Pro3D-Editor: A Progressive-Views Perspective for Consistent and Precise 3D Editing
Authors: Yang Zheng
Mengqi Huang
Nan Chen
Zhendong Mao
Venue: NeurIPS 2025 poster ()
Keywords: 3D Edit
3D Gaussian Splatting
multi-view diffusion model
Primary area: applications
TL;DR: We propose a novel progressive 3D editing framework to achieve consistent and precise 3D editing

Abstract: Text-guided 3D editing aims to precisely edit semantically relevant local 3D regions, which has significant potential for various practical applications ranging from 3D games to film production. Existing methods typically follow a view-indiscriminate paradigm: editing 2D views indiscriminately and projecting them back into 3D space. However, they overlook the different cross-view interdependencies, resulting in inconsistent multi-view editing. In this study, we argue that ideal consistent 3D editing can be achieved through a progressive-views paradigm, which propagates editing semantics from the editing-salient view to other editing-sparse views. Specifically, we propose Pro3D-Editor, a novel framework, which mainly includes Primary-view Sampler, Key-view Render, and Full-view Refiner. Primary-view Sampler dynamically samples and edits the most editing-salient view as the primary view. Key-view Render accurately propagates editing semantics from the primary view to other key views through its Mixture-of-View-Experts Low-Rank Adaption (MoVE-LoRA). Full-view Refiner edits and refines the 3D object based on the edited multi-views. Extensive experiments demonstrate that our method outperforms existing methods in editing accuracy and spatial consistency.

Pro3D-Editor: A Progressive- Views Perspective for Consistent and Precise 3D Editing
Zhendong Mao Uniivessity of Science and Technology of China Institute of Artificial intelligence, Hefei Comprehensive National Science Center 12y849900389 huanggm, chen_ Basee__nnn@@malll ustc. edu. en, zdmaoQustc edu. en
Yang Zheng' Menggi Huang' Nan Chen'
"A recar with chicken"
chicken cat'
chicken pig
chicken pig and hat..."
"A girl with rabbit ears"
pair of goggles"
red dress"
blue shoes"
"A head"
... pepper"
"Mechine holds orco"
pizza"
knight
mask
Figure 1: We propose a new 3D editing pipeline, Pro3D-Editor, which achieves consistent and precise local editing of 3D objects: our method supports iterative editing of 3D objects (top), handles diverse local edits the same 3D object effectively (mid), and demonstrates strong editing capabilities across various 3D objects (bottom).
Abstract
Text-guided 3D editing aims to precisely edit semantically relevant local 3D re- gions, which has significant potential for various practical applications ranging from 3D games film production. Existing methods typically follow a view- indiscriminate paradigm: editing 2D views indiscriminately and projecting them back into 3D space. However, they overlook the different tross-view interdepen- dencies, resulting in inconsistent multi-view editing. In this study, we argue that ideal consistent 3D editing can achieved through a progressive- which propagates editing semantics from the editing salient view to other editing- sparse views. Specifically, we propose Pro3D- Editor, novel framework, which
paradigm,
Menggi Huang is the corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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mainly includes Primary-view Sampler, Key-view Render, and Full-view Refiner. Primary-view Sampler dynamically samples and edits the most editing-salient view as the primary view. Key-view Render accurately propagates editing semantics from the primary view to other key views through its Mixture-of- View-Experts Low-Rank Adaption (MoVE-LoRA), Full-view Refiner edits and refines the 3D object based on the edited multi-views. Extensive experiments demonstrate that our method outperforms existing methods in editing accuracy and spatial consistency. Project Page: htts://hhoouli4300ggioooooooooD gittbbio
Introduction
4 5 6 aims to precisely edit specific local features of a given 3D object based on the text guidance while preserving all other text-irrelevant features. Recently, this task has attracted significant attention as it facilitates diverse and personalized 3D asset synthesis, bringing various practical applications ranging from 3D games to film production. Unlike the well- studied 2D editing 17 8 9 [0]. rext-guided 3D editing presents greater challenges as it demands a comprehensive understanding of real-world 3D structures to achieve both inter-view consistency (i.e., ensuring coherent appearance across views) and intra-view discrimination (i.e., enabling distinctive and view-specific edits for each view).
Text-guider 3D editing 0234
Existing methods focus on lifting editing semantics from the 2D image plane to the 3D spatial space, which can be categorized into two streams, i.e., the iterative single-view stream and the parallel nilti-views stream. The former stream M 2 iteratively refines the 3D representation by leveraging gradients from individual view images until the 3D object is well-aligned with the textual guidance, as shown in Fig. 2 (a). For example, Vox-E [1] uses a pre-trained text-to-image diffusion model to obtain each view's gradients and then repeatedly update the 3D object. The latter stream 3 5 simultaneously edits multiple rendered images from fixed viewpoints and subsequently propagates these modifications onto the 3D object, as illustrated in Fig. ( For example, PrEditor3D (5] employs commpt-to-rroppe image editing to modify rendered multi-view images from fixed viewpoints, and then update the 3D objects. In summary, the commonality of both streams is that they are
i.e., each view of the 3D object is edited indiscriminately.
However, the existing view-indiscriminate paradigm overlooks the different cross-view interde- pendencies induced by different editing instructions and therefore leads to view-conflicts, resulting in inconsistent 3D editing. Naturally, each view of the 3D object shows different editing salience depending on the editing instruction. For instance, "adding glasses" to 3D character primarily affects its frontal view with minimal impact on its rear one, while "adding a ponytail" conversely. Therefore, the cross-view interdependence manifests as the editing interdependence across views, where an "editing-salient" view is more effective in guiding an "editing-spars" one, while the reverse can only provide insufficient guidance and therefore lead to view-confficts. As shown in Fig. 2 d), the existing iterative single-view stream indiscriminately samples a random view to edit at each step, disregarding its editing salience, result in fiew-conficts where both the front and back views erroneously display a cat face (highlighted by the red bounding box). Meanwhile, as shown in Fig. Eee. the existing parallel multi-view stream indiscriminately samples several fixed views and edits each indiscriminately, ignoring the varying semantic salience of different views with respect to the editing instruction, thereby leading to conflicts among these edited views, such pizza appearing in the frontal view but disappearing its rear one (highlighted by the red bounding box).
To address these challenges, we propose novel progressive-views. paradigm, which progressively samples and edits views from editing salient to sparse, enabling a consistent and smooth editing process for arbitrary 3D objects and editing instructions. Compared with the iterative single-view stream, our paradigm edits views in descending order of salience, avoiding the conflicts caused by random view sampling (highlighted by the blue bounding box in Fig. d)). Compared with the parallel multi-view stream, our paradigm first edits the salient views and then uses them to guide further sparse view editing, avoiding the conflicts caused by indiscriminately editing multiple views in parallel (highlighted by the blue bounding box in Fig. ()))
Technically, we propose novel progressive 3D editing framework termed Pro3D-Editor, which constructs a hierarchical "primary-view -> key-views - full-views" editing pipeline based on the dynamic editing salience across different views. Specifically, the Pro3D-Edito consists of three

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3b Object
Object
3b Object
Random Sample
Fixed Sample
Samamic
SDS Update Cont Confllitta
Primary
Single View
View A
View 8
View N
Confilets x
Edited Primory
Consistent
Single
Edited View A
Edited View
Edited View
View Gradient
Edited Key View A
Edited Key View B
Edited Key View N
Edited 3b Object
Edited 3D Object
Edited 3D Object
Consistent
(4), iterative single-view stream
(b), parallel multi-views stream
(c). our progressive-views paradigm
Ours paradigm
paradigm (a)
Ours paradigm
paradigm (b)
Edit
30
(d).
text: recer with chicken cat
text: mechanical claw holds OREO pizza
Figure 2: We propose a novel editing paradigm (top) for text-guided 3D editing. Compared with existing paradigms, it achieves spatial consistency in edited regions (d) and mitigates feature conflicts across views (e). Moreover, our paradigm enables more precise local 3D editing.
successive modules: (1) Primary-view Sampler module dynamically samples and edits the most editing salient view as the primary view by calculating the salience score between each view and the editing signal, which is further linearly extrapolated with its corresponding negative view amplify accuracy. (2) Key-view Render module takes the edited primary view as the anchor and propagates its editing semantics to other key views. This is achieved through a novel Mixture-of- View-Experts Low-Rank Adaption MoVE-LLRA) which learns feature correspondences from the primary view to the remaining key views while blocking reverse learning to avoid conflicts. (3) Full-view Refiner module repairs numerous newly rendered views to refine the edited 3D result, which is achieved by fusing the editing information from the edited key multi-views.
Our main contributions are summarized as follows: (1) Concepts. We introduce a progressive-view puradigm for consistent and precise 3D editing by propagating the editing semantics from editing- salient views onto cditing-sparse views. (2) Technology. Based on the proposed paradigm, we design pipeline called Pro3D-Editor. In this pipeline, the Primary-view Sampler dynamically samples the most editing-salient view by calculating salience scores. The Key-view Render captures feature correspondences from the editing-salies view the editing-sparss views while blocking reverse learning to avoid conflicts. The Full-view Refiner repairs numerous newly rendered views to provide additional 3D structural information, refining the edited 3D regions. (3) Experiments. Extensive experimental results demonstrate that Pro3D-Editor surpasses current methods, achieving a 47.4% improvement in LPIPS (editing quality) and 9.7% improvement in DINO-I (editing accuracy).
2
Related Works
Multi- View Generation Models generate multi-view images guided by single 2D input. Trained
on large-scale 3D datasets [12 [3], multi-view diffusion models [14 15| 16 17 18. 19
20
21
22 first encodes external camera parameters to generate multi-view images from specified perspectives. MVDream 23 introduces multi-view attention mechanism to extend self-attention mechanism to 3D, improving spatial consistency across multi-views. Building on these, we propose fine-tuning models to better align features between the primary and other views, enabling precise and consistent regional multi- view editing.
effectively capture the spatial relationships across multi-views. Zero-1-to-3
3

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Original
Primory View
Rendered NultiViews
Original 3D65
Edited 3065
Primary
Somple
Multi-Vo View Unet with
forward
MoVE-LoRA
G
MILMM Description: "Knight
holds plain shield
tinettund Multi-View Unet
TeQ-STOSE
Fill-view Refiner
User Edit: "Knight holds shield
(o). Primary view Sampler
(b). Key-view Render
(c). Fill-view Refiner
Image
Multi- View Latents
Reference Viewpoints Viewpoints
perspective encoding
Spottial Self Aelf-AAttation
HoiVE-LORA
Generated
render
Repaining Module
Elenent Add
MultiViews
Multt View Artortion
primary
viewh,
edet 30
Parallel Attention
Forelid Attennion
refine 3b
Multi- -View biffusion Unet
(d). Details of Multi-View Unet
(c). Detials MoVE-LoRA
(f). Details of Full-view Refiner
Figure 3: Method overview. Given a 3D object represented by 3DGS, Pro3D-Fditor achieves precise 3D editing, which includes three main steps: (a) Primary-view Sampler selects and edits the most editing-salient view as the primary view. (Sec. [.T. editing information from the primary view to local regions of the remaining key views. (Sec. 3.2): (c)
: (b) Key-view Render accurately propagates the
Full-view Refiner edits and refines the 3D object based on the edited multi-views. (Sec. 3.3
3D Reconstruction From Multi Views aims to generate 3D objects from given images, which can be naturally extended to the 3D editing task. Mainstream 3D representations include NeRF, triplane, and 3D Gaussian Splatting (3DGS). NeRF [2 25 encodes 3D scenes implicitly with MLPs trained on dense views. Triplane models 26
31 represent features on orthogonal planes. 3DGS 33 explicitly models 3D objects as collections of Gaussians, iteratively refined with multi-view supervision. In 3D editing, it crucial to modify only the edited regions. Implicit methods like NeRF and triplane struggle with this, whereas the iterative nature of 3DGS makes it especially suitable. Therefore, we use 3DGS our editing representation.
27
28
29
Method
The pipeline of Pro3D-Editon is shown in Fig. including three main modules: Primary-view Sampler, Key-view Render, and Full-view Refiner. Primary-view Sampler is designed to sample and edit the primary view for subsequent multi-view editing (Sec. [.I). Key-view Render is designed to accurately propagate the editing semantics from the primary view to other key views, achieving precise multi-view editing (Sec. [..2p. Full-view Refiner repairs numerous newly rendered views by fusing the editing information from the key views, which helps address the fragmentation issue in sparse-views guided 3DGS editing and achieve high-quality 3D editing(Sec.. 3.3
3.1 Primary-view Sampler
Primary-view Sampler selects the most editing- salient view (i.e., the one with the richest editing information) as the primary view and edits it. When editing multi-views, the fine-tuned multi-view diffusion model propagates the editing information from the primary view the remaining key views. Therefore, the choice of the primary view significantly affects the quality the final editing results.
As shown in Fig. (a), the 3D object is first rendered into continuous 360 surrounding video, denoted I E IR #x where represents the number of rendered images, W and represent the width and height of rendered images. The frames are then fed into the Multimodal Large Language Model 34 to obtain descriptive text y/s for the original 3D object. And the user-provided editing text is denoted ye- Our Primary-view Sampler evaluates each rendered image by considering the relationships among ys. Ye, and Tes assigning score to each image. The rendered image with the highest score is selected the primary view for the entire 3D editing pipeline.

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Sampler Seore
a tail on the back.
Selected View
100
200
300
User Edit: Girl
Sampler Store
Selected View
weors red dress.
0435
0.99
100" 200 300*
Figure 4: Score distribution of Primary-view Sampler. It automatically selects the most editing- salient view as the primary view based the given 3DGS object and user-provided editing prompt. When editing tail, the scores exhibit a reasonable bimodal distribution, peaking at the side views. When editing the dress, the scores show a reasonable unimodal distribution, peaking the front view.
Primary-view Sampler focuses on two aspects. First, the selected view should align well with the text, ensuring high editing information density. Propagating edits from editing-salient views to oditing-sparse views helps reduce feature conflicts across views. Second, the relative views at 135 and 225 azimuth angles with respect to the primary view should contain minimal information, serving as a penalty term. This is because the line-tuning data for the multi-view generation model includes six fixedpeerpecttive rendered images, with the azimuth angle of the first view the remaining five views at 45%, 90", 135", 180", and 225", respectively. The lack of views at 135" and 225 means less information on the back side during 3D editing. If these missing views contain significant editing information, it can greatly reduce the quality of the final 3D edit.
0", and
Given the above considerations, the scoring formula of Primary-view Sampler can be expressed as:
score' < P(Ie, P(Ies Ne)* (P(Ie. Us. yel + P(Ie, Uo. Ve)"),
(1)
where P(I.. y2 softmax(CLIP((E ya) softmax transforms these CLIP similarities into probabil- ities for selection and represents the i-th view. P(Ie; Ne) softmax(CLIP y.))'. P(le. Mes ye) P(les ye ) P([c: ). p and represent the views at relative angles of 135e and 225e with respect to the i-th view. is a hyperparameter that controls the weight allocation.
Primary-vien Sampler selects the highest-scoringg rendered image as the primary view, denoted as C R 1xx Subsequently. 2D editing model edits this primary view based on the provided editing text, generating the edited image C.. which serves as the edited primary view for subsequent multi-view editing. As shown in Fig. 4 the Primary-view Sampler is capable of selecting the most editing-salient view and exhibits reasonably low scores on editing-sparss views.
3.2 Key-view Render
Under the guidance of the edited primary view ce obtained from Sec. Key-view Render accurately propagates editing information from the primary view to local regions of the remaining key views. To ensure accurate editing region control and feature consistency in edited regions, we introduce improvements both fine-tuning and inference stages of the multi-view diffusion model. In the fine-tuning stage, we design a Mixture-of Tiee-Experts Low-Rank Adaption (MoVE-LoRA) to capture feature correspondences from the primary view edits the semantically consistent local regions of the remaining key views based on the editing information from the primary view. the inference stage, we adopt a two-stage inference strategy to further enhance feature consistency within edited regions.
remaining key views, which accurately
5

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3.2.1 Mixture-of- Mixture-of-View-EXpeee View-Experts Low-Rank Adaption
Our Mixture-of- View-Experts Low-Rank Adaption (MoVE-LORA) is introduced as an additional trainable component to fine-tune the backbone. It is designed to capture feature correspondences from the primary view to the remaining views, laying the foundation for accurate multi-view editing.
As shown in Fig. (b), multi-view generation backbone [35 contains parallel attention module with three components: image cross-attention, multi-view attention, and spatial self-attention. Among these attention components, only the multi-view attention focuses on the feature correspondence between multi-view images. To capture accurate corresponding features from the primary view to the remaining views, we utilize the LoRA [36] structure exclusively within the multi-viww attention. However, since each view exhibits distinct feature correspondences with the primary view, sharing the same LoRA weights across multi-view images can lead feature entanglement, thereby hindering accurate feature alignment across views. To address this, we design MoVE-LoRA to decouple the feature correspondences among multi-views.
The detail of MoVE-LoRA is shown in Fig. (d). A shared matrix A E R is designed to capture the features of the primary view, where d denotes the number of channels in the image latent and denotes the low-rank dimension. Different matrices B; E Rx represent different expert models, which are used to capture the distinct feature correspondences between each view and the primary view, decoupling the features among multi-views. The forward process can be expressed as:
y = Wow + Σ B,Ax;
(2)
where Wo denotes the Linear layers in the attention module. M denotes the number of experts, which is set to be equal the number of multi-views, denotes the multi-view image latents.
Specifically, the matrix A is updated via backpropagation solely from the primary view, without any gradient contributions from the remaining key views. This design encourages the matrix A to learn the intrinsic features of the primary view and facilitates each matrix B; to capture the feature mappings from the primary view to the remaining key views.
In the multi-view diffusion model fine-tuning, the selected view G₁ from Primary-view Sampler and user-provided editing prompt Ve serve as conditional inputs to the denoiser (-). Then, with the viewpoint of the primary view set as the 0 azimuth, we render the remaining five key views. These six images finally used as fine-tuning data to train MoVE-LoRA, enabling it to capture feature correspondences from the primary view to the remaining key views. Our Mo MoRE- structure is trained by mean-squared loss:
E0(zt, E= -l ye, ())
where denotes unscaled noise and t denotes denoising timestep.
3.2.2 Two-Stage Inference
While the fine-tuned multi nult diffusion model with MoVE-LORA captures disentangled feature correspondences, it also learns redundant features in edited regions, compromising feature consistency across views. To mitigate this issue, we propose a two-stage inference approach, retaining the inherent spatial understanding capability of the backbone model when generating edited features.
The edited primary image 6e guides the fine-tuned multi-view diffusion model generate multi-view images. The editing regions in these generated views precisely correspond to those in the primary view. However, the redundant features may interfere with the generated views, resulting spatially unreasonable edits. To address this, we first obtain multi-view editing masks Me E 6 by comparing generated results with the original multi-view images. These masks represent the local editing regions that the multi-view diffusion model deems semantically relevant. Then, we incorporate these masks into the inference process to perform a second round generation, better preserving the backbone's 's inherent spatial understanding capability.
In the second round of generation, the multi-view editing masks are applied within the multi-view attention layer to conduct a fusion operation. This specific procedure is formulated as:
((1 A) MA(z) + x x MAMOVELORA(=)) @ Me + MAMIVELORA(2)) @ (1 Me). (4)

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Table Quantitative comparison with existing methods. Pro3D-Ediie achieves the best perfor- mance in terms of both editing quality and precise manipulation of targeted 3D object regions.
editing quality
editing accuracy
Methods
FID 4
PSNR f
LPIPS +
FVD 4
CLIP-T
DINO-I
T
37
139.53
12.39
0.323
1056.4
0.279
0.701
MVEdit 3
176.64
13.99
0.362
1748.7
0.298
0.741
3D-Adapter
4
160.49
13.11
0.356
1236.7
0.292
0.726
LGM [3
85.12
16.85
0.192
685.6
0.299
0.846
Ours (naive)
107.94
16.44
0.196
774.8
0.285
0.823
Ours (Pro3D-Editor)
63.99
22.01
0.101
449.9
0.304
0.928
Improvement
A 24.8%
A 30.6%
A 47.4%
A 34.4%
A 1.7%
A 9.7%
Rccar with pig wearing hat
Girl wearing sherts red dress
Head with red pepper mustache
Original
Ours
LGM
Tailor3D
3DAdapter
0
B
MVEdit
Figure 5: Qualitative comparison with existing methods. LGM and Tailor3D fail to preserve the original features, such the shoes of little girl doll. MVEdit inconsistently edits new face the back of the head. In comparison, Pro3D-Edive achieves accurate and spatially consistent 3D editing.
Here, 2 E 6x represents the multi-view latents, with h and wo as height and width. MA denotes the multi-view attention layers in the backbone, while MA LoRA denotes the fine-tuned versions of these layers. M, denotes the binary mask of the edited regions.
In summary, establish accurate feature correspondences across multi-views through MoVE-LoRA and further enhance the feature consistency via a two-stage inference strategy. These edited key multi-view images serve supervisory views for the subsequent Full-view view Refiner.
3.3 Full-view Refiner
Under the guidance of the edited key images I. obtained from Sec. 3.2.2 our Full-view Refiner designed to perform iterative editing of 3DGS objects and refine the edited 3D regions.
7

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Similar to the past studies [39 40] in the field of text-driven 3D scene editing, we directly project the edited key multi-views back into 3D space to achieve precise modifications in localized 3D regions. However, the discreteness and unstructured nature of 3DGS make it challenging to achieve high-quality editing solely based on sparse views, often leading to fragmented outcomes in edited 3D) regions. Inspired by the field of sparse-views 3DGS reconstruction field [4], Full-view Refiner fuses the edited key multi-views to repair the newly rendered views, thereby obtaining more structured 3D information to help overcome the fragmentation issues in edited 3DGS.
First, we iteratively optimize the existing 3D object O under the guidance of Ie obtain the preliminarily edited 3D object Oj. While O1 preserves the original 3D features, it still exhibits fragmentation in the edited regions. Then, to repair fragmented editing regions, we utilize the multi-view images I, rendered under On from the viewpoints corresponding to Le. and -uga-qualtty images L to fine-tune a repair module. The backbone of the repair module is 2D diffusion model. During the training process, we add noise e to Ie and get noised latents 2f. The rendered degraded images I, serve as the guidance condition to the denoising process. The loss function defined as:
Crepair
E ||e EØ(2f, ,t, Vr, 1,)) 2,e,(
where y/2 denotes an object-specific prompt, t denotes denoising timestep.
The fine-tuned repairing module can learn how to generate structurally coherent views from degraded images. Finally, we render a large volume of images from novel viewpoints under the 3D object O1, which are then repaired by the fine-tuned model provide additional 3D structural information for the edited regions. These numerous repaired novel views serve as the training data for iterative updates of O1, ultimately yielding a structured, high-quality 3D editing result O2.
4 Experiments
4.1 Experimental Setups
Implementation Details. The weighting coefficient in the Primary-view Sampler is set to 0.5. For the MoVE-LoRA, the rank of the shared matrix A is set to 32. The number of expert matrices B, is set to 6. The weighting coefficient À in the two-stage inference stage is set to 0.5. We employ a leave-one-out strategy, updating the 3DGS object using the edited multi-views by iteratively leaving out one view and training on the remaining views for 10k steps. Then we employ Control Tile as the base of the Full-view Refiner, injecting LoRA into all attention layers with rank = 64, and fine-tune it for 1800 steps with learning rate of 1e-3. Finally, continue updating the 3DGS object for an additional 10k steps. The entire editing process is trained on an A100 GPU for about 1.5 hours.
Evaluation. Our evaluation 3D dataset contains 6 objects and 15 editing prompts. To construct the evaluation image dataset, we render 72 views for each edited object by sampling azimuth angles every 5%. We evaluate our method and the baselines from two aspects: (1) Editing quality: FID [4]. PSNR, LPIPS. FVD 43), and the texture details score from GPTEva13D (4 (2) Editing accuracy: CLIP-T 45). DINO-T 46). the 3D plausibility score and the text-asset alignment score from GPTEva13D. Detailed explanations of the evaluation metries are provided in Appendix A.
4.2 Main Results
In this section, we compare Pro3D-Editor with state-of-the-art methods (MVEdit [], 3DAdapter 44. Tailor3D (7). and IGM (38) using qualitative and quantitative analyses. For a fair comparison, the multi-views for T and LGM are from our method. More details are provided in Appendix A.
Quantitative results. As shown in Tab. [ our method outperforms existing basclines in both 3D editing quality and editing accuracy. Compared with existing methods, Pro3D- Editor achieves 47.4% improvement in editing quality (LPIPS) and a 9.77% improvement in editing accuracy (DINO-I). Additionally, as shown in Fig. [6] our editing results are highly likely preferred by GPTEva13D in terms of aexte alignment, 3D plausibility, and texture details.
Qualitative results. Fig. 5 shows a qualitative comparison between Pro3D-EEdior and existing methods. Our method is capable of producing editing results with finer details. In contrast, baselines like T exhibit inferior editing quality, such the girl doll's dress. Our method also ensures
8

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3D dDAaaeeer
Ours
Text-Assel Aligmment
50
Tailor3
Ours
Text-Asset Alignment
95
3D
Pl Plausibility
86
SD Plansibility
98
Textual Details
Textual Details
88
40
40
100
Ours
Text-Asset Aligament
79
LGN
Oury
Asse- Alignment
3D Plausibility
3D Plausibility
Textual Details
Textual Details
20
40
60
80
100
20
40
80
80
100
The blue segments indicate the selection rate of Pro3DDFFitor, while the orange segments represent that of the baseline. A higher selection rate indicates better editing performance of the corresponding method.
Figure 6: Quantitative comparison using GPTEvaI3D
Table 2: Ablation Studies of essential modules. Compared with the naive method (ID-0), introducing Primary-view Sampler (ID-1) 1) enhances the aligmment between the edited 3D objects and the editing prompts. Introducing MoVE-LoRA (ID-2) ensures spatial consistency in the edited regions. Full-view
Refiner (ID-3) significantly improves the editing quality, with a 10.6% gain in the LPIPS metric.
ID
Settings
LPIPS 4
CLIP-T1
DINO-I
naive method
0.196
0.285
0.823
I
0 + Primary-view Sampler
0.118
0.300
0.875
1 + MoVE-LoRA
0.113
0.302
0.879
3
2 + Full-view Refiner
0.101
0.304
0.928
spatial consistency in the edited regions, while baselines like MVEdit often generate spatially inconsistent objects, such as facial features on the back of the head. Furthermore, our method accurately edits semantically relevant local regions, which the baselines fail to achieve.
4.3 Ablations
To evaluate the effectiveness of our proposed paradigm and the essential components in improving consistency and quality, we conduct extensive ablation experiments. It is important to note that the quantitative metrics used in these experiments certain limitations. Specifically, some spatial inconsistencies and structural discontinuities that are clearly noticeable in 3D space may lead to only minor numerical differences when projected into 2D space, making them difficult to detect through 2D metrics alone. Therefore, to provide a more comprehensive assessment, provide additional visualizations in Appendix B.
based on 2D image evaluations, which have
Effectiveness of progressive-viess paradigm. As shown Fig. 5 we first explain our naive method, which uses fine-tuning for 3D editing. It replaces Primary- Sampler with random sampling of the primary view, substitutes MoVE-LoRA with the simplest LoRA structure where all views share the same LoRA, and removes Full-view Refiner. By comparing this defined naive method, we demonstrate that is our proposed progressive-viewt paradigm that ensures high-quality and accurate text-guided 3D editing, rather than the fine-tuning approach itself. Compared with the naive method, ProtD-Editor achieves a 8855% improvement the LPIPS metric (editing quality) and an 12.6% improvement in the DINO-I metric (editing accuracy).
Pro3D-Editon with
Effectiveness of Primary-view Sampler. As shown in Tab. 2 compared with the method with a random sampling of the primary view (ID-0), introducing the Primary-view Sampler achieves 5.3% improvement in the CLIP-T metric, which demonstrates that Primary-view Sampler effectively enhances the alignment between the edited 3D objects and the editing prompts.
Effectiveness of MoVE- E-LLRA. As shown in Tab. 2 compared with the method without VO (ID-0), introducing MoVE-LORA (ID-1) enables precise control over the edited regions and achieves an improvement on the DINO-I metric. As shown Fig. 7 we provide visual examples to better demonstrate the effectiveness of VoRE-LORA. The front view serves as the primary view in this case. Without any fine-tuning, the back view lacks precise control over the edited regions. When shared LoRA employs the same matrices A and B for all views, it fails to preserve spatial consistency in the edited regions (i.e., the ears are lengthened in the front view but appear shorter the back view). In contrast, our proposed Mo VE achieves precise and spatial consistent local 3D editing.

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original object
w/o fine-tune
w/ shared LoRA
w/ MoVE-LORA
front view
back view
Figure 7: Ablation Studies MoVE-LoRA. The edited multi-views generated with the MoVE-
LoRA exhibit the most precise 3D local editing and superior spatial consistency in the edited regions.
w/o repair
w/ repair
Figure 8: Ablation Studies of Full-view Refiner. Full-view Refiner mitigates structural fragmentation and blurriness, which are caused by directly applying sparse multi-view edits to existing 3D objects.
Effectiveness of Full-view Refiner. As shown in Tab. compared with Full-view Refiner (ID-2), introducing this module (ID-3) achieves a 10. 10.6% improvement on the LPIPS metric, indicating enhanced perceptual quality. As shown in Fig. 8 when the edited multi-views are directly used to update the existing 3DGS objects, the edited 3DGS objects exhibit noticeable fragmentation and structural discontinuities (e.g., the nose of pig and the mask of doll). contrast, with Full-view Refiner, the edited 3D objects demonstrate greater structural continuity and improved detail.
5 Conclusion
In this paper, we propose a novel progressive-views paradigm to achieve consistent and precise text-guided 3D editing. Specifically, we design corresponding pipeline Pro3D-Editor which dynamically edits the most editing-salice salient view and propagates its editing semantics to other key views. Extensive experiments show our method outperforms the existing methods in spatial consistency and editing accuracy, demonstrating strong potential for 3D asset manipulation applications. More discussion about limitation and broader impacts available in Appendix C.
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6 Acknowledgements
This research is supported by Artificial Intelligence National Science and Technology Major Project 20232D0121200, and National Natural Science Foundation of China under Grant 623B2094 and 62222212.
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A Implementation Details and Comparative Experiments
A.1 Implementation Details
We use the MV-Adapter SDXL checkpoint our multi-view diffusion model. In pipeline, fine-tune the multi-view attention layers within the MV-Adapter network. For different views, we set distinct B matrices and identical A matrices, with the Jor__rank set to 32 and lora_alpha set to 16. During training, the parameters of the A matrix are updated only by the gradients from the primary view. We fine-tune the model for 800 steps, which takes 45 minutes During inference, we set the classifier-fi free guidance to 2. For 3D editing and refining, we first use a leave-one-ond strategy to train the original 3DGS object for 10k steps, resulting We then render the degraded views corresponding to the target perspectives and use them as the condition for ControlNet- rank of 64 to all attention layers of the controlnet and fine-tune for 1800 steps. Finally, we use the fine-tuned ControlNet-Tile to repair the rendered images of new perspectives and train the degraded 3DGS for an additional 10k steps. The entire 3D editing and refining process takes about 45 minutes.
A100 GPU.
degraded 3DGS.
Using the generated multi-views as the target, we add LoRA with
A.2 Explanation of Quantitative Evaluation Metrics
In terms of evaluating editing quality, FID assesses the overall visual similarity between the edited result and the original object. LPIPS measures perceptual similarity, while PSNR reflects changes in detail. FVD evaluates the temporal continuity and stability across multi-views. The 3D plausibility score and texture details score proposed by GPTEval3D specifically measure the structural rationality and texture detail of 3D editing results. In terms of edit controllability, the text-asset alignment score from GPTEva13D and CLIP-T measure the similarity between the editing results and the editing text. DINO-I measures the similarity between the editing results and the original object. Since our task focuses localized 3D editing, DINO-I can reflect the accuracy of the edits some extent. Overall, these metrics provide a comprehensive quantitative evaluation of both the editing quality and editing accuracy from different perspectives, collectively reflecting the overall performance of the 3D editing method. However, when it comes to view consistency in the editing results, these metrics fall short of accurately reflecting it. Therefore, we provide additional visualizations to fully demonstrate the improvements of our method compared with existing baselines.
A.3 Comparison with Existing Methods
In Fig. we show a detailed editing example. Existing methods often edit the entire object and fail to preserve local regions that are semantically irrelevant to the editing text. Even though LGM and Tailor3D use multi-views generated from our method, they still modify semantically irrelevant regions. Moreover, existing methods such as MVEdit often generate spatially inconsistent 3D objects. In contrast, our method achieves consistent, precise, and high-quality text-guided 3D editing. For more comparison results, please refer to the HTML file provided in our supplementary materials, which contains multiple orbiting videos that demonstrate the improvements of our method text-guided 3D editing.
B
More Ablation Experiments and User Studies
B.1 Ablation of Each Component
Effectiveness of Primary-view Sampler. In Fig. and Fig. I1 we highlight the importance of the Primary-view Sampler. When the primary view is randomly selected and editing semantics are propagated from an editing-sparse view to editing-salicn salient views, it results in inter-view inconsistency (i.e., lack of spatial coherence across views) and intra-view indiscrimination (i.e., poor control over editing-salien views). These issues are clearly illustrated by the inconsistent beard appearance across views in Fig. 10 and the unreasonable editing of the cat's head in certain views in Fig. [1] underscores the necessity of our progressive-vient editing-salient editing-sparse views. The precise and consistent 3D editing achieved by our method stems not merely from fine-tuning, but from this carefully designed paradigm.
paradigm, which directs semantic flow from
Effectiveness of Mo VE-LORA. In Fig. we present qualitative results to demonstrate the effec-
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original
ours
LGM
TD
MVEdit
3DAdapter
Figure 9: Qualitative comparison with existing methods. It can be observed that our method achieves precise and high-quality local 3D editing while addressing the issue of spatial inconsistency.
tiveness of MoVE-LORA in enhancing multi-view editing consistency, as is difficult to accurately evaluate such consistency using existing quantitative metrics. Here, "Shared LoRA" refers to setting where the same LoRA matrices A and B are applied to the latent of miews. As shown in the figure, Shared LoRA fails accurately preserve the original object features (e.g., incorrect object colors) and leads to spatially inconsistent edits (c.g., misaligned ears across views). In contrast, MoVE-LoRA not only better preserves the original object features but also ensures spatially consistent editing across multi-views.
Effectiveness of Full-view Refiner. As shown in Fig. 13 we compare the 3D editing results with and without Full-view Refiner. Without Full-view Refiner, the edited 3DGS object may become fragmented. For example, in the case of doll's mask, the absence of the Full-view Refiner can lead to the generation of numerous floating discrete Gaussians. This because sparse-view guidance of 3DGS updates prioritizes consistency with the given multi-views views specific angles, potentially neglecting the overall 3D structural continuity. contrast, introducing Full-view Refiner provides extra 3D structural information, ensuring the surface continuity of the final edited 3DGS object.
B.2 Human Perception Evaluation
We recruit 8 volunteers to evaluate Pro3D-Editor under different settings from three aspects: Editing Consistency (EC), Editing Accuracy (EA), and Editing Quality (EQ). The volunteers were asked to rank the editing results under different settings from first to fourth place. Each volunteer is given two different edited objects to assess. As shown in Tab. it can be observed that with the addition of each essential module, the final editing results align more closely with human preferences. Notably, the results in the table represent the average rankings given by all volunteers.
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Primary View
Remaining Key Views
Original
Random Sampler
Original
Primary Sampler
Figure 10: Ablation studies of Primary-view Sampler. When the randomly selected view is not the most editing-saliens view, the editing information from this editing-spars sparse view may fail to propagate effectively to the editing-salient salient views, leading to spatially inconsistent across multi-views.
Primary View
Remaining Key Views
Original
Random Sampler
Original
Primary Sampler
Figure 11: Ablation studies of Primary-view Sampler. Since editing-saliet views are difficult to be precisely controlled by editing-spann sparse views, when the randomly selected view is not the most editing-saliem view, the other editing-salient views may produce unreasonable editing results.
C Limitations and Broader Impacts
C.1 Limitations
The Pro3D-Editor is computationally demanding and requires substantial GPU memory, primarily due to the fine-tuning process high-resolution multi-view generation model. Compared to
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original
Shared LoRA
MoVE- LoRA
Ablation studies of Mo Vo-LO.R. Compared with Shared LoRA, MoVE-LORA only better preserves the features of the original multi-views, but also ensures spatial consistency of the editing regions, achieving precise and consistent multi-view editing.
Figure 12:
Original
w/o Refiner
Refiner
Figure 13: Ablation studies Full-view Refiner. Introducing the Full-view Refiner can improve quality of the final 3D editing results by eliminating some floating discrete Gaussians, addressing fragmentation issues, and ensuring the structural continuity of the edited 3D object.
Table 3: Human perception evaluation for different settings. The inclusion of each module achieves more effective editing of 3D results that align with human preferences.
ID
Settings
BC (1 4)
EA (1 4)
EQ (1 4)
naive method
1.75
1.625
1.625 1.875
0 + Primary-view Sampler
1.875
2.125
+ MoVE-LLRA
2.5
2.625
2,4375
3
2 Full-view Refiner
3.875
3.875
3.8125
existing training-free methods, our approach necessitates more computational resources for model training. However, it achieves more precise and consistent 3D editing. The Pro3D-Editor framework also differs from existing methods 2D-guided 3D editing. Existing methods typically generate new 3D object directly from 2D multi-views without considering the structural features of the original 3D object. In contrast, our method employs the concept of sparse 3DGS reconstruction for 3D editing, which is more time-consuming than existing methods in obtaining refined 3D structure.

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C.2 Broader Impacts
Positive Societal Impacts. Pro3D-Editor brings several contributions to the field of text-guided 3D editing. By enabling semantically accurate and spatially consistent edits across multi-views, addresses key limitations of existing training-free approaches, which often suffer from view inconsistency and structural degradation. It has the potential to lower the barrier for creating high- quality 3D content, making easier for designers, artists, and even non-experts to customize 3D assets using intuitive language prompts. This increased accessibility could help foster broader participation 3D content creation and may contribute to progress areas such as digital art, gaming, and virtual reality, where interactive and editable 3D representations are becoming increasingly important.
Negative Societal Impacts. Despite its advantages, the use of Al-driven 3D editing tools may also raise concerns about potential misuse. As the modification of 3D assets becomes easier and more automated, issues related ownership. copyright infringement, and unauthorized replication of proprictary 3D models may arise. The ability to edit and redistribute high-quality 3D content with minimal expertise could blur the lines between original and derivative works, making it more challenging to protect the intellectual property rights of creators. Currently, the protection of original creators often relies on ethical norms rather than enforceable legal mechanisms, which may be insufficient to deter misuse in practice.
25


Reviews:
[{"review_id": "gteP6BVhJf", "reviewer": "Reviewer_uWGa", "summary": "This paper introduces a pipeline, Pro3D-Editor, which leverages Primary-view Sampler (to select and edit the most salient primary view), Key-view Render (to propagate the editing semantics to other key views) and Full-view Refiner (to jointly edite the entire 3D object) to achieve the consistent text-guided 3D editing, in a progressive-views paradigm. It tryies to address the challenges of inter-view consistency and intra-view discrimination in 3D editing task.", "questions": "- From Line120 to Line122, I still don't follow the reason why. Can authors have more detailed or visualized explanations?", "limitations": "- The pipeline is complex to do such simple object-level 3D editing tasks, thus leading to high computation resources (as authors mentioned).\n- The proposed method is inherently limited to the object-level editing, where we can have 360 degree views. In the case of a 3D scene, this method falls. However, authors didn't clarify such point which is important and more challenging in \"3D editing\" task.\n- The experiments are not convincing enough, which can be addressed if authors have more. Since there are already tons of such editing methods recently.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- The paper is well-structured, the pipeline is technically well-designed and several components are described and demonstrated clearly. Overall this paper is easy to follow.\n- Pro3D-Editor introduces a novel and well-motivated solution to conduct the consistent 3D editing task. The three conponents are straightforward and well-designed, where each of them makes great contributions to the entire task.\n- Misture-of-View-Experts Low-Rank Adaption (MoVE-LoRA) is introduced to capture the feature correspondences from the primary view to other views, which is reasonable and easy to understand.\n- The experiment of \"Effectiveness of progressive-views paradigm\" is reasonable to me, which demonstrates the functionality of proposed method instead of naive finetuning. And the improvements are clear to see from the quantitative numbers.\n- The presentations of qualitative comparions are good.\n\n**Weakness**\n\n- The effectiveness of MoVE-LoRA seems vague to me, and its alation experiement is not enough. To strictly evaluate the effectiveness of MoVE-LoRA, it's better to change ID-2 to 0+Primary-view Sampler+Full-view Refiner. From the reported results in paper, the improvements from ID-1 to ID-2 is limited. I suggest to add another ablation as I mentioned above which can be compared to both ID-2 and ID-3.\n- I also think the ablation ID-1 is not convencing enough. Can authors provide another ablation \"3-Primary-view Sampler\"? similar to the first point I listed. In the meantime, ID-2 is good to me.\n- Depite the insights from the three-stage pipeline, this paper uses a fixed number of primary views. However, I believe the better one should support multiple primary views which supports to handle more complicated 3D editings, instead of simplistic objects shown in paper. As also depicted in Figure 3, there obviously are multiple views whose scores are similar and close to the highest. I hope the authors can have more discussions or even experiments about it, what if we use >1 non-overlap primary views?\n- Lack of failure cases demonstrateion and the scope of the proposed solution can handle. Despite the method is straightforward, is it robust enough to handle all kinds of objects or prompts? What if the prompt is irrelevant equally to all views (which means we cannot select a good enough primary view at all)? What if most of views have high scores and how the selection of primary view in this case affect the results?\n- Another point to better demonstrate the scope of the Pro3D-Editor which this paper not invovled. I hope the authors can have more experiments about the kinds of editing tasks. For example, the cases shown in paper all belong to one type: editing one single part (type or color) of the object. However, what if we edit the entire style or appearance? what if we edit only the geometry or only the appearance? I think the former one will further test the Full-view Refiner.\n- Lack of the computing resources and time cost demonstrations.\n\nI think authors should provide a clear scope the proposed solution can handle, whatever it is the best or not. Unless the authors can promise it is the best solution to the entire 3D editing task and have no failures any more.", "was_revised": true, "final_justification": "My questions are all addressed. And I recommend to accept the paper.", "rebuttal": "We sincerely appreciate the reviewer’s valuable and constructive comments, which are crucial for improving the quality of our manuscript. Below, we address each in detail.\n# Weakness 1&2:  \n\"The effectiveness of MoVE-LoRA seems vague to me, ...... I suggest to add another ablation as I mentioned above which can be compared to both ID-2 and ID-3.\" &  \n\"I also think the ablation ID-1 is not convencing enough. Can authors provide another ablation \"3-Primary-view Sampler\"? similar to the first point I listed, ......\"  \n# Response 1&2:  \nCurrent metrics are insensitive to small-scale but critical multi-view inconsistencies, yet such subtle inconsistencies are easily perceived by humans. This limitation of the quantitative metrics causes improvements to appear limited. To support our claim:  \n- Figures 2 and 3 in Appendix show that omitting the Primary-view Sampler leads to a noticeable mismatch between the 3D editing results and the editing prompts.  \n- Figure 4 in Appendix reveals that without MoVE-LoRA, the edited key views exhibit severe multi-view inconsistency.  \n- Figure 5 in Appendix demonstrates that removing the Full-view Refiner results in structural discontinuities caused by inconsistencies across full views.  \n\nWe believe these qualitative ablations, together with the user study presented in Appendix Table 1, more clearly show the effectiveness of each module than the quantitative metrics alone.  \nFollowing your advice, we conduct ablation experiments showing that the Primary-view Sampler significantly improves alignment between the edited object and the prompt, and MoVE-LoRA enhances consistency across the key views.  \n- Comparing ID-0 and ID-3, ID-3 achieves a 4.1% improvement in CLIP-T, indicating that the Primary-view Sampler mainly improves the similarity between results and prompts.  \n- Comparing ID-1 and ID-3, ID-3 achieves a 4.0% improvement in DINO-I, indicating that MoVE-LoRA improves consistency among key views.  \n- Comparing ID-2 and ID-3, ID-3 achieves a 5.6% improvement in DINO-I, indicating that Full-view Refiner improves consistency across full views. Because Full-view Refiner incorporates more views than MoVE-LoRA, the DINO-I improvement from ID-2 to ID-3 is larger than that from ID-1 to ID-3.\n|ID|Primary-view Sampler|MoVE-LoRA|Full-view Refiner|LPIPS &darr;|CLIP-T &uarr;|DINO-I &uarr;|\n|-|-|-|-|-|-|-|\n|0|&cross;|&check;|&check;|0.106|0.292|0.911|\n|1|&check;|&cross;|&check;|0.103|0.303|0.892|\n|2|&check;|&check;|&cross;|0.113|0.302|0.879|\n|3|&check;|&check;|&check;|0.101|0.304|0.928|\n\n# Weakness 3:  \nDespite the insights from the three-stage pipeline, this paper uses a fixed number of primary views, ...... I hope the authors can have more discussions or even experiments about it, what if we use >1 non-overlap primary views?  \n# Response 3:  \nWe clarify that selecting one primary view in our main paper is a specified design choice, intentionally aligned with our core motivation, i.e., ensuring consistency across all views at every step to achieve consistent 3D editing.  \nFirst, using multiple primary views inevitably introduces conflicts among these views, as illustrated in Figure 1(b) of the main paper. Following your advice, we conduct an experiment. In this experiment, we add a tail to a chick. We find that the 90° and 270° views both yield high scores and capture non-overlapping regions. We first edit the 90° view and generate six key views, then replace the corresponding view with the edited 270° view, which resembles providing two primary views. The result exhibits a structural discontinuity at the tail, showing worse performance compared to using one primary view.  \nSecond, using a single primary view to generate other key views is sufficient for 3D object editing. The reason is that, as evidenced by existing 3D editing methods, six views suffice to cover all information required for editing. Thus, we design this progressive pipeline that proceeds from one primary view to six key views, capable of handling most common 3D object editing.\n\n# Weakness 4:  \nLack of failure cases demonstration and the scope of the proposed solution can handle. Despite the method is straightforward, is it robust enough to handle all kinds of objects or prompts? What if the prompt is irrelevant equally to all views (which means we cannot select a good enough primary view at all)? What if most of views have high scores and how the selection of primary view in this case affect the results?  \n# Response 4:  \nFollowing your advice, we will add more failure cases in the revision. We clarify that most observed failure cases stem from 2D editing failures, which fall outside the scope of our main motivation. For example, when we instruct the 2D editor to modify a doll’s right hand, it edits both of the hands, yielding a 3D result that mismatches the prompt.  \nOur Primary-view Sampler achieves high accuracy. Expanding our benchmark to 40 cases, we find only two primary views are not best, yielding an overall accuracy of 95%.  \nWhen the prompt is irrelevant to all views, the 2D editor cannot edit the primary view correctly, causing a mismatch between the 3D result and the prompt. We clarify that such cases fall outside our consideration, as the failure originates upstream in the 2D editing stage.  \nBesides, it is rare for most views to have high scores, yet our method remains robust even in such cases. To support our claim, we deliberately choose a tree that appears identical from every 360° angle and a yellow star on its top. The star appears identical from the 0° and 180° viewpoints. Our prompt is \"edits the yellow star to a red one\". In this case, views within 0°-40°, 140°-220°, 320°-360° have high scores. Our method performs well regardless of which view is chosen as the primary view.\n\n# Weakness 5:  \nAnother point to better demonstrate the scope of the Pro3D-Editor which this paper not invovled. I hope the authors can have more experiments about the kinds of editing tasks, ...... However, what if we edit the entire style or appearance? what if we edit only the geometry or only the appearance? I think the former one will further test the Full-view Refiner.  \n# Response 5:  \nIn the revision, we will explicitly state that our scope is focused on 3D objects, including the entire style or appearance edit. We plan to extend our paradigm to broader 3D editing tasks (e.g., 3D scene) in the future.  \nFollowing your advice, we conduct experiments that apply global style edits (e.g., making an object transparent) and other editing operations (e.g., addition and deletion). We provide quantitative metrics on a larger benchmark (main paper’s local edits benchmark with equal-sized global edits) in the table below. The results show that our method performs equally well on these tasks, demonstrating its versatility.  \nWe clarify that global edits are omitted from the main paper because they are generally easier than local edits. Global edits only require view-consistent changes, whereas local edits must additionally preserve every semantic-irrelevant region. Besides, global edits are easier to Full-view Refiner, since this task amounts to reconstructing the object from multi-views. As Full-view Refiner is adapted from sparse-view reconstruction techniques, it is capable of handling global object editing tasks.\n|Methods|FID&darr;|PSNR&uarr;|LPIPS&darr;|FVD&darr;|CLIP-T&uarr;|DINO-I&uarr;|\n|-|-|-|-|-|-|-|\n|Tailor3D|123.68|13.76|0.308|987.2|0.280|0.712|\n|MVEdit|172.30|14.12|0.355|1701.1|0.298|0.747|\n|LGM|89.06|17.41|0.204|698.1|0.296|0.824|\n|**Ours**|66.09|21.26|0.106|482.9|0.301|0.919|\n\n# Weakness 6:  \nLack of the computing resources and time cost demonstrations.  \n# Response 6:  \nFollowing your advice, we provide our computing resources and time cost in the table below. For similar edits on the same 3D object (e.g., edit the same doll wearing different hats), our method can reuse the LoRA to reduce the editing time to about 10 min. We clarify that our method mainly focuses on improving the performance upper bound of 3D editing within practically acceptable time budgets. Our editing accuracy and consistency far surpass existing methods, achieving a 47.4% improvement in LPIPS.  \nThe time cost of our method is acceptable in light of the improvements we delivers. In real-world 3D asset production, artists often spend several hours editing a single object, much longer than ours. Our method improves editing quality while minimizing time cost. Compared with our substantial performance improvement, the time cost is practically acceptable.  \nMeanwhile, in existing AIGC research, we believe that generation quality is much more important than generation efficiency. For example, fine-tuning methods like DreamBooth remain in active production use because they outperform tuning-free approaches. In the future, we will explore training a universal encoder-decoder model for multi-view editing to improve editing efficiency.\n|Method|Editing Time|GPU Memory Usage|\n|-|-|-|\n|Tailor3D|4 min|65 GB|\n|LGM|5 sec|10 GB|\n|MVEdit|6 min|13 GB|\n|3DAdapter|10 min|24 GB|\n|**Ours**|90 min|60 GB|\n|**Ours(reuse MoVE-LoRA)**|10min|20 GB|\n\n# Question 1:  \nFrom Line120 to Line122, I still don't follow the reason why. Can authors have more detailed or visualized explanations?  \n# Response 7:  \nWe clarify that our penalty term introduced in Line120-122 is tailored to base model’s specific view layout, ensuring edits are minimized on the missing viewpoints. Conditioned on a primary view, our base model generates six views at 0°, 45°, 90°, 180°, 270°, and 315°, which are not uniformly spaced. The 135° and 225° views are missing. Consequently, Full-view Refiner receives more views of the front viewpoints than the back, which degrades editing quality when large changes are required on the rear side. To mitigate this imbalance, this penalty term help choose a primary view that is editing salient while keeping any required edits on the rear as minimal as possible."}, {"review_id": "jUcEMolVU2", "reviewer": "Reviewer_xREP", "summary": "This paper introduces a novel approach to text-guided 3D editing, aiming to precisely modify semantically relevant local 3D regions with applications in 3D games and film production. Unlike existing methods that edit 2D views indiscriminately and project them into 3D space, often leading to inconsistent multi-view results, the proposed Pro3D-Edit framework addresses these issues using a progressive-views paradigm, which propagates editing semantics from the editing-salient view to other editing sparse-views. Experiments demonstrate superior editing accuracy and spatial consistency compared to existing methods.", "questions": "1. Could the authors provide more adequate and varied qualitative results, as well as intermediate edited primary views and key views?\n2. How are the individual steps of the editing process distributed during this 1.5-hour training period on a sheet of A100?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1. The limitation of current approaches and the motivation for a new paradigm are clearly illustrated in Fig. 1, making the need for improvement easily understandable.\n2. The concept of a progressive-views paradigm for consistent and precise 3D editing by propagating editing semantics from salient views to sparse views has the potential to be a seminal contribution.\n\n### Weaknesses\n1. The proposed approach requires the input 3D object to be represented as 3DGS and is incompatible with meshes, raising concerns about practical applicability and compatibility with recent 3D generation methods that directly output meshes.\n2. The diversity of presented results is limited, raising concerns on the method's versatility.\n3. Edited primary views and key views are not presented, which are important to understand how this paradigm works.", "was_revised": true, "final_justification": "With the time cost breakdown and new quantitative results, most of my concerns have been addressed. If the author can provide additional qualitative results in the updated manuscript, I would like to recommend acceptance.", "rebuttal": "Thank you for your constructive comments on our manuscript, which help us reorganize the our manuscript more clearly. We provide detailed responses to each point below.  \n# Weakness 1:  \nThe proposed approach requires the input 3D object to be represented as 3DGS and is incompatible with meshes, raising concerns about practical applicability and compatibility with recent 3D generation methods that directly output meshes.  \n# Response 1:  \nWe clarify that the core contribution of our method is to progressively and consistently project 2D editing features into 3D, which is not tied to any specific 3D representation. By modifying the Full-view Refiner, the proposed framework can be directly adapted to meshes. Our other modules Primary-view Sampler and Key-view Render achieves a 42.3 % improvement in LPIPS and a 6.8 % improvement in DINO-I (comparing ID-0 and ID-2 in Table 2 of the main paper), which is independent with 3D representation.  \nBecause the 3D object local editing task requires keeping semantically irrelevant regions unchanged, we adopt 3DGS instead of meshes. The iterative, differentiable nature of 3DGS naturally supports this requirement, making it better suited to this task. Besides, existing baselines also support only a single 3D representation. For example, LGM operates solely on 3DGS, whereas MVEdit is restricted to meshes.  \nIn future work, we will explore combining our current progressive editing paradigm with 3D generation models to achieve precise 3D object local editing directly on 3D meshes, for example, leveraging the encoder of 3D generation models, we can encode the mesh into a latent space and perform editing under the supervision of edited key views.\n\n# Weakness 2:  \nThe diversity of presented results is limited, raising concerns on the method's versatility.  \n# Response 2:  \nWe clarify that our method is applicable to versatile 3D object editing tasks, including local, global and stylization edits. We focus on local editing in the main paper because it is more challenging: while global editing only requires multi-view consistency, local editing must additionally preserve all semantically irrelevant 3D regions.  \nFollowing your advice, we conduct experiments that apply global style edits (e.g., making an object transparent) and other editing operations (e.g., addition and deletion). We provide quantitative metrics on a larger benchmark (main paper’s local edits benchmark with equal-sized global edits) in the table below. The results show that our method performs equally well on these tasks, demonstrating its versatility across a broader range of 3D object editing tasks.  \nWe also clarify that our new benchmark covers most common cases for 3D object editing. We will present more editing results on complex 3D objects in the revision. Besides, existing baselines employ similarly sized benchmarks. For example, PrEditor3D contains only 18 objects and 40 prompts.\n|Methods|FID&darr;|PSNR&uarr;|LPIPS&darr;|FVD&darr;|CLIP-T&uarr;|DINO-I&uarr;|\n|-|-|-|-|-|-|-|\n|Tailor3D|123.68|13.76|0.308|987.2|0.280|0.712|\n|MVEdit|172.30|14.12|0.355|1701.1|0.298|0.747|\n|LGM|89.06|17.41|0.204|698.1|0.296|0.824|\n|**Ours**|66.09|21.26|0.106|482.9|0.301|0.919|\n\n# Weakness 3 & Question 1:  \nEdited primary views and key views are not presented, which are important to understand how this paradigm works. &  \nCould the authors provide more adequate and varied qualitative results, as well as intermediate edited primary views and key views?  \n# Response 3 & 4:  \nWe clarify that Figures 2 and 3 in Appendix present both the edited primary view and the key views, demonstrating the accuracy of our Primary-view Sampler and the consistency of the edited key views. Figures 2 and 3 in Appendix show the edited primary views in the left-most column and the edited key views in the remaining columns. For example, the left-most column of Figure 3 in Appendix shows that we selected an appropriate primary view and edited the chick into a cat. The remaining columns of Figure 3 in the Appendix show that, under the guidance of the edited primary view, our method successfully generates accurate and consistent key views. Following your advice, we will move these visualizations to a more obvious position in the main paper and provide more examples in the revision.\n\n# Question 2:  \nHow are the individual steps of the editing process distributed during this 1.5-hour training period on a sheet of A100?  \n# Response 5:  \nFollowing your advice, we report the per-step time cost in the table below. We clarify that our method aims to improve the performance upper bound of 3D editing within practically acceptable time budgets. Compared with prior work, our method raises LPIPS by 47.4%.  \nWe clarify that the time cost is acceptable in light of the improvements our method delivers. In real-world 3D asset production, artists often spend several hours editing a single object, much longer than our method. Our method improves editing quality while minimizing time cost. Compared with our substantial performance improvement, the time cost is practically acceptable.  \nMeanwhile, in existing AIGC research, we believe that generation quality is much more important than generation efficiency. For example, fine-tuning methods like DreamBooth remain in active production use because they outperform tuning-free approaches. In the future, we will explore training a universal encoder-decoder model for multi-view editing to improve editing efficiency.\n|Step|Time cost|\n|------|------|\n|Primary-view Sampler|10 s|\n|MoVE-LoRA Fine-tuning (in Key-view Render)|50 min|\n|Edited Key-views Inferencing (in Key-view Render)|30 s|\n|Repair Module Fine-tuning (in Full-view Renfiner)|30 min| \n|3D Object Updating (in Full-view Renfiner)|10 min|"}, {"review_id": "NLnR1RYLJ4", "reviewer": "Reviewer_URcu", "summary": "This paper introduces Pro3D-Editor, a three-stage pipeline for 3D editing: (1) selecting and editing a primary view using a proposed Primary-View Sampler; (2) editing six key views via the Key-View Render module with a Mixture-of-View-Experts Low-Rank Adaptation; and (3) propagating edits to all other views through a Full-View Refiner. The method is compared against several baselines on a custom benchmark of six objects and fifteen prompts, evaluating both editing quality and semantic accuracy.", "questions": "1. Why do the reported editing times differ between the main text and the supplementary materials?\n2. How sensitive is Pro3D-Editor to hyperparameters such as \\alpha (view-salience weight) and \\lambda (refinement weight)? What value ranges were tested?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strength:**\n1. The paper is well written and easy to follow.\n2. Visual results are impressive, showing precise, view-consistent edits.\n3. A diverse set of metrics (FID, PSNR, LPIPS, FVD, CLIP-T, DINO-I, GPTEval3D) thoroughly evaluates both editing quality and accuracy.\n\n**Weakness:**\n\n1. Mask computation details are missing. It’s unclear how masks are generated in the two-stage pipeline and how much the two-stage process contributes without examples or descriptions of the masks. Would performance drop significantly if the two-stage step were removed?\n2. Hyperparameter sensitivity is unexplored. The salience score in the Primary-View Sampler uses a naïvely designed weighting parameter \\alpha, but no ablation studies show how the distribution changes with different \\alpha values.\n3. Small evaluation benchmark. Testing on only six objects and fifteen prompts limits conclusions about generalization to more complex or varied 3D assets.\n4. High computation time. Pro3D-Editor requires about 1.5 hours per edit, compared to prior methods like MVEdit (~5 minutes), which may affect its practical use in real-world scenarios.", "was_revised": true, "final_justification": "After reading the rebuttal and the reviews from other reviewers, and considering the discussion phase, most of my concerns have been addressed. I acknowledge that the proposed method outperforms existing approaches in terms of performance. However, I still believe that the time cost will be a significant bottleneck for its practical usage. Therefore, I will maintain my current rating.", "rebuttal": "We appreciate the reviewer’s valuable suggestions and will incorporate them into the manuscript. Below, we address each point in detail.  \n# Weakness 1:  \nMask computation details are missing. It’s unclear how masks are generated in the two-stage pipeline and how much the two-stage process contributes without examples or descriptions of the masks. Would performance drop significantly if the two-stage step were removed?  \n# Response 1:  \nWe clarify that the mask computation employs a standard practice rather than a core contribution of our method, so we omit its details in the main paper. To present our entire paradigm more clearly, our main paper focuses on the three other core modules and omits the mask-computation details. Following your advice, we provide the full implementation details of mask computation and will add these details in the revision.  \nGiven the original multi-view renders $I_c$ and the edited multi-views $I_e$ generated by MoVE-LoRA, we compute the pixel-wise difference $|I_e − I_c|$, remove isolated outliers, and take the remaining regions as the multi-view editing masks.  \nThe two-stage step mainly boosts multi-view consistency, yet its impact on final 3D-editing quality is secondary to that of our other modules (e.g., Primary-view Sampler, MoVE-LoRA, and Full-view Refiner). We provide an ablation study on removing this mask. Although removing the two-stage step causes a drop in quantitative metrics, the decline is far steeper when any of the other modules are removed.\n|Settings|LPIPS &darr;|CLIP-T &uarr;|DINO-I &uarr;|\n|------|------|------|------|\n|w/o two-stage step|0.104|0.303|0.908|\n|w/o Primary-view Sampler|0.106|0.297|0.911|\n|w/o MoVE-LoRA|0.103|0.303|0.892|\n|w/o Full-view Refiner|0.113|0.302|0.879|\n|Full modules|0.101|0.304|0.928|\n\n# Weakness 2:  \nHyperparameter sensitivity is unexplored. The salience score in the Primary-View Sampler uses a naïvely designed weighting parameter \\alpha, but no ablation studies show how the distribution changes with different \\alpha values.  \n# Response 2:  \nWe clarify that our hyper-parameter $\\alpha$ is robust. It performs well across a wide range of values. We provide corresponding ablation experiments about $\\alpha$ here. We set $\\alpha$  from 0 to 1 and sample it every 0.05. Experiments indicate an $\\alpha$ either too large or too small yields an unreasonable peak distribution.  \n- Small $\\alpha$  (0 - 0.3): The chosen primary view gives little weight to back-side edit density, causing unreasonable peaks. For example, in the bottom half of Figure 3 in the main text, an incorrect peak emerges near 200°. Yet for the prompt “Girl wears a red dress”, the front view should be chosen first.  \n- Large $\\alpha$ (0.7 - 1.0): Too large a penalty weight shifts the peak. For example, in the upper half of Figure 3 in the main text, peak shifts away from the side toward front-back views, which are ill-suited for editing prompt “Car with a tail on the back”.       \n  \nThe ablation experiments show that the hyper-parameter $\\alpha$ performs well in the range 0.3 - 0.7, showcasing its robustness.\n\n# Weakness 3:  \nSmall evaluation benchmark. Testing on only six objects and fifteen prompts limits conclusions about generalization to more complex or varied 3D assets.  \n# Response 3:  \nWe clarify that our benchmark is of the same scale as those in existing methods, for example, PrEditor3D contains only 18 objects and 40 prompts. Following your advice, we expand our benchmark. Our new beachmark includes global style edits (e.g., making an object transparent) and other editing operations (e.g., addition and deletion). And we provide quantitative metrics on this larger benchmark (main paper’s local edits benchmark with equal-sized global edits) in the table below. The results show that our method performs equally well on these tasks, demonstrating its versatility across a broader range of 3D object editing tasks.  \nWe also clarify that global editing is generally easier than local editing: the former only requires view-consistent changes, whereas the latter must additionally preserve every semantic-irrelevant region. Thus, in the main paper, we focus on the task of 3D object local editing and omits results for 3D global editing. We will present more editing results on complex 3D objects in the revision.\n|Methods|FID&darr;|PSNR&uarr;|LPIPS&darr;|FVD&darr;|CLIP-T&uarr;|DINO-I&uarr;|\n|-|-|-|-|-|-|-|\n|Tailor3D|123.68|13.76|0.308|987.2|0.280|0.712|\n|MVEdit|172.30|14.12|0.355|1701.1|0.298|0.747|\n|LGM|89.06|17.41|0.204|698.1|0.296|0.824|\n|**Ours**|66.09|21.26|0.106|482.9|0.301|0.919|\n\n# Weakness 4:  \nHigh computation time. Pro3D-Editor requires about 1.5 hours per edit, compared to prior methods like MVEdit (~5 minutes), which may affect its practical use in real-world scenarios.  \n# Response 4:  \nWe clarify that our method mainly focuses on improving the performance upper bound of 3D editing within practically acceptable time budgets. Our editing accuracy and consistency far surpass existing methods, achieving a 47.4% improvement in LPIPS. Besides, for similar edits on the same 3D object (e.g., edit the same doll wearing different hats), our method can reuse the LoRA to reduce the editing time to about 10 min.  \nWe clarify that the time cost is acceptable in light of the improvements our method delivers. In real-world 3D asset production, artists often spend several hours editing a single object, much longer than our method. Our method improves editing quality while minimizing time cost. Compared with our substantial performance improvement, the time cost is practically acceptable.  \nBesides, existing 3D object editing methods such as MVEdit, though faster, often produce results that lose most original object features, making them unsuitable for real-world asset production.  \nMeanwhile, in existing AIGC research, we believe that generation quality is much more important than generation efficiency. For example, fine-tuning methods like DreamBooth remain in active production use because they outperform tuning-free approaches. In the future, we will explore training a universal encoder-decoder model for multi-view editing to improve editing efficiency.\n\n# Question 1:  \nWhy do the reported editing times differ between the main text and the supplementary materials?  \n# Response 5:  \nWe clarify that the editing time reported in the main text is same as that in the supplementary materials. We hypothesize this confusion stems from our splitting the editing time into two parts in the supplementary materials while reporting only the total in the main text. In the supplementary materials, Line 13 states that Primary-view Sampler + Key-view Render together take about 45 min, and Line 20 states that Full-view Refiner takes another 45 min, summing to about 1.5 h. And in the main text, Line 231 states that the entire editing process takes about 1.5h.\n\n# Question 2:  \nHow sensitive is Pro3D-Editor to hyperparameters such as \\alpha (view-salience weight) and \\lambda (refinement weight)? What value ranges were tested?  \n# Response 6:  \nWe clarify that our hyper-parameter $\\alpha$ and $\\lambda$ are robust and they perform well across a wide range of values. The hyper-parameter $\\alpha$ aims to balance the editing saliency between front and back views, and $\\lambda$ aims to decoupling redundant features learned by MoVE-LoRA, ensuring consistency in the edited regions. Our method uses only two hyper-parameters, both of which yield good 3D-editing performance across a wide range of values.  \nWe test $\\alpha$ from 0 to 1 in steps of 0.05. Experimental results demonstrate that values between 0.3 and 0.7 produce reasonable score peaks, thus we choose the midpoint 0.5.  \nLikewise, we test $\\lambda$ from 0 to 1 in steps of 0.1. Experimental results demonstrate that values between 0.3 and 0.6 best balance the spatial relationships learned by MoVE-LoRA and the base model’s intrinsic feature consistency. By balancing the influence of the base model and MoVE-LoRA on the edited regions, we ensure the multi-view spatial structure learned by MoVE-LoRA while disentangle its redundant features. We finally set $\\lambda$ to the same midpoint 0.5."}, {"review_id": "dwwOGYRSal", "reviewer": "Reviewer_BzhZ", "summary": "This paper proposes Pro3D-Editor, a novel progressive-views framework for text-guided 3D editing that focuses on improving spatial consistency across views. The method introduces a progressive editing strategy starting from the most salient view and propagating editing semantics to other key views using a specially designed MoVE-LoRA module, followed by a full-view refinement stage. Experiments on multiple editing prompts demonstrate clear improvements in editing quality and consistency over prior works.", "questions": "1. How sensitive is the final 3D editing quality to the accuracy of the Primary-view Sampler?\n2. Could the authors provide a discussion on failure cases, especially when the Primary-view Sampler selects an incorrect primary view? Will it degrade to existing view-indiscriminate editing methods?\n3. How does the inference time of Pro3D-Editor compare with other editing methods? \n4. Is there any way to reduce the per-edit LoRA fine-tuning overhead, such as reusing or sharing LoRA?", "limitations": "The progressive-view edit strategy depends on the accuracy of the Primary-view Sampler, and yet the paper does not discuss the consequences of selecting an incorrect primary view. The authors could provide a more thorough discussion on this aspect, and consider mitigation strategies accordingly.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n+ The writing is clear and easy to follow.\n\n+ The proposed progressive-view editing strategy is intuitive and experimentally shown to be effective at improving view consistency.\n\n+ The experiments and ablations are well-designed.\n\n**Weaknesses:**\n+ The method is computationally expensive, requiring per-edit LoRA fine-tuning and a multi-stage inference pipeline.\n\n+ The overall editing performance seems heavily depends on the Primary-view Sampler. The paper lacks a thorough discussion on its accuracy and does not explore the consequences if the selected primary view is incorrect. \n\n+ The paper does not include inference time comparisons with other baselines. Given that Pro3D-Editor is a training-based approach, including such results is crucial for a fair and practical assessment.\n\n+ No failure cases are discussed.", "was_revised": true, "final_justification": "While the results are promising and the method shows strong editing performance, the computational overhead remains relatively high for per-edit usage. I believe the paper has merit but would benefit from further refinement on efficiency and broader applicability, so I will keep my rating.", "rebuttal": "Thank you for your valuable suggestions and constructive comments, which are crucial for improving the quality of our manuscript. Below, we address each in detail.\n# Weakness 1 & Weakness 3 & Question 3:  \nThe method is computationally expensive, requiring per-edit LoRA fine-tuning and a multi-stage inference pipeline. &  \nThe paper does not include inference time comparisons with other baselines. Given that Pro3D-Editor is a training-based approach, including such results is crucial for a fair and practical assessment. \n&  \nHow does the inference time of Pro3D-Editor compare with other editing methods?  \n# Response 1 & 2 & 3:  \nFollowing your advice, we provide a time cost comparison with other methods in the table below. We clarify that our method mainly focuses on improving the performance upper bound of 3D editing within practically acceptable time budgets. Our editing accuracy and consistency far surpass existing methods, achieving a 47.4% improvement in LPIPS. And for similar edits on the same 3D object (e.g., edit the same doll wearing different hats), our method can reuse the trained LoRA weights. In this case, our method requires only inference, taking roughly 10 minutes, comparable to the runtime of baselines. In contrast, other existing methods (e.g., MVEdit) cannot achieve such precise and consistent 3D object local editing.  \nBesides, the time cost of our method is acceptable in light of the improvements we delivers. In real-world 3D asset production, artists often spend several hours editing a single object, much longer than ours. Our method improves editing quality while minimizing time cost. Compared with our substantial performance improvement, the time cost is practically acceptable.  \nMeanwhile, in existing AIGC research, we believe that generation quality is much more important than generation efficiency. For example, fine-tuning methods like DreamBooth remain in active production use because they outperform tuning-free approaches. In the future, we will explore training a universal encoder-decoder model for multi-view editing to improve editing efficiency.\n|Method|Editing time|\n|------|------|\n|Tailor3D|4 min|\n|LGM|5 sec|\n|MVEdit|6 min|\n|3DAdapter|10 min|\n|Ours|50 min (MoVE-Lora fine-tuning) + 30s (Key-views inference) + 30min (Repair module fine-tuning) + 10min (3DGS updating)|\n\n# Weakness 2:  \nThe overall editing performance seems heavily depends on the Primary-view Sampler. The paper lacks a thorough discussion on its accuracy and does not explore the consequences if the selected primary view is incorrect.  \n# Response 4:  \nWe clarify that our editing performance heavily depends on the Primary-view Sampler. Thus, we improve the accuracy of our Primary-view Sampler by leveraging CLIP similarity while simultaneously accounting for the editing information density across multiple views. The accuracy of our Primary-view Sampler is high, reaching 95%. We expand our benchmark to 40 complex cases. Results show that, in most cases, the Primary-view Sampler yields a sensible score distribution, choosing a suitable primary view. Only 2 of 40 examples select a not best primary view, leading to implausible 3D edits.  \nIn extreme cases, e.g., when the editing prompt is entirely irrelevant to the 3D object, no view exhibits editing saliency, causing the 2D editor to apply an unreasonable edit to the primary view, ultimately leading to 3D editing failure. Such cases are exceedingly rare. Because every view carries no editing information density in these rare cases, our method degrades into view-indiscriminate editing and the results closely resemble those shown for “Random Sampler” in Appendix Figure 3. For common 3D object editing tasks, our method handles them reliably.\n\n# Weakness 4 & Question 2:  \nNo failure cases are discussed. &  \nCould the authors provide a discussion on failure cases, especially when the Primary-view Sampler selects an incorrect primary view? Will it degrade to existing view-indiscriminate editing methods?  \n# Response 5 & 6:  \nFollowing your advice, we will provide more failure cases in the revision. And failure cases closely resemble those shown for “Random Sampler” in Appendix Figure 3.  \nWhen the Primary-view Sampler selects an incorrect primary view, our method degrades into view-indiscriminate editing method, but such cases are exceedingly rare. We clarify that such failures primarily occur when the editing prompt is irrelevant to the 3D object, such cases hardly ever occur in real-world 3D asset production. For common 3D object editing tasks, our method handles them reliably. Besides, other methods also fail to produce correct 3D edits in such rare cases. By comparison, our incorrect results preserve more original 3D object features, outperforming the incorrect outputs of other methods.\n\n# Question 1:  \nHow sensitive is the final 3D editing quality to the accuracy of the Primary-view Sampler?  \n# Response 7:  \nOur 3D editing quality heavily depends on the accuracy of the Primary-view Sampler. If the Primary-view Sampler selects a primary view that the 2D editor can not edit properly, our 3D edit will also fail. We first clarify that our Primary-view Sampler achieves 95% accuracy, it can handle common 3D object editing tasks reliably. Second, our pipeline demonstrates robustness to the choice of primary view. We conduct experiments in which any of the top-five ranked views is chosen as the primary view, and the resulting 3D edits are usually reasonable. However, selecting the primary view from the sixth- to tenth-ranked views usually yields implausible results.  \nWe provide quantitative metrics in the table below. Using any of the top-five ranked views as the primary view has minimal impact on editing quality, whereas selecting views ranked sixth to tenth leads to a noticeable drop in performance. It clearly shows that our method is robust.\n|Settings|FID &darr;|PSNR &uarr;|LPIPS &darr;|FVD &darr;|CLIP-T &uarr;|DINO-I &uarr;|\n|------|------|------|------|------|------|------|\n|Top ranked views|63.99|22.01|0.101|449.9|0.304|0.928|\n|Top-five ranked views|69.47|19.34|0.136|490.6|0.302|0.908|\n|Sixth- to tenth-ranked views|82.92|17.09|0.173|614.1|0.289|0.876|\n\n# Question 4:  \nIs there any way to reduce the per-edit LoRA fine-tuning overhead, such as reusing or sharing LoRA?  \n# Response 8:  \nOur method allows the same MoVE-LoRA to be reused for similar edits on a given object, minimizing amortized time. For example, when we edit the same doll wearing different hats, we can reuse the same MoVE-LoRA, reducing inference time to 10 minutes. However, sharing a single MoVE-LoRA across distinct objects is infeasible, as it leads to multi-view inconsistencies and fails to preserve semantically relevant regions during editing."}]

Author rebuttal:

Meta-review: The paper receives four acceptance rating. Initially, the reviewers have concerns about some technical clarity, computational efficiency for each edit, and experimental results (e.g., ablation study). After the rebuttal, despite the suggested improvement on the editing runtime, all the reviewers are satisfied with the addressed issues in the rebuttal. The AC closely checks the paper, reviews, rebuttal, and the discussed points, and agrees with the assessment from reviewers. Therefore, the AC recommends the acceptance rating and encourages the authors to incorporate the suggestions raised by the reviewers in the final version, as well as releasing the model and code for reproducibility.
Decision: Accept (poster)