. It can only work on llff(Mildenhall et al. 2019) datasets. We try replica(Straub et al. 2019) and tnt(Knapitsch et al

work page 2019

[4] [6]

Therefore, in most cases, the results generated by lsnerf can only exhibit color diversity and cannot exhibit diversity in texture and material

In general, different regions of the style image are of the same style (the same material and the same texture), but with different colors. Therefore, in most cases, the results generated by lsnerf can only exhibit color diversity and cannot exhibit diversity in texture and material

work page

[5] [9]

snerf is capable of learning to transfer the color from a style image, as it relies on statistical methods of image analysis to calculate the loss, which may limit its abil- ity to capture more complex stylistic elements(texture) beyond color

work page

[6] [10]

In some cases, blurring may occur due to geo- metric errors

snerf refines the geometry but lacks of efficient super- vision. In some cases, blurring may occur due to geo- metric errors. •arf(Zhang et al. 2022): arf proposes nnfm loss to perform precise texture transfer, and deferred back-propagation for memory reduction. However,

work page 2022

[7] [11]

nnfm loss uses the VGG(Simonyan and Zisserman 2015a) features of the style and rendering for match- ing. The VGG features of the same object in different view may match different style feature due to the VGG feature lacks of 3D awareness, lead to the same object displaying different styles from different views

work page

[8] [12]

•ref-npr(Zhang et al

When rendering the full-resolution image, the deferred back-propagation method still consumes a significant amount of GPU memory. •ref-npr(Zhang et al. 2023): ref-npr first styles a refer- ence view, then transfer the style of reference view to other views. We use SANET(?) to style reference view with style image. ref-npr introduces the Template-Based Sem...

work page 2023