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REVIEW 4 major objections 5 minor 1 cited by

DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read DragScene claims that a single drag on one rendered view can edit the whole 3D scene consistently.

desk verdict A genuinely new pipeline for drag-editing real 3D scenes, with the point-cloud latent propagation as the key idea; the evaluation, however, doesn't yet back the multi-view consistency claim. read the letter →

arxiv 2412.13552 v1 pith:GO5ZZ5F2 submitted 2024-12-18 cs.CV cs.GR

Chenghao Gu , Zhenzhe Li , Zhengqi Zhang , Yunpeng Bai , Shuzhao Xie , Zhi Wang This is my paper · ORCID
classification cs.CVcs.GR
keywords 3Dsceneeditingdrag-stylemulti-viewconsistencylatentdiffusionmodelspointcloudreconstructionGaussianSplattingsingle-viewinteractionview-consistent
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

DragScene claims that a user can edit a real-world 3D scene by drawing a mask and dragging a point on a single rendered view, with the edit propagated coherently to every other view. The paper proposes to do this by running a 2D latent-diffusion drag edit on one reference image, reconstructing a coarse point cloud that fuses the edited image with a few original views, and then using latent maps rendered from that point cloud to supervise the drag edit of all other views. If this works, it turns a precise 2D interaction into a general 3D editing tool that is not tied to one 3D representation and does not require text prompts or per-object retraining. The paper reports qualitative and quantitative evidence on real scenes from common 3D editing benchmarks, arguing that earlier deformation-based and prompt-based methods cannot achieve this level of localized control.

What carries the argument

The load-bearing object is the 3D latent map: a coarse point cloud reconstructed by DUSt3R from the edited reference view and a small set of original views, with every point carrying the latent code of the edited reference image and the user's mask. Rendered into a new viewpoint, this map supplies both the target latent for the edited region and the mask that protects unchanged regions. Its role is to translate a single 2D edit into view-consistent latent supervision; the paper's ablation shows that omitting either the edited-image supervision or the original-image supervision degrades the point cloud and breaks feature correspondence, which in turn produces mismatched edited views.

What would settle it

Render a synthetic 3D scene with known ground-truth geometry, apply a drag that changes the shape, such as lengthening an object, reconstruct the point cloud from the edited reference view alone, and compare depth in the masked region to ground truth; if the depth error in the edited region is large or grows with drag distance, the multi-view-consistency mechanism will fail exactly where it is needed.

Watch

Extended reading notes

Core claim

The central discovery is a mechanism for keeping drag edits multi-view consistent. Instead of running a 2D drag editor independently on each view, which produces inconsistent features, DragScene first edits one reference view and obtains its diffusion latent. It then reconstructs a coarse point cloud from the edited reference image plus sparse original views, using a regression loss that keeps unmasked regions aligned with the original scene and masked regions aligned with the edit. The reference latent is assigned to that point cloud, and latent maps plus masks are rendered into all other views, where they drive a latent optimization with a reconstruction term pulling the edited region toward the 3D latent map and a mask term keeping unchanged regions near their original latent. The edited multi-view images are finally used to reconstruct the target 3D scene, demonstrated with 3D Gaussian Splatting, and the paper positions DragScene as the first drag-style 3D editing method applicable to real-world scenes.

Load-bearing premise

The pipeline assumes that the point-cloud reconstruction of the edited reference view gets the edited region's geometry right, even though the edited image is something the reconstruction model was not trained on; if that geometry is wrong, the latent maps mislead other views and consistency breaks.

Editorial extensions

If this is right

  • If DragScene is correct, users can edit captured real scenes, including faces, furniture, and outdoor scenery, by dragging in one view without writing text prompts or training per object.
  • Because the method operates on rendered images and a point-cloud latent bridge, it can be retargeted to newer 3D representations beyond 3D Gaussian Splatting whenever multi-view images can be rendered and reconstructed.
  • The framework converts a capable 2D drag editor into a 3D editor, so future improvements in 2D drag editing would directly improve the quality and variety of 3D edits.
  • Multi-view consistency is enforced at the latent level before final reconstruction, which should reduce the artifacts and discontinuities seen when drag editing is applied naively to each view separately.
  • The approach supports localized and creative edits, such as widening a blade or lengthening a face, which the paper argues are poorly handled by deformation-based and prompt-driven 3D editing baselines.

Reading between the lines

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

  • A direct extension is to replace the chosen 2D drag editor with any newer one; the paper itself notes that editing quality is bounded by the 2D drag model, so the framework's ceiling will track progress in 2D drag editing.
  • The single-reference-view design implies that very large viewpoint changes or heavy occlusions could break the point-cloud bridge, a failure mode the paper acknowledges as unsatisfactory results for large view ranges.
  • A testable prediction is that multi-view consistency degrades as edit magnitude grows, because larger edits move the edited image further from the reconstruction model's training distribution; measuring masked-region reprojection error as a function of drag distance would expose this.
  • The latent-bridge mechanism does not inherently depend on scene staticity, so extending it to dynamic scenes or to text-guided drags, both listed as future work in the paper, is a plausible path.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. DragScene is a method for editing a reconstructed 3D scene by dragging points on a single reference view. Given a reference image rendered from the scene, a user-specified mask and handle/target point pairs, the authors first apply DragDiffusion to obtain a 2D edit and a DDIM-inverted latent for the edited view. They then reconstruct a coarse point cloud with DUSt3R from the edited reference and several original views, using the regression loss in Eq. (3) to keep unedited regions aligned with the original views while forcing edited regions to agree with the reference edit. The edited view's latent is attached to the point cloud, and rendered latent maps and masks are used to optimize DDIM-inverted latents of other views via Eqs. (4)-(7). Finally, the edited images are used to reconstruct a new 3D Gaussian Splatting scene. The paper claims this is the first drag-style 3D editing method applicable to real-world scenes and that it maintains multi-view consistency.

Significance. The problem addressed is real: existing text-driven 3D editing lacks localized geometric control, and drag-style interaction is a natural alternative. DragScene's design is elegant and representation-agnostic in principle; using DUSt3R pointmaps as 3D clues to transfer a latent edit across views is a sensible way to avoid per-scene optimization. The ablations in Figs. 7-9 and the comparisons in Fig. 6 show plausible qualitative results. If the multi-view consistency claim can be substantiated quantitatively, this would be a useful practical contribution to 3D scene editing. However, as presented, the evidence for the central consistency claim is mostly qualitative, with only a single-scene, target-deviation FID and NIQE curve, so the contribution is not yet established to the standard of a top venue. The paper also does not release code or data, which limits reproducibility.

major comments (4)
  1. [Section 4.3 and Fig. 6] The central claim that DragScene achieves multi-view consistency is supported only by qualitative examples. There is no user study and no quantitative cross-view consistency metric. The only quantitative evaluation, Fig. 9, reports FID between novel-view edited images and the edited reference image; this measures deviation from the target view, not consistency among views, and the text does not state the number of scenes or edits over which the scores are averaged. Please report quantitative consistency (e.g., re-projection error on the reconstructed point cloud, pairwise LPIPS/PSNR between matched views, or a user study) across multiple scenes and edits.
  2. [Section 3.2, Eq. (3), and Limitation] The coarse point cloud that carries the edit is reconstructed from an edited reference image that is out of distribution for DUSt3R. If the masked-region pointmap is wrong (e.g., for the 'make the sofa taller' edit), the rendered latent targets in Section 3.3 will pull other views toward inconsistent geometry. The only evidence that this does not happen is the single qualitative ablation in Fig. 7; the paper itself concedes failures for large view ranges but does not delimit them. Please provide a quantitative evaluation of the edited-region geometry, for example by comparing the reconstructed point cloud with a ground-truth edited mesh in a synthetic scene or by measuring re-projection consistency of the edited region across held-out views.
  3. [Section 3.2, Eq. (2)] The mask M is a 2D user annotation, but Eq. (2) transforms it with the same camera-pose mapping used for 3D pointmaps, h(M) being the homogeneous embedding of the mask. The depth or 3D surface used to lift the 2D mask into 3D is not specified. Since this mask is used in Eq. (3) and later rendered as R_vi(P_M) in Eqs. (4)-(6), the ambiguity directly affects the method's reproducibility. Please state explicitly how the 2D mask is associated with the reconstructed point cloud and how Eq. (2) is evaluated.
  4. [Section 4.2] The comparison with GaussianEditor and DragDiffusion-3D is entirely qualitative (Fig. 6). Given that the paper claims superiority in both editing quality and controllability, a quantitative comparison (e.g., user preference rates, or a protocol running the same set of edit intents across multiple scenes) is needed to rule out selection effects in the displayed examples.
minor comments (5)
  1. [Section 4.5] The sentence 'As shown in Fig. 7, the optimal results are achieved when eta is around 0.4' appears to reference the wrong figure; Fig. 7 is the point-cloud reconstruction ablation, while Fig. 9 contains the NIQE and FID plots.
  2. [Section 4.1] The implementation text says 'using the AdamW optimizer[11]' but reference [11] is the original Adam paper; please cite AdamW or correct the reference.
  3. [Section 3.2] The model name is written inconsistently as 'DUSt3R' in the main text and 'Dust3R' in Section 4.4; please use one spelling throughout.
  4. [Section 4.1] The description of LoRA fine-tuning does not specify which images are used for the 80 fine-tuning iterations; please clarify whether only the reference image or a set of views is used.
  5. [Section 3.2, Eq. (3)] The regression loss is described as an optimization over pointmaps, but the text does not state which variables are updated or how the optimization is initialized and run; please add a sentence describing the optimization procedure.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the multi-view propagation is an explicit optimization objective built on external components; only a non-load-bearing self-citation and a self-referential evaluation metric are noted.

full rationale

DragScene's derivation chain is not circular in the sense prohibited by the review criteria. The reference-view edit produced by DragDiffusion is the user-specified target, and Equations (3), (5), and (6) explicitly define an optimization objective that propagates this target to other views. The paper never presents multi-view consistency as an emergent prediction independent of this objective; rather, consistency is enforced by the loss design, which is the intended mechanism of the method. The 3D clues come from the external DUSt3R model, the 2D editing comes from the external DragDiffusion model, and the final representation uses the external 3D Gaussian Splatting framework. The only self-citation is Reference [45] (MesonGS, which shares two authors with this paper), used as an example of 3DGS adoption in applications; it is not load-bearing in the derivation. The FID metric in Figure 9 is computed against the edited reference view, but the text transparently labels it as measuring 'deviation from the editing target,' which is exactly the quantity the optimization is designed to minimize. This makes the metric a weak indicator of cross-view consistency, but it is not a disguised prediction or a circular derivation. Concerns about DUSt3R's reliability on out-of-distribution edited images are correctness and robustness risks, not circularity, and the Limitation section openly acknowledges failures for large view ranges. Overall, the paper's pipeline is self-contained and its claims are engineering claims about a propagation scheme, not derived results that reduce to their own inputs.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The method's novelty rests on several external pre-trained models and hand-set hyperparameters. The load-bearing assumption is that DUSt3R can reconstruct coherent geometry from an edited, potentially out-of-distribution image, and that the reference view's DDIM latent is a valid template for other views. No new physical entities are introduced.

free parameters (5)
  • lambda (Lmask weight) = not reported
    In Eq. 4, the balance between the reconstruction loss and the unmasked-region consistency loss is set by lambda, but its value is never given; it directly controls the multi-view fidelity/consistency tradeoff.
  • inversion strength eta = 0.4
    Chosen via NIQE/FID ablation in Sec. 4.5 on the evaluation scenes; the reported 'optimal' value is fitted on the same data used for the final results, not a prediction.
  • latent optimization learning rate sigma = not reported
    The step size sigma for the gradient descent in Eq. 7 is not specified, and the number of optimization iterations m is also omitted; these hyperparameters control how closely novel-view latents match the rendered 3D latent maps.
  • DDIM inversion steps te and tr = te=35, tr=20
    Chosen by hand in Sec. 4.1; these steps control the amount of noise in the reference latent and therefore the edit's fidelity versus editability.
  • number of edited views s = ~20 images
    Chosen by hand in Sec. 4.1; the density of views affects reconstruction quality and the coverage of 3D clues for novel-view propagation.
assumptions (6)
  • domain assumption DUSt3R produces reliable dense pointmaps for an edited reference image, even when the edit changes geometry.
    Invoked in Sec. 3.2, Eq. 3, where the point cloud is supervised to align the masked region with the edited reference view. If the edited image is out of distribution, the pointmaps may be distorted.
  • domain assumption The edited reference image is a valid anchor for multi-view geometry estimation.
    DUSt3R is fed the edited image I0 and original views; Eq. 1 and Eq. 2 assume the viewpoint transformations are consistent with the edited content.
  • domain assumption DDIM inversion latents are semantically consistent across views for the same scene region.
    In Sec. 3.3, the reference latent is assigned to the point cloud and rendered to other views; if latents are not comparable across views, the rendered maps will not guide optimization correctly.
  • domain assumption Camera poses for the original scene are known and accurate.
    Eqs. 1-2 use world-to-camera poses Pm and Pn to warp pointmaps and masks; inaccurate poses break the alignment loss.
  • domain assumption Stable Diffusion v1.5 / DragDiffusion can produce a coherent edit on the reference view.
    The whole pipeline inherits the limitations of the 2D drag editor; the paper itself notes failures such as mouth opening in the Limitation section.
  • domain assumption 3DGS reconstruction from edited multi-view images yields a valid edited 3D scene.
    Sec. 3.4 assumes that training a 3DGS on the edited views faithfully recovers the intended edit without artifacts or inconsistencies.

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Cite this review

Pith. "Pith review of DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions." pith.science (2026). https://pith.science/paper/GO5ZZ5F2

@misc{pith2026241213552,
  author       = {Pith},
  title        = {Pith review of: DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GO5ZZ5F2}},
  note         = {Machine review of arXiv:2412.13552}
}
read the original abstract

3D editing has shown remarkable capability in editing scenes based on various instructions. However, existing methods struggle with achieving intuitive, localized editing, such as selectively making flowers blossom. Drag-style editing has shown exceptional capability to edit images with direct manipulation instead of ambiguous text commands. Nevertheless, extending drag-based editing to 3D scenes presents substantial challenges due to multi-view inconsistency. To this end, we introduce DragScene, a framework that integrates drag-style editing with diverse 3D representations. First, latent optimization is performed on a reference view to generate 2D edits based on user instructions. Subsequently, coarse 3D clues are reconstructed from the reference view using a point-based representation to capture the geometric details of the edits. The latent representation of the edited view is then mapped to these 3D clues, guiding the latent optimization of other views. This process ensures that edits are propagated seamlessly across multiple views, maintaining multi-view consistency. Finally, the target 3D scene is reconstructed from the edited multi-view images. Extensive experiments demonstrate that DragScene facilitates precise and flexible drag-style editing of 3D scenes, supporting broad applicability across diverse 3D representations.

Figures

Figures reproduced from arXiv: 2412.13552 by the authors.

Figure 1
Figure 1. Results of DragScene. DragScene successfully enables drag-based editing for 3D scenes. By following user-provided editing instructions (masks and points), our model seamlessly performs drag-style editing on the original 3D scene. All the results presented above are based on 3D Gaussian Splatting (3DGS), demonstrating natural, creative, and view-consistent edits. Abstract 3D editing has shown remarkable capability in… view at source ↗
Figure 2
Figure 2. Our Motivation. Comparison of DragScene and Directly applying DragDiffusion to multi-view images. (a) illustrates that existing 3D editing methods fail to solve the specific editing task and DragScene performs well. (b) PCA visualization of Unet feature maps during the diffusion process. It demonstrates that directly applying 2D drag-style methods to multi-view images produces inconsistent features, whereas DragScen… view at source ↗
Figure 3
Figure 3. Overview of DragScene. Our approach consists of three steps: firstly, we apply a 2D drag-based diffusion model to edit the reference image and obtain the reference latent representation through DDIM inversion. Second, we perform consistent construction of the reference latent representation to obtain 3D latent maps. Finally, we apply the Inversion process to other views, further optimizing the images in latent space… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Consistent Reconstruction of Latent Representations. To facilitate consistent multi-view latent optimization, we apply DUSt3R to reconstruct the coarse 3D point cloud, with aligned masks assisting in the optimization process. The latent representa￾tion of the reference…
Figure 5
Figure 5. Figure 5: More results of DragScene. We present various views of both the original and edited scenes. All scenes are reconstructed using 3D Gaussian splatting. User Edit Original DragScene GaussianEditor DragDiffusion Ext on reference view “Make its blade wider” “Make his face l…
Figure 6
Figure 6. Figure 6: Comparisons between DragScene and other methods. We show the rendering view of scenes edited with different models. For fairness, we provide different forms of editing instructions with the same editing intent. 6 [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Ablation on reconstruction of 3D clues. The first three columns display the reconstructed point cloud of the reference view, and the fourth column shows the subsequent editing result. As shown, without the supervision of edited images, the predicted point cloud fails t…
Figure 8
Figure 8. Figure 8: Ablation Study on the the Inversion Strength η = topt/ttotal. Results of high quality and multi-view consistency are obtained when η is around 0.4. When the value of η is smaller, the noise in the latent representation decreases, leading to a more blurred output after …
Figure 9
Figure 9. Figure 9: Ablation study on the Inversion strength η. NIQE Score (↓) and FID Score (↓) are reported. When the inversion strength η reaches 0.4, both metrics achieve their optimal perfor￾mance. 5. Conclusion In our research, we introduce DragScene, a novel 3D edit￾ing method that…

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

    cs.CV 2026-07 conditional novelty 4.0 of 10

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Reference graph

Works this paper leans on

51 extracted references · 31 canonical work pages · cited by 1 Pith paper

  1. [1]

    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. 3, 5

    work page 2022

  2. [2]

    In- structpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. In- structpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392–18402, 2023. 2, 3

    work page 2023

  3. [3]

    pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

    David Charatan, Sizhe Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In CVPR, 2023. 5

    work page 2023

  4. [4]

    Mvsnerf: Fast general- izable radiance field reconstruction from multi-view stereo

    Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast general- izable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF international conference on computer vision, pages 14124–14133, 2021. 3

    2021

  5. [5]

    Mvdrag3d: Drag-based creative 3d editing via multi-view generation-reconstruction priors

    Honghua Chen, Yushi Lan, Yongwei Chen, Yifan Zhou, and Xingang Pan. Mvdrag3d: Drag-based creative 3d editing via multi-view generation-reconstruction priors. arXiv preprint arXiv:2410.16272, 2024. 3

    arXiv 2024

  6. [6]

    Proedit: Simple progres- sion is all you need for high-quality 3d scene editing

    Jun-Kun Chen and Yu-Xiong Wang. Proedit: Simple progres- sion is all you need for high-quality 3d scene editing. arXiv preprint arXiv:2411.05006, 2024. 3

    arXiv 2024

  7. [7]

    Neuraled- itor: Editing neural radiance fields via manipulating point clouds

    Jun-Kun Chen, Jipeng Lyu, and Yu-Xiong Wang. Neuraled- itor: Editing neural radiance fields via manipulating point clouds. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 12439–12448,

    work page

  8. [8]

    Consist- dreamer: 3d-consistent 2d diffusion for high-fidelity scene editing

    Jun-Kun Chen, Samuel Rota Bul`o, Norman M¨uller, Lorenzo Porzi, Peter Kontschieder, and Yu-Xiong Wang. Consist- dreamer: 3d-consistent 2d diffusion for high-fidelity scene editing. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 21071–21080,

    work page

Show all 51 references
  1. [9]

    Dge: Direct gaussian 3d editing by consistent multi-view editing

    Minghao Chen, Iro Laina, and Andrea Vedaldi. Dge: Direct gaussian 3d editing by consistent multi-view editing. arXiv preprint arXiv:2404.18929, 2024. 3

    2024 arXiv

  2. [10]

    Dragvideo: Interactive drag-style video editing

    Yufan Deng, Ruida Wang, Yuhao Zhang, Yu-Wing Tai, and Chi-Keung Tang. Dragvideo: Interactive drag-style video editing. In European Conference on Computer Vision, pages 183–199. Springer, 2025. 3

    2025

  3. [11]

    Adam: A method for stochastic opti- mization

    P Kingma Diederik. Adam: A method for stochastic opti- mization. (No Title), 2014. 5

    2014

  4. [12]

    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. 3

    2024

  5. [13]

    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. 3

    2022

  6. [14]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communi- cations of the ACM, 63(11):139–144, 2020. 3

    2020

  7. [15]

    Nerfren: Neural radiance fields with reflections

    Yuan-Chen Guo, Di Kang, Linchao Bao, Yu He, and Song- Hai Zhang. Nerfren: Neural radiance fields with reflections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18409–18418, 2022. 3

    2022

  8. [16]

    threestudio: A unified framework for 3d content generation

    Yuan-Chen Guo, Ying-Tian Liu, Ruizhi Shao, Christian Laforte, Vikram V oleti, Guan Luo, Chia-Hao Chen, Zi- Xin Zou, Chen Wang, Yan-Pei Cao, and Song-Hai Zhang. threestudio: A unified framework for 3d content generation. https://github.com/threestudio- project/ threestudio, 2023. 5

    2023

  9. [17]

    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. 3, 5

    2023

  10. [18]

    Gans trained by a two time-scale update rule converge to a local nash equilibrium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bern- hard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 7

    2017

  11. [19]

    Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021. 5

    2021 arXiv

  12. [20]

    2d gaussian splatting for geometrically accu- rate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accu- rate radiance fields. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 3

    2024

  13. [21]

    Sc-gs: Sparse-controlled gaus- sian splatting for editable dynamic scenes

    Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. Sc-gs: Sparse-controlled gaus- sian splatting for editable dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4220–4230, 2024. 2, 3

    2024

  14. [22]

    Nerf- shop: Interactive editing of neural radiance fields

    Cl´ement Jambon, Bernhard Kerbl, Georgios Kopanas, Stavros Diolatzis, Thomas Leimk¨uhler, and George Drettakis. Nerf- shop: Interactive editing of neural radiance fields. Proceed- ings of the ACM on Computer Graphics and Interactive Tech- niques, 6(1), 2023. 2, 3

    2023

  15. [23]

    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, 5

    2023

  16. [24]

    Neural sparse voxel fields

    Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651–15663,

  17. [25]

    Editing conditional radi- ance fields

    Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. Editing conditional radi- ance fields. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5773–5783, 2021. 3

    2021

  18. [26]

    Manigaussian: Dynamic gaussian splatting for multi-task robotic manipulation

    Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu, and Yansong Tang. Manigaussian: Dynamic gaussian splatting for multi-task robotic manipulation. In European Conference on Computer Vision. Springer, 2024. 5 9

    2024

  19. [27]

    Regiondrag: Fast region- based image editing with diffusion models

    Jingyi Lu, Xinghui Li, and Kai Han. Regiondrag: Fast region- based image editing with diffusion models. arXiv preprint arXiv:2407.18247, 2024. 3

    2024 arXiv

  20. [28]

    3dgsr: Implicit surface reconstruction with 3d gaussian splat- ting

    Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Xiuzhe Wu, Ziyi Yang, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi. 3dgsr: Implicit surface reconstruction with 3d gaussian splat- ting. arXiv preprint arXiv:2404.00409, 2024. 3

    2024 arXiv

  21. [29]

    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. 3

    2021

  22. [30]

    completely blind

    Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Mak- ing a “completely blind” image quality analyzer. IEEE Signal processing letters, 20(3):209–212, 2012. 7

    2012

  23. [31]

    Dragondiffusion: Enabling drag-style manipula- tion on diffusion models

    Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Dragondiffusion: Enabling drag-style manipula- tion on diffusion models. arXiv preprint arXiv:2307.02421,

    arXiv

  24. [32]

    Instant neural graphics primitives with a multires- olution hash encoding

    Thomas M¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a multires- olution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 3

    2022

  25. [33]

    The blessing of random- ness: Sde beats ode in general diffusion-based image editing

    Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, and Chongxuan Li. The blessing of random- ness: Sde beats ode in general diffusion-based image editing. arXiv preprint arXiv:2311.01410, 2023. 3

    2023 arXiv

  26. [34]

    Drag your gan: Interactive point-based manipulation on the generative image manifold

    Xingang Pan, Ayush Tewari, Thomas Leimk ¨uhler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt. Drag your gan: Interactive point-based manipulation on the generative image manifold. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–11, 2023. 2, 3

    2023

  27. [35]

    Cagenerf: Cage-based neural radiance field for gen- eralized 3d deformation and animation

    Yicong Peng, Yichao Yan, Shengqi Liu, Yuhao Cheng, Shanyan Guan, Bowen Pan, Guangtao Zhai, and Xiaokang Yang. Cagenerf: Cage-based neural radiance field for gen- eralized 3d deformation and animation. Advances in Neural Information Processing Systems, 35:31402–31415, 2022. 3

    2022

  28. [36]

    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. 2, 3, 5

    2022

  29. [37]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, pa...

    2015

  30. [38]

    Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

    Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Han- shu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti...

    2024

  31. [39]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502,

    2010 arXiv

  32. [40]

    Drag-a-video: Non-rigid video editing with point- based interaction

    Yao Teng, Enze Xie, Yue Wu, Haoyu Han, Zhenguo Li, and Xihui Liu. Drag-a-video: Non-rigid video editing with point- based interaction. arXiv preprint arXiv:2312.02936, 2023. 3

    2023 arXiv

  33. [41]

    Dn-splatter: Depth and normal priors for gaussian splatting and meshing

    Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, and Juho Kannala. Dn-splatter: Depth and normal priors for gaussian splatting and meshing. arXiv preprint arXiv:2403.17822, 2024. 3

    2024 arXiv

  34. [42]

    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, 3, 5, 7

    2024

  35. [43]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 20697–20709, 2024. 4

    2024

  36. [44]

    View-consistent 3d editing with gaus- sian splatting

    Yuxuan Wang, Xuanyu Yi, Zike Wu, Na Zhao, Long Chen, and Hanwang Zhang. View-consistent 3d editing with gaus- sian splatting. In European Conference on Computer Vision, pages 404–420. Springer, 2025. 3

    2025

  37. [45]

    Mesongs: Post- training compression of 3d gaussians via efficient attribute transformation

    Shuzhao Xie, Weixiang Zhang, Chen Tang, Yunpeng Bai, Rongwei Lu, Shijia Ge, and Zhi Wang. Mesongs: Post- training compression of 3d gaussians via efficient attribute transformation. In European Conference on Computer Vision. Springer, 2024. 5

    2024

  38. [46]

    Deforming radiance fields with cages

    Tianhan Xu and Tatsuya Harada. Deforming radiance fields with cages. In European Conference on Computer Vision , pages 159–175. Springer, 2022. 2, 3

    2022

  39. [47]

    Neumesh: Learn- ing disentangled neural mesh-based implicit field for geome- try and texture editing

    Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learn- ing disentangled neural mesh-based implicit field for geome- try and texture editing. In European Conference on Computer Vision, pages 597–614. Springer, 2022. 3

    2022

  40. [48]

    Plenoctrees for real-time rendering of neural radiance fields

    Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoctrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 5752–5761,

  41. [49]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis. arXiv preprint arXiv:2409.02048, 2024. 8

    2024 arXiv

  42. [50]

    Nerf-editing: geometry editing of neural radiance fields

    Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, and Lin Gao. Nerf-editing: geometry editing of neural radiance fields. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 18353–18364, 2022. 2, 3

    2022

  43. [51]

    Drivinggaussian: Compos- ite gaussian splatting for surrounding dynamic autonomous driving scenes

    Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, De- qing Sun, and Ming-Hsuan Yang. Drivinggaussian: Compos- ite gaussian splatting for surrounding dynamic autonomous driving scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, page...

    2024

Pith tools

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