Pith. sign in

REVIEW 4 major objections 4 minor 56 references

ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read ArtNVG stylizes 3D scenes in 20 minutes by separating content and style controls and sharing attention across nearby views.

desk verdict A genuine, moderately novel 3DGS stylization system whose central claim holds, but the local-consistency evidence is thinner than the tables suggest and its key mechanism has an untested divergent-view regime. read the letter →

arxiv 2412.18783 v2 pith:PZMUVYWF submitted 2024-12-25 cs.CV

Zixiao Gu , Mengtian Li , Ruhua Chen , Zhongxia Ji , Sichen Guo , Zhenye Zhang , Guangnan Ye , Zuo Hu This is my paper · ORCID
classification cs.CV
keywords 3DGaussianSplattingstylizationdiffusionmodelscontent-styleseparationneighboring-viewattentionmulti-viewconsistencystyletransferzero-shot
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 introduces ArtNVG, a pipeline that applies the artistic style of a reference image to an existing 3D Gaussian Splatting scene in about 20 minutes. The core claim is that two design choices fix the weaknesses of prior 3D stylization: separating content and style controls in a diffusion model (using the CSGO projection modules and a Tile ControlNet) instead of inverting the content image, and replacing ordinary self-attention with a Neighboring-View Attention that lets nearby views share key and value features during denoising. Together these are said to preserve scene content, match the reference style, and keep local colors and textures consistent across views. The paper reports better CFSD, CSD, and CLIP-DC scores than StyleGaussian and InstantStyleGaussian, plus higher user preference.

What carries the argument

The load-bearing mechanism is the Attention-based Neighboring-View Alignment layer: for N views, the keys and values are concatenated as $K_{\mathrm{NV}} = [K_1, \ldots, K_N]^T$ and $V_{\mathrm{NV}} = [V_1, \ldots, V_N]^T$, and each view's query attends to all neighboring keys and values, with $\mathrm{NVAttn}_i = \mathrm{Attn}(Q_i, K_{\mathrm{NV}}, V_{\mathrm{NV}})$. This replaces every self-attention layer in the UNet and the content control, so the denoising of each view sees shared local features. Paired with Content-Style Separated Control (the CSGO style projection for style and the CSGO content projection plus Tile ControlNet for content), the diffusion process keeps content and style on separate channels, reducing information leakage.

What would settle it

Run the pipeline on a scene captured with wide-baseline cameras, so neighboring camera centers are far apart and overlapping content is small, then measure CFSD and CLIP-DC against the no-NV-attention variant; if consistency scores do not improve, or if visible seams appear where clusters meet, the key-value consistency assumption fails.

Watch

Extended reading notes

Core claim

The paper's central discovery is a zero-shot 3D stylization method built on 3D Gaussian Splatting that avoids two known failure modes: information leakage between content and style, and local inconsistency among neighboring views. Content and style are injected through separate cross-attention controls, and self-attention layers in both the UNet and the content control module are replaced by NV attention, which concatenates the keys and values of N camera-center-close views into one attention computation. The stylized renderings then finetune the original 3DGS scene, with an added NNFM loss to preserve high-frequency texture details. On the Tanks and Temples dataset with WikiArt styles, the method reportedly outperforms both baselines on content fidelity, style similarity, and consistency metrics.

Load-bearing premise

The method assumes that the self-attention key and value features from camera-center-close views are mutually consistent in the diffusion latent space, so that concatenating them reinforces shared details instead of injecting conflicting content.

Editorial extensions

If this is right

  • Stylization of an existing 3DGS scene takes about 20 minutes total, with no extra pre-training of new networks.
  • Content and style are controlled through separate cross-attention inputs, reducing content leakage from the style image and style leakage from the content images.
  • Neighboring views are denoised jointly rather than independently, improving local color and texture consistency.
  • Quantitative evaluation over eight scenes shows improvements on CFSD, CSD, and CLIP-DC relative to both baselines, and a user study favors ArtNVG on content fidelity, style alignment, and visual quality.
  • The number of neighboring views N trades off cluster fragmentation against inter-cluster continuity, with N = 15 yielding the best consistency in their experiments.

Reading between the lines

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

  • An extension beyond the paper: the NV-attention principle should transfer to other diffusion-based 3D editing tasks such as text-guided editing or relighting, since it only swaps self-attention layers.
  • An extension beyond the paper: the view-grouping criterion based on camera-center distance could be made content-aware, for example by grouping views with high visual overlap or similar depth maps, which may improve robustness on large-baseline captures.
  • An extension beyond the paper: separating content and style controls could combine with other ControlNet variants to add user interaction such as object removal or addition, which the paper lists as a limitation.
  • A testable extension beyond the paper: measuring local consistency with pixel-level correspondences or optical flow across neighboring stylized views would provide a more direct check than the CLIP-DC metric reported.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes ArtNVG, a 3D Gaussian Splatting (3DGS) stylization framework that applies the style of a reference image to an existing 3D scene. The method combines two main ideas: (1) Content-Style Separated Control, which uses the CSGO projection modules and a Tile ControlNet to inject content and style controls separately into cross-attention layers, and (2) Attention-based Neighboring-View Alignment, which replaces self-attention layers in the UNet and ControlNet with an attention mechanism that concatenates keys and values from nearby views (Eq. 7-8) to improve local color and texture consistency. Stylized renderings are used to finetune the original 3DGS scene with an L1 plus NNFM loss (Eq. 10). The authors report qualitative comparisons, a user study, and quantitative metrics (CFSD, CSD, CLIP-DC) against StyleGaussian and InstantStyleGaussian, claiming superior content preservation, style alignment, and local consistency.

Significance. If the claims are substantiated, ArtNVG would be a useful system for fast, reference-guided 3D stylization, a task with growing demand in film and games. The main technical novelty is the Neighboring-View Attention mechanism, which is simple and intuitively plausible, and the use of CSGO/Tile ControlNet for separated content and style control is a sensible application of externally validated components rather than a fundamentally new model. The paper's strengths include a clear system description, a user study, supplementary extended results, and an ablation that isolates the contribution of Neighboring-View Attention to local consistency. However, the quantitative evaluation is thin and does not yet establish the central consistency claim: the grouping mechanism is underspecified, the evaluation metrics are aggregated without error bars or significance tests, and the Content-Style Separated Control contribution is never ablated. These issues require further evidence before the paper's claims can be accepted.

major comments (4)
  1. [Sec. 3.2.3, Eq. (7)-(8)] The Neighboring-View Attention mechanism selects neighbors solely by camera-center distance ('camera centers are closer'), but in unbounded 360-degree scenes, cameras with close centers can point in divergent directions and thus have little or no visual overlap. In such cases, concatenating K and V from different views (Eq. 7) mixes tokens that correspond to different 3D content, which can blur details or inject conflicting content into the attention output. The paper does not add any relative-pose conditioning, positional encoding, epipolar constraint, or overlap validation to the attention mechanism, nor does it specify whether the neighboring-view groups are disjoint or overlapping and how the groups are reconciled. This is load-bearing because the central claim of improved local consistency relies on mutual compatibility of K/V features across views. Please specify the grouping algorithm precisely, validate compatibility for views with large baselines and divergent orientations, and report consistency metrics at group boundaries and across randomly selected view pairs rather than only along adjacent frames that likely fall in the same group.
  2. [Sec. 4.4, Table 1] The quantitative comparison reports only three aggregate metrics averaged over eight scenes, with no per-scene breakdown, no standard deviations, and no significance tests. The reported differences are small in several cases (e.g., CLIP-DC 0.81 vs. 0.79), so it is unclear whether the claimed superiority is robust or within run-to-run variation. Please provide per-scene results, error bars or confidence intervals, and, ideally, paired significance tests across scenes and styles. This is necessary to support the abstract and Section 4 claim that ArtNVG 'surpasses existing methods' on all three metrics.
  3. [Sec. 4.5, Table 2] The ablation study validates the Neighboring-View Attention and the NNFM loss, but it does not ablate the Content-Style Separated Control, which is listed as Contribution 2 and is central to the claimed reduction of 'information leakage.' Without a variant that replaces the CSGO/Tile ControlNet content and style controls with an inversion-based content preservation approach, the experiments do not directly support the claim that separated control reduces leakage or improves content fidelity. The 'Train from Scratch' variant ablates the pre-trained scene geometry, not this proposed component. Please add an ablation that isolates the effect of Content-Style Separated Control.
  4. [Sec. 3.2.3 and Sec. 4.4] The CLIP-DC metric in Eq. (16) measures the consistency of CLIP edit directions between adjacent frames on a rendered camera path. The paper does not specify the camera path used, and it is likely that adjacent frames on such a path are precisely the views that are clustered together in the same neighboring-view group. As a result, CLIP-DC may not detect discontinuities at group boundaries or between views with larger baselines. Please describe the path and, more importantly, evaluate consistency across group boundaries and across pairs of views that are not in the same group, since those are the cases where the proposed alignment mechanism is most at risk.
minor comments (4)
  1. [Eq. (6)] Equation (6) uses the predicted noise epsilon_t as the added noise in the DDIM update, which is not the standard DDIM sampling or inversion step and appears to be a typo; please clarify the actual sampling/inversion procedure used.
  2. [Sec. 3.2.3 and Fig. 2] The notation around Neighboring-View features is difficult to follow: K_NV and V_NV are defined as concatenations of K_i and V_i for N views, but the figure does not clearly indicate whether the query comes from a single view and whether the same group is used for all self-attention layers; please clarify with a concise formal description of the grouping and attention shapes.
  3. [Sec. 4.4 and Sec. 4.5] The 'Ours' row in Table 2 (CFSD 0.12, CSD 0.15, CLIP-DC 0.80) differs from the 'Ours' row in Table 1 (CFSD 0.14, CSD 0.13, CLIP-DC 0.81), which is confusing because the reader may expect identical numbers; please state explicitly that the tables report averages over different scene subsets.
  4. [Supplementary, Eq. (4)] In the NNFM loss definition, the min operation is written as 'min Dcos(Fr(i,j), Fs(i,j))' without specifying the index over which the minimum is taken; please write the index explicitly, following the ARF formulation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ArtNVG's reported gains are empirical comparisons against external baselines, and no evaluation metric is an input to the method's optimization or defined in terms of its outputs.

full rationale

ArtNVG's derivation chain is self-contained in the sense that its central claims are empirical rather than definitional. The method renders content images from an existing 3DGS scene, injects content and style controls using externally validated CSGO projection modules and a Tile ControlNet, replaces self-attention with the Neighboring-View attention of Eqs. (7)-(8), and finetunes the scene with an L1 plus NNFM loss. None of the evaluation metrics (CFSD, CSD, CLIP-DC) is used as a training loss, and none is defined in terms of the method's own parameters, so the reported metric improvements are measurements rather than identities. The attention concatenation in Eq. (7) is an architectural mechanism, not a fitted prediction: it does not by construction guarantee low CFSD, high CSD, or high CLIP-DC, and the ablations show that the mechanism changes measured scores. The paper cites no prior work by the present authors, so there is no self-citation chain carrying the argument. Hyperparameters such as N=15 and the CSGO scales are disclosed as design choices; selecting N by inspecting the CLIP-DC curve in Fig. 7 is a model-selection step that could make the reported consistency numbers optimistic, but it is disclosed and does not make the claim reduce to its inputs. The skeptic concern that camera-center-close views need not share content in 360-degree scenes is an assumption-validity and robustness issue, not a circularity reduction, and the Limitations section openly notes that geometric transformations are out of scope. Overall, no load-bearing step equates an output to an input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method uses no new physical or model entities; it composes existing pretrained models (3DGS, CSGO, SDXL, ControlNet, IP-Adapter) and a new attention layer, which is a software component rather than an invented entity.

free parameters (4)
  • Neighboring view count N = 15
    Chosen after sweeping N over {6,8,10,15,20,30} and selecting the value with best CLIP-DC in Fig. 7.
  • content scale = 1.0
    Hand-set hyperparameter for CSGO content control; reported in supplementary section A without search.
  • style scale = 0.6
    Reduced from CSGO's default 1.0 to preserve geometry; hand-tuned in supplementary section A.
  • control scale = 1.0
    Increased from CSGO's default 0.6 to preserve structure; hand-tuned in supplementary section A.
assumptions (4)
  • domain assumption The original 3DGS reconstruction provides faithful content images whose geometry is preserved during finetuning.
    The entire pipeline renders content images from the pre-trained scene and relies on the scene's geometry to remain valid during 1,000 finetuning iterations (Section 3.2, Section A).
  • domain assumption CSGO projection modules and the CLIP image encoder separate content and style as intended when applied to rendered 3DGS views.
    The paper transfers CSGO and IP-Adapter pretrained components to 3D stylization without retraining them (Section 3.2.2).
  • ad hoc to paper Self-attention key/value features of neighboring views are mutually compatible in the diffusion latent space and concatenating them improves consistency without adding conflicting content.
    This is the central modeling bet of the NV-attention mechanism; the paper assumes it and tests it only through the CLIP-DC metric on limited scenes (Section 3.2.3).
  • domain assumption Metrics CFSD, CSD, and CLIP-DC adequately capture content preservation, style alignment, and cross-view consistency for stylization quality.
    Evaluation relies on these auxiliary metrics plus a small user study; the paper notes stylization is subjective (Section 4.4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization." pith.science (2026). https://pith.science/paper/PZMUVYWF

@misc{pith2026241218783,
  author       = {Pith},
  title        = {Pith review of: ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PZMUVYWF}},
  note         = {Machine review of arXiv:2412.18783}
}
read the original abstract

As demand from the film and gaming industries for 3D scenes with target styles grows, the importance of advanced 3D stylization techniques increases. However, recent methods often struggle to maintain local consistency in color and texture throughout stylized scenes, which is essential for maintaining aesthetic coherence. To solve this problem, this paper introduces ArtNVG, an innovative 3D stylization framework that efficiently generates stylized 3D scenes by leveraging reference style images. Built on 3D Gaussian Splatting (3DGS), ArtNVG achieves rapid optimization and rendering while upholding high reconstruction quality. Our framework realizes high-quality 3D stylization by incorporating two pivotal techniques: Content-Style Separated Control and Attention-based Neighboring-View Alignment. Content-Style Separated Control uses the CSGO model and the Tile ControlNet to decouple the content and style control, reducing risks of information leakage. Concurrently, Attention-based Neighboring-View Alignment ensures consistency of local colors and textures across neighboring views, significantly improving visual quality. Extensive experiments validate that ArtNVG surpasses existing methods, delivering superior results in content preservation, style alignment, and local consistency.

Figures

Figures reproduced from arXiv: 2412.18783 by the authors.

Figure 1
Figure 1. ArtNVG. Our method apply the style of a reference image to a 3D Gaussian Splatting (3DGS) scene (Left). This is realized by stylizing the renderings of 3DGS and finetuning the original scene (Right). Our contribution is a content-style separated neighboring-view aligned stylization framework, which primarily reduce the risk of information leakage and improve consistency of local colors and textures. Abstract As dema… view at source ↗
Figure 2
Figure 2. (a) Left: Overview of ArtNVG. (b) Right: Architecture of the Neighboring-View Attention Layer. Given an original 3DGS scene [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparative Analysis of Detail Consistency in Neighboring Views with and without Neighboring-View Attention. This illustration presents a comparative visualization of stylization results between two neighboring views. The regions demarcated by red bounding boxes clearly exhibit enhanced detail consistency when NV Attention is implemented, in contrast to the results obtained without NV Attention. 4.2. Qualitative Res… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Qualitative Results of Comparison. We show diverse results of stylization in various scenes with different styles. Compared to SOTA methods StyleGaussian and InstantStyleGaussian, our method achieves the best visual quality by generating locally consistent scenes with …
Figure 5
Figure 5. Figure 5: User Study. We record the user preference of our method and the baselines. Our method obtains more preference in content fidelity, style alignment, and visual quality than the baselines. (a) Content &. Style (b) Train from Scratch (c) w/o NV Attention (d) w/o NNFM Loss…
Figure 6
Figure 6. Figure 6: Qualitative Results of Ablation Study. We compare our method against several variants. The images show two example comparisons of the ”Ignatius” and the ”Horse” scenes with two different styles. Note that all images are novel view renderings from 3DGS. (b) is the resul…
Figure 7
Figure 7. Figure 7: Impact of the neighboring views number N on con￾sistency. Comparing our method with different number of neigh￾boring views N ∈ {6, 8, 10, 15, 20, 30}, we evaluate the consis￾tency across the whole 3D scene by CLIP-DC and find our method achieves the optimal consistency…
Figure 8
Figure 8. Figure 8: Impact of style scale and control scale on content structure preservation. We show stylized images using various combi￾nations of hyperparameters (style scale, control scale) ∈ {(0.6, 0.6),(0.6, 1.0),(1.0, 1.0),(1.0, 0.6)}. (c) achieves the best performance on content …
Figure 9
Figure 9. Figure 9: Extended results. We show three stylized views per scene. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Extended results. We show three stylized views per scene. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 27 canonical work pages

  1. [1]

    https://www.wikiart.org/

    Wikiart.org. https://www.wikiart.org/. 5, 12

    work page

  2. [2]

    Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5855–5864, 2021. 2

    2021

  3. [3]

    Mip-nerf 360: Unbounded anti-aliased neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition, pages 5470–5479, 2022. 2

    work page 2022

  4. [4]

    Efficient geometry-aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123–16133, 2022. 2

    2022

  5. [5]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In European conference on computer vision , pages 333–350. Springer,

  6. [6]

    Text-to-3d using gaussian splatting

    Zilong Chen, Feng Wang, Yikai Wang, and Huaping Liu. Text-to-3d using gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21401–21412, 2024. 2

    2024

  7. [7]

    Style in- jection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer

    Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo. Style in- jection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8795–8805, 2024. 5, 12 9

    work page 2024

  8. [8]

    Vica-nerf: View- consistency-aware 3d editing of neural radiance fields

    Jiahua Dong and Yu-Xiong Wang. Vica-nerf: View- consistency-aware 3d editing of neural radiance fields. Ad- vances in Neural Information Processing Systems, 36, 2024. 2, 3, 6

    work page 2024

Show all 56 references
  1. [9]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5501–5510, 2022. 2

    2022

  2. [10]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk War- burg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 12479–12488, 2023. 2

    2023

  3. [11]

    Style- nerf2nerf: 3d style transfer from style-aligned multi-view images

    Haruo Fujiwara, Yusuke Mukuta, and Tatsuya Harada. Style- nerf2nerf: 3d style transfer from style-aligned multi-view images. arXiv preprint arXiv:2406.13393, 2024. 2, 3, 4, 5, 6

    2024 arXiv

  4. [12]

    Stylenerf: A style-based 3d-aware generator for high- resolution image synthesis

    Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Stylenerf: A style-based 3d-aware generator for high- resolution image synthesis. arXiv preprint arXiv:2110.08985,

    arXiv

  5. [13]

    Instruct-nerf2nerf: Editing 3d scenes with instructions

    Ayaan Haque, Matthew Tancik, Alexei A Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerf2nerf: Editing 3d scenes with instructions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19740– 19750, 2023. 2, 3, 5, 6, 12

    2023

  6. [14]

    Learning to stylize novel views

    Hsin-Ping Huang, Hung-Yu Tseng, Saurabh Saini, Maneesh Singh, and Ming-Hsuan Yang. Learning to stylize novel views. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13869–13878, 2021. 3

    2021

  7. [15]

    Stylizednerf: consistent 3d scene stylization as styl- ized nerf via 2d-3d mutual learning

    Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. Stylizednerf: consistent 3d scene stylization as styl- ized nerf via 2d-3d mutual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18342–18352, 2022. 3

    2022

  8. [16]

    Stylesplat: 3d object style transfer with gaussian splatting

    Sahil Jain, Avik Kuthiala, Prabhdeep Singh Sethi, and Prakan- shul Saxena. Stylesplat: 3d object style transfer with gaussian splatting. arXiv preprint arXiv:2407.09473, 2024. 2, 3

    2024 arXiv

  9. [17]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019. 3

    2019

  10. [18]

    Neu- ral 3d mesh renderer

    Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. Neu- ral 3d mesh renderer. In Proceedings of the IEEE confer- ence on computer vision and pattern recognition, pages 3907– 3916, 2018. 3

    2018

  11. [19]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1, 2023. 2, 3

    2023

  12. [20]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision, pages 4015–4026, 2023. 3

    2023

  13. [21]

    Tanks and temples: Benchmarking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4), 2017. 5

    2017

  14. [22]

    Stylerf: Zero-shot 3d style transfer of neural radiance fields

    Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, and Eric P Xing. Stylerf: Zero-shot 3d style transfer of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages ...

    2023

  15. [23]

    Stylegaussian: Instant 3d style transfer with gaussian splatting

    Kunhao Liu, Fangneng Zhan, Muyu Xu, Christian Theobalt, Ling Shao, and Shijian Lu. Stylegaussian: Instant 3d style transfer with gaussian splatting. arXiv preprint arXiv:2403.07807, 2024. 2, 3, 5

    2024 arXiv

  16. [24]

    Text-guided texturing by synchronized multi-view diffusion

    Yuxin Liu, Minshan Xie, Hanyuan Liu, and Tien-Tsin Wong. Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891, 2023. 5

    2023 arXiv

  17. [25]

    Sdedit: Guided image synthesis and editing with stochastic differential equations

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations. 2

  18. [26]

    Text2mesh: Text-driven neural stylization for meshes

    Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13492– 13502, 2022. 3

    2022

  19. [27]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 2

    2021

  20. [28]

    Instant neural graphics primitives with a multiresolu- tion hash encoding

    Thomas M¨uller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolu- tion hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 2

    2022

  21. [29]

    Snerf: stylized neural implicit representations for 3d scenes

    Thu Nguyen-Phuoc, Feng Liu, and Lei Xiao. Snerf: stylized neural implicit representations for 3d scenes. ACM Transac- tions on Graphics (TOG), 41(4):1–11, 2022. 3

    2022

  22. [30]

    Giraffe: Represent- ing scenes as compositional generative neural feature fields

    Michael Niemeyer and Andreas Geiger. Giraffe: Represent- ing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453–11464, 2021. 2

    2021

  23. [31]

    D-nerf: Neural radiance fields for dynamic scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 10318–10327, 2021. 2

    2021

  24. [32]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 3

    2022

  25. [33]

    Gaussian splatting in style

    Abhishek Saroha, Mariia Gladkova, Cecilia Curreli, Dominik Muhle, Tarun Yenamandra, and Daniel Cremers. Gaussian splatting in style. arXiv preprint arXiv:2403.08498, 2024. 3

    2024 arXiv

  26. [34]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2

    2016

  27. [35]

    Graf: Generative radiance fields for 3d-aware im- age synthesis

    Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware im- age synthesis. Advances in Neural Information Processing Systems, 33:20154–20166, 2020. 2 10

    2020

  28. [36]

    Very deep convolutional networks for large- scale image recognition

    Karen Simonyan. Very deep convolutional networks for large- scale image recognition. arXiv preprint arXiv:1409.1556 ,

    arXiv

  29. [37]

    Measuring style similarity in diffusion models

    Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shra- may Palta, Micah Goldblum, Jonas Geiping, Abhinav Shri- vastava, and Tom Goldstein. Measuring style similarity in diffusion models. arXiv preprint arXiv:2404.01292, 2024. 5, 12

    2024 arXiv

  30. [38]

    Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction

    Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5459– 5469, 2022. 2

    2022

  31. [39]

    Nerfstudio: A modular framework for neural radiance field development

    Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, et al. Nerfstudio: A modular framework for neural radiance field development. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–12,...

    2023

  32. [40]

    Instruct-gs2gs: Editing 3d gaussian splats with instructions, 2024

    Cyrus Vachha and Ayaan Haque. Instruct-gs2gs: Editing 3d gaussian splats with instructions, 2024. 2, 3, 6

    2024

  33. [41]

    Diffusers: State-of-the-art diffusion models

    Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers,

  34. [42]

    Nerf-art: Text-driven neural radiance fields stylization

    Can Wang, Ruixiang Jiang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Nerf-art: Text-driven neural radiance fields stylization. IEEE Transactions on Visualiza- tion and Computer Graphics, 2023. 3

    2023

  35. [43]

    Gaussianeditor: Editing 3d gaussians delicately with text instructions

    Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, and Qi Tian. Gaussianeditor: Editing 3d gaussians delicately with text instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20902– 20911, 2024. 2

    2024

  36. [44]

    Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction

    Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689, 2021. 2

    2021 arXiv

  37. [45]

    Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction

    Yiming Wang, Qin Han, Marc Habermann, Kostas Daniilidis, Christian Theobalt, and Lingjie Liu. Neus2: Fast learning of neural implicit surfaces for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3295–3306, 2023. 2

    2023

  38. [46]

    Gaussctrl: multi-view consistent text-driven 3d gaussian splatting editing

    Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: multi-view consistent text-driven 3d gaussian splatting editing. arXiv preprint arXiv:2403.08733, 2024. 2, 3, 5, 6

    2024 arXiv

  39. [47]

    Csgo: Content- style composition in text-to-image generation

    Peng Xing, Haofan Wang, Yanpeng Sun, Qixun Wang, Xu Bai, Hao Ai, Renyuan Huang, and Zechao Li. Csgo: Content- style composition in text-to-image generation. arXiv preprint arXiv:2408.16766, 2024. 2, 3, 4, 5, 12

    2024 arXiv

  40. [48]

    Desrf: Deformable stylized radiance field

    Shiyao Xu, Lingzhi Li, Li Shen, and Zhouhui Lian. Desrf: Deformable stylized radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 709–718, 2023. 3

    2023

  41. [49]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. 2023. 12

    2023

  42. [50]

    Gaussian- dreamer: Fast generation from text to 3d gaussian splatting with point cloud priors

    Taoran Yi, Jiemin Fang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussian- dreamer: Fast generation from text to 3d gaussian splatting with point cloud priors. arXiv preprint arXiv:2310.08529,

    arXiv

  43. [51]

    3dstylenet: Creating 3d shapes with geometric and texture style variations

    Kangxue Yin, Jun Gao, Maria Shugrina, Sameh Khamis, and Sanja Fidler. 3dstylenet: Creating 3d shapes with geometric and texture style variations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12456– 12465, 2021. 3

    2021

  44. [52]

    Instantstylegaussian: Efficient art style transfer with 3d gaussian splatting

    Xin-Yi Yu, Jun-Xin Yu, Li-Bo Zhou, Yan Wei, and Lin-Lin Ou. Instantstylegaussian: Efficient art style transfer with 3d gaussian splatting. arXiv preprint arXiv:2408.04249, 2024. 2, 3, 4, 5

    2024 arXiv

  45. [53]

    Coarf: Controllable 3d artistic style transfer for radiance fields

    Deheng Zhang, Clara Fernandez-Labrador, and Christopher Schroers. Coarf: Controllable 3d artistic style transfer for radiance fields. In 2024 International Conference on 3D Vision (3DV), pages 612–622. IEEE, 2024. 2

    2024

  46. [54]

    Stylizedgs: Con- trollable stylization for 3d gaussian splatting

    Dingxi Zhang, Yu-Jie Yuan, Zhuoxun Chen, Fang-Lue Zhang, Zhenliang He, Shiguang Shan, and Lin Gao. Stylizedgs: Con- trollable stylization for 3d gaussian splatting. arXiv preprint arXiv:2404.05220, 2024. 3

    2024 arXiv

  47. [55]

    Arf: Artistic radiance fields

    Kai Zhang, Nick Kolkin, Sai Bi, Fujun Luan, Zexiang Xu, Eli Shechtman, and Noah Snavely. Arf: Artistic radiance fields. In European Conference on Computer Vision, pages 717–733. Springer, 2022. 2, 3, 4, 12

    2022

  48. [56]

    splatfacto

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 4 11 ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian...

    2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.