Variation-aware Flexible 3D Gaussian Editing
Authors: Hao Qin
Yukai Sun
Meng Wang
Ming Kong
Mengxu Lu
Qiang Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: 3d editing
3d gaussian splatting
knowledge distillation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Indirect editing methods for 3D Gaussian Splatting (3DGS) have recently witnessed significant advancements. These approaches operate by first applying edits in the rendered 2D space and subsequently projecting the modifications back into 3D. *However, this paradigm inevitably introduces cross-view inconsistencies and constrains both the flexibility and efficiency of the editing process*. To address these challenges, we present **VF-Editor**, which enables native editing of Gaussian primitives by predicting attribute variations in a feedforward manner. To accurately and efficiently estimate these variations, we design a novel variation predictor distilled from 2D editing knowledge. The predictor encodes the input to generate a variation field and employs two learnable, parallel decoding functions to iteratively infer attribute changes for each 3D Gaussian. Thanks to its unified design, VF-Editor can seamlessly distill editing knowledge from diverse 2D editors and strategies into a single predictor, allowing for flexible and effective knowledge transfer into the 3D domain. Extensive experiments on both public and private datasets reveal the inherent limitations of indirect editing pipelines and validate the effectiveness and flexibility of our approach.

Published as a conference paper at ICLR 2026
VARIATION-AWARE FLEXIBLE 3D GAUSSIAN EDITING
Hao Qin'; Yukai Sun Meng Wang', Ming Kongl.2) Mengxu Lu Qiang Zhu
"Zhejiang University *Zhejiang Key Laboratory of Geographic Information Science fhacqin, 3220101205, 22451130, 2 jjkknnging, lumengxu, zhug 0 ju w.w.d.....mm en
"Put party hat on her"
Variation
Predictor
~ 0.3s
Diverse Editing Results
Make colortal
Make colortul 2
Visualization of the Variation
Replace sunflower
Make bronte
"Make fashion sunglasses
Flexible Editing
"Give him masloche"
Variation Fusion
Tolkien Eyj
Free Mixing
Figure 1: VF-Editor is a native editing method for 3D Gaussian Splatting across multiple scenes and instructions. In the top-left corner, we present 2D visualization of the 3D variation within VF-Editor; please refer to App. C for specific visualization rules.
ABSTRACT
Indirect editing methods for 3D Gaussian Splatting (3DGS) have recently wit- nessed significant advancements. These approaches operate by first applying edits in the rendered 2D space and subsequently projecting the modifications back into 3D. However, this paradigm inevitably introduces cross-view inconsistencies and constrains both the flexibility and efficiency of the editing process. To address these challenges, we present VF-Editor, which enables native editing of Gaussian primi- tives by predicting attribute variations feedforward manner. To accurately and efficiently estimate these variations, we design a novel variation predictor distilled from 2D editing knowledge. The predictor encodes the input to generate a variation field and employs two learnable, parallel decoding functions to iteratively infer attribute changes for each 3D Gaussian. Thanks to its unified design, VF-Editor can seamlessly distill editing knowledge from diverse 2D editors and strategies into single predictor, allowing for flexible and effective knowledge transfer into the 3D domain. Extensive experiments on both public and private datasets reveal the inherent limitations of indirect editing pipelines and validate the effectiveness and flexibility of our approach.
INTRODUCTION
Personalized 3D editing is important in many fields such as virtual reality, industrial design, and game development, can substantially enhance creative efficiency and quality Botsch & Kobbeli (2005). With advancements in 2D-AIGC technology Saharia et al. (2022) Ramesh et al. (2021) Rombach
"Equal contribution
Corresponding author

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Published as a conference paper at ICLR 2026
et al. (202bb) and 3D representation methods Mildenhall et al. (2021): Wynn & Turmukhambetov (2023) : Kerbl et al. (2023); Chung et al. (2024). significant progress has been made in text- based 3D
editing techniques Lu al. (2024b) Mikaaill et al. (2023) Fang et al. (2024b) Chen et al. (2024b);
Fang et al. (2024a).
(023) Vachha & Haque (2024) involves obtaining edited images from various views of a scene via 2D editors, then reconstructing the 3D scene based on these images. Although this approach enables diverse 3D edits, it still faces several challenges: 1) Since the 2D editor cannot ensure consistent editing patterns across views, conflicts often arise between different views in the reconstructed result. The separate 2D editing and 3D reconstruction processes across different editing rounds constrain both the flexibility and efficiency of the final 3D editing outcomes. Some studies alleviate view inconsistencies by exchanging attention maps among views during 21) editing Dong & Wang (2023a); Hu et al. (2024): Karim et al. (2024) Wang et al. (2024)); Chen et al. (2024a). However, due to the black-box nature of neural networks, such approaches are insufficient to fundamentally resolve the inconsistencies across views. Moreover, research on enabling flexible interaction between different rounds of 3D editing remains relatively scarce.
A common strategy Haque et al.
In the field of 3D generation, increasing attention has been directed toward native generation based on forw-forward networks Li et al. (2025b); Wang et al. (2024c); Zhao et al. (2025b); Hong et al. (2024). This is because native 3D generative models fundamentally avoid the view inconsistency issues commonly associated with reconstruction based approaches, while also significantly enhancing the flexibility of 3D content creation Xiang al.] (2025); [Shi et al.) (2025). Motivated by this, aim to address the limitations of current indirect editing paradigms by training native 3D editor. However, due the scarcity of training data, is infeasible to efficiently train such a feed-forward 3D editor using standard supervised learning techniques.
To this end, we propose an innovative framework, VF-Editor, which distills 2D editing priors into 3D editing knowledge and enables the training of a feed-forward 3D editor. Rich 2D editing priors provide sufficient knowledge for model training, but we find that a 3DGS editor that directly predicts the edited results is still difficult to converge. Given the explicit nature of 3D Gaussian Splatting Kerbl et al. (2023). we can predict the variations of all primitives under given editing instruction. the final edited result can be obtained by superimposing these variations onto the original attributes Lu jet al. (2024a). Compared with directly predicting the edited outputs, modeling the variations alleviates the learning burden. Moreover, assigning precise variation values to each attribute of every primitive allows fine-graing -reigga control over the editing region and intensity, as well as the ability to compose multi-stage editing results for more personalized outcomes. Therefore, in VF-Editor, we redefine the 3DGS editing task as a feed-forward variation prediction problem.
The variation predictor of VF-Editor contains two key components: the variation field generation module and the parallel decoding function. The variation field generation module is used to encode input information and generate variation field, while the parallel decoding function is employed to parallelly parse the variation of each 3D Gaussian from the variation field. By uniformly compressing specific variation quantities into the latent space, the variation field generation module effectively avoids the multi-round optimization process in traditional variation modeling methods Wu et al. (2024a); Fridovich- Keil et al. (2023). Additionally, the parallel decoding function achieves linear computational complexity related to the number of Gaussian primitives, and the decoding process can be accelerated through parallel computation. To mitigate the model convergence issues caused by the intercoupling of various non-structural attributes within the 3D Gaussians Charatan et al. (2024); Zou et al. 2024), we design two parallel decoding functions for iterative prediction of the variation.
The superiority of VF-Editor primarily lies in its ability to: 1) distill multi-source 2D editing priors into a single model to meet various types of 3D editing requirements: 2) accommodate inconsistencies across multiple views while enabling diverse inference; 3) generalize effectively and support real- time editing for in-domain scenarios; 4) offer enhanced interpretability and flexibility compared to traditional methods. We train and analyze VF-Editor multi-source data, and the experimental results validate the effectiveness of our method. Our main contributions are:
We propose VF-Editor, an innovative 3DGS editing framework that enables native editing in ford-fo manner by distilling 2D editing priors into 3D space. VF-Editor not only effectively addresses the long-standing issue of mueti-view inconsistency, but also significantly enhances the fiexibility and efficiency of the editing process.
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We design variation prodictor comprising variation field generation module and two parallel decoding functions, achieving computational complexity linearly proportional to the number of Gaussian primitives. Moreover, it accommodates sulti-source editing knowledge, effectively meeting diverse editing instructions.
We evaluate method under various settings and conduct comprehensive analyses; quali- tative and quantitative results demonstrate its effectiveness and broad application potential.
2 RELATED WORK
2D Editing Substantial progress has been made in 2D editing techniques. IP2P Brooks et al. (2023) generates a dataset with GPT-3 Brown et al. (2020) and P2P Hertz et al. (2022) and tine-tunes StablcDiffusion Rombach et al. 2022a) to perform text-guided editing. Subsequent works Zhang ct al. 2023); Sheynin et al. 2024); Zhao et al. (025a); Hui et al. (2024) achieve more flexible editing
results by collecting higher-quality and more diverse datasets. Additionally, some works Hertz et al.)
(2022) Parmar et al. (2023); Tumanyan et al. (202); Cao et al. (2023) explore using large-scale pre-trained text-to-image diffusion models achieve text-guided image-to-image transitions. They first invert the input image into the noise space and then perform denoising guided by the instruction. GLIDE Nichol et al. (2022) trains a noised CLIP model to guide diffusion models, while Textual Inversion Gal et al. (2022) optimizes special text embeddings representing the target concept. DDIM Inversion Song et al. (2021) Mokady et al. (2022; Wallace et al. (2023) maps real images to noised latents with acceptable error. More recent studies Duan et al. (2023): Qian et al. (2024): Wu & la Torre et al. (2024) propose various techniques to mitigate the accumulated error in inversion. Different editing strategies excel at handling distinct types of editing instructions, each possessing unique visual editing knowledge. We attempt to utilize VF-Editor to store multiple 2D editing knowledge within single model and construct 3D editing knowledge.
(2023); Spapeernnn--piggleeeee
3D Editing Leveraging advanced 2D editing tools, numerous 3D editing methods have emerged. DreamEditor Zhuang et al. (2023) and Vox-E et al. (2023) locate edit region through attention map and update the 3D data using Score Distillation Sampling (SDS) Poole et al. (2022). In contrast, Haqque et al. (203) integrates IP2P Brooks al. (2023) to guide edits while preserving the overall structural integrity via iterative dataset updating. GenN2N Liu et al.] (2024)
introduces edit codes to differentiate between various editing styles, enabling diverse inference from a single instruction. ViCA-NeRFi
& Wang 2023b) and DATENeRF RRojas et al. (0024)
leverage depth information enhance editing consistency across different views. More recently, numerous subsequent works Chen et al. (2024b): Fang et al. (2024a); Wu et al. (2024b); Wang al. (2024b) achieve substantial improvements in editing efficiency by replacing NeRF with 3DGS. DGE Chen et al. (2024a) and Free-Editor Karim et al. (2024) jointly edit different views with epipolar- based feature injection, significantly enhancing the editing quality. However, the aforementioned methods require the conversion of 3D data into 2D images during the editing process, which leads to inconsistencies across different views that cannot be fundamentally resolved. Shap-Editor Chen et al.) (2023) proposes latent-space editing method for NeRF, eliminating the need for 2D data during inference, but it is only capable of handling simple objects and lacks flexibility. 3DSceneEditor Yan et al. (2024) achieves object addition, deletion, or relocation by segmenting individual objects from the scene, while GSS Saroha et al. (2024) realizes 3D stylization by modifying the color coefficients of each primitive. However, their support for only single type of edit makes them inadequate for flexible editing. 3D-LATTE Parelli et al. (2025) and VoxHammer Li et al. (2025a) enable native 3D editing through the use of 3D diffusion models. However, their reliance on pretrained 3D generators inherently constrains the diversity of data distributions they can handle. In this paper, we aim to design universal, flexible, and rapid editing tool for 3D Gaussians.
METHOD
VF-Editor first trains the variation predictor Pe by distilling 2D editing knowledge, after which can perform real-time 3D editing across multiple instructions and scenes. We will first introduce the architecture of Pa. then explain its training and inference processes. The overall schematic of Ed-Edito is shown in Figure
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Knowledge Distillation
Variation Predictor (Pp)
DDIM Inforanee
Inpul (29)
Zumm Toltion
14
u,T,
M
X6i
Vaniation Fiekd
5,c
Editor
Random
Dillusion
6g
dim 1024
M 6p R, 5,C
6,
Score Distillaioon Sampling
Self-Antetion
Cross-Atemiin
6
8
8,
8,
Food Forward
Zero Linear
Editon
Ourput (X*)
Visunlization of the Variation
Loss Calcularion
up
Gradieen
Figure 2: Schematic of VF-Editor. Given 3D scene X and an editing instruction y, the variation predictor Pa generates variations which, when overlaid on the input scene X', produce the edited result X". VF-Editor trains Pa by distilling multi-source visual editing knowledge.
3.1 VARIATION PREDICTOR
Given the source 3D model X and an editing instruction y. Po can predict the variations A {6n, 8e, 8a, 8g, 6, Each of μ., a, e, and denotes the mean, scale, opacity, color, and rotation of 3D Gaussians, respectively. The edit result X is obtained by overlaying A onto x
Po (X' .,,e) A X" =" +
where E ~~ ~ I). Pa consists of a random tokenizer T and two key components: the variation field generation module M and of iterative parallel decoding functions J. Specifically, M is used to integrate the features X and and to generate the variation field, while F can rapidly extract A, for each 3D Gaussian from the variation field.
Random Tokenizer (7) To handle different numbers of 3D Gaussians, we first design a random tokenizer that transforms the 3D Gaussians into fixed number of tokens. Specifically, we randomly select 3D Gaussians from X as anchor points, with the remaining 3D Gaussians serving as data points. For each anchor point, we choose k-1 3D Gaussians from the data points that are spatially closest to form group, thereby decomposing X into 3D tokens, each of dimensionality k ff. where f is the dimension of Gaussian primitive. To facilitate subsequent computations, an MLP applied to each 3D token for dimensional transformation. Given the non-uniform spatial distribution of 3D Gaussians Feng et al. (2024b): Qu et al. (025);; |eng et (2024a), we adopt random sampling instead of the conventional farthest point sampling Qi al. 2017); Zhao al. (2021) commonly used in point cloud processing to select anchor points. This choice avoids the over-selection sparse edge primitives, leading more reasonable distribution of sampled anchor points.
Variation Field Generation Module (M) We hypothesize that a key factor contributing to multi- view inconsistency in 3D editing is the inherent probabilistic flow of existing 2D editing methods. This flow leads to high variability in 2D editing results, making precise control challenging Cail (2055) While restricting 2D editing diversity can significantly improve consistency across views, it comes at the cost of reduced diversity in the 3D editing results Chen & Wang (024).. To thoroughly avoid inconsistencies across views, we choose store the possible outcome of 2D editing into Po. that is, to preserve rather than limit the probabilistic flow during the distillation process. Specifically, we preserve the key noise that is strongly correlated with the probability flow (e.g.. the initial noise in DDIM inference [Song et al. (2021)). and concatenate it with the 3D tokens as inputs to M. This distillation idea of using key noise to retain probability flow has been proven to be effective in the field of diffusion acceleration Kang et al. (2024); Yin al, (2024); Lin et al. (2024). For details of the precise storage strategy of E, please refer to Section 3.2.2] As shown in Figure 2] M is constructed by stacking transformer blocks Vaswani et al. (2017). The instruction is encoded by the CLIP text encoder Radford et al. (2021) and then injected into the 3D tokens through cross-attention layers to form the variation field:
Sa = M(((X*)) eey))
where, @ denotes the concatenation operation, and fa represents the variation field.
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Iterative Parallel Decoding Function (F) Unlike common strategies employed in dynamic scene reconstruction Wu et al. (2024a); Cao & Johnson (2023), we do not convert the variation field into a triplane. Instead, we design the parallel decoding function decode the variation of each Gaussian primitive parallel. Specifically, we employ a transformer architecture without self-attention to represent this decoding function, taking all attribute values of each 3D Gaussian as input (query) and the variation field as the condition (key and value). The variation of each 3D Gaussian is decoded independently, allowing parallel processing to accelerate computation. To alleviate the issue of 3D Gaussians tending to alter appearance rather than move positions, we design an iterative decoding strategy by separating the mean y from other attributes:
[64] = Fi ( Xa Ta X2; Xe X,; fa), [6,, da, Sc, 6,1 F2 + d. X.S C.; fa). (3)
Given that the feature dimension of each 3D Gaussian is relatively low, the dimension of F can be correspondingly reduced, keeping the model lightweight. To stabilize the initial training process, insert "zero linear" (a linear layer initialized with zeros) layers at the end of F, ensuring the initial outputs of Pa are zero, thus providing more effective initial gradients for training.
3.2 KNOWLEDGE DISTILLATION
VF-Editor trains Po by distilling 2D editing knowledge. Since we have not introduced significant domain-specific designs into Po, it can theoretically store knowledge from multiple editing modalities:
Distill
Pe.
(4)
where, ετ; denotes different 2D editing models or strategies. To achieve knowledge distillation from multiple sources, we employ datasets from diverse domains and utilize various editing models to generate wide range of training samples. In the following, we list our collected data and then explain the different editing strategies applied.
3.2.1 3D DATA
Traditional 3D editing methods commonly utilize a fixed set of 3D-instruction pairs for testing, but these pairs are insufficient to provide adequate training data for Pa. So, we collect additional 3D data from various domains.
Reconstructed Objects (RObj): ShapeSplat Ma et al. (2024) contains 65K reconstructed objects from 87 unique categories, from which we select 662 high-quality 3D models across 32 categories as part of our training set. Most objects in ShapeSplat lack rich color variations; therefore, we primarily evaluate VF-Editor's capability for global style editing on this subset, such as instructions like "make its color look like rainbow."
Generated Objects (GObj): addition to the reconstructed objects, we enrich our training set by generating batch of 3D objects through generative models. Specifically, we first employ SD3 [sser ct al. (2024) to produce 500 cartoon character images with diverse appearances. These images are then fed into V3D Chen et al. (202cc) to obtain corresponding 3D objects, from which we select 319 high-quality generated 3D models. this subset, we primarily evaluate the capability of VF-Editor in local detail editing tasks, such "pul a party hat him."
Reconstructed Scenes (Scene): We also conduct training on several 3D scenes, specifically including three public 3D scenes (face, person.small, fangzhou_smal sunflowery and 115 generated street-view scenes Chung et al. (202)). Besides employing the editing instructions commonly utilized in previous works, we additionally train colorization. style transfer, and replacement instructions based on various 2D editing strategies for these scenes. Except for the generated scenes, all other scenes are reconstructed using Mini-splatting Fang & Wang (2024) to obtain the 3D Gaussians.
three private 3D scenes
(doll lal.ggggasccaa lecccallleee.. sunfl www.
3.2.2 EDITING STRATEGY
Different 2D editing models and editing strategies encapsulate distinct editing knowledge. We attempt to distill various types of such knowledge into Po and construct 3D editing knowledge accordingly.
DDIM Inference: Using a 2D editor to perform DDIM inference for obtaining edited images is a widely adopted 2D editing strategy. For the 3D data in RObj and GObj, utilize IP2P Brooks et al.
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(2023) to edit the rendered images and store the {initial noise) (instruction)- image) triplets. The deterministic nature of the DDIM sampler ensures a one-to-one correspondence between the incorporate the initial noise to preserve the probabilistic flow in IP2P. Considering that IP2P is not particularly adept at coloring tasks and to further validate the ability of VF-Editor in distilling multi-model knowledge, also employ CtrlColor Liang et al. (2024) for DDIM inference on the Scene dataset to collect triplets applicable to coloring tasks.
initial noise and the edited image Song et al. (202T). Therefore,
Diffusion Inversion:
Benefiting from the excellent properties of diffusion models, 2D editing can be achieved using only 2D generator. We adopt the DDPM inversion strategy proposed by Huberman Spiegelglas et al. (2024) to edit images and collect triplets suitable for replacement tasks. Due to the uncertain nature of the DDPM sampler HHo et al. (2020), storing all noise in the trajectory to triplets excessively redundant. To simplify computation, we retain only the noise sampled from the Gaussian distribution the final step of inversion as e. Although this approach does not ensure a one-to-one correspondence between and the edited result, find that due to the sparsity of the data, the model can still identify a degenerated probabilistic flow that leads to convergence.
Once sufficient triplets are collected through DDIM inference and diffusion inversion, we proceed to train Pa. X and E are used as inputs, y is used as a conditioning signal, and the edited image serves the target for the image rendered by the edited result X":
Cain Ex ..s [d(R(P&(X** 2,E) + 2²),2 Ex- [d(R(x"), >])]
(5)
where R represents differentiable rasterization rendering, and z" refer to the edited image. represents the distance metric funetion, and in VF-Editor, we use Mean Squared Error (MSE). For simplicity, we omit the camera parameters in Equation 5 The specific number of train- ing data collected through DDIM inference and Diffusion inversion is shown in Table Incor-
Table I: The quantity of various training data.
Type
3D Data Instruction 3D-Instruction Triplet
RObj
662
4
2,490
18,355
GObj
319
9
847
9,261
Scene
121
7
126
4950
1102
20
3,463
32566
porating into the input and supervising with only one view's editing result effectively mitigates the issue of multi-view inconsistency. Moreover, we observe that, given sufficient training data, Po is capable of inferring changes in novel views based on variations observed in the known ones.
Score Distillation Sampling (SDS): Besides performing data distillation by collecting triplets, we also attempt to utilize SDS Poole et al. (2022) to distill knowledge from the 2D editor:
Luss R(),, [w(t) 1, (( =]]
(6)
where w()) is weighting function that depends the timestep I, Ea is the noise predictor within the 2D editor Brooks et al. 2023), and 2 is the feature vector obtained by adding to latent embedding of R(x"). ). For Lads. triplets need not be collected offline, and the distillation process is unaffeccted by the quality of images generated by the 2D editor. However, since SDS only provides indirect validation rather than direct supervision, mode collapse often occurs in the editing results, leading to loss of diversity Wang et al. (2023): Zhuo et al.] (2024). Consequently, Cuts is not employed as the primary means of distillation. Nevertheless, the Pa trained using Las offers a robust baseline solution, with specific effects and discussions presented in Section 4.5 and 4.6
3.3 INFERENCE
After distillation, Po enables real-time editing for in-domain scenarios. Given the source 3D Gaussians and noise sampled from the standard Gaussian distribution, along with instruction, Po generates the corresponding variations. The edited result can be obtained by applying the variations to the source 3D Gaussians, and the entire editing process takes approximately 0.3 seconds.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Network Architecture: We set n and & to 256 and 128, respectively, and map the dimension of each token 4096 after MI.P projection. Noise sampled from the standard Gaussian distribution,

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mnake color look
GaussiaEEdttor
DGE
VT-Editon-
VF-Edilor-M
Put party hot on Rime
Gaussian
1-gs2gs
Vi-Editor-S
VF-Editor-M
Replace the sunflowr with rea ball"
Make full
1-gs2gs ChriColar
Hgs2gs-ClriColorr
VF-Editor-Nh
DGE-inversion
VF-Ediue-M
Figure 3: Qualitative comparison. VF-Editor achieves desired 3D editing with maximal preservation of original information. For video results, please see Demo.mp4 in the supplementary materials.
Table 2: Comparison with other editing methods. VF-Editor achieves the best performance.
RObj Cimm
GObj
Scene Ceim
Method
1AA1
TSt
Gcont
ISt
Caim
Ccont
TS1
Ccontt
I-gs2gs
3.86
0.193
0.659
3.51
0.176
0.863
3.37
0.112
0.872
4.74
GaussianEditor
3.25
0.261
0.736
3.19
0.194
0.865
3.65
0.107
0.897
4.89
DGE
3.10
0.252
0.752
2.95
0.191
0.879
3.54
0.093
0.894
5.05
VF-Editor-M
4.32
0.296
0.763
4.15
0,206
0,875
4.06
0.127
0.903
5.24
VF-Editor-S
4.31
0.292
0.767
4.24
0.227
0.881
4.04
0.132
0.895
5.19
with size of 64 64, is reshaped and concatenated with 3D tokens. Editing instructions are encoded by CLIP and fed into the cross-attention layer of M. We do impose any constraints regularization on 3D Gaussian attributes within F to ensure the flexibility of the generated variations.
optimization objectives, Lain and Ludss for Po- Regarding Ldin. the batch size is set to 16, and the entire training process takes 52 hours on A100 GPUs. For Lads, the batch size is
Training: In Section 3.2.2
introduce
8*4, and the training process takes 90 hours on A100 GPU, Since Leds tends to cause the output of Pa to collapse to a unique solution, we focus on Lain. for our investigations in Section 4.2 and 4.3
To facilitate analysis, the sh degree of 3D Gaussians is set
Metries: Following Haque et al. (2023); Chen et al. (2024a), we compute the CLIP Text-Image Direction Similarity (Cssm) and CLIP Direction Consistency (Ccon). Additionally, we calculate the Inception Score (TS) and conduct the Image Aesthetics Assessment (TAA) using Yi et al. (2023) to
further evaluate the quality and diversity.
4.2 COMPARISON
We compare VF-Editor with three 3DGS editing methods, Instruct-gs2gs (I-gs2gs) Vachha & Haque (2024)) GaussianEditor Chen al. (0224b, and DGE Chen et al. (2024a). Given the domain gap in the 3D datasets we collected, we train two versions of Vd-Editor: VF-Editor-S, trained on single-domain dataset, and VF-Editor-M, trained on multi-domain datasets.
we demonstrate visual comparisons with the baseline. Previous works on 3D editing typically utilize instructions with abundant prior information when evaluating their effects. However, as observed in the first row of Figure 3 these methods nearly fail to function when faced with instructions lacking prior information, even when the editing instructions are very simple. Regarding the coloring experiment, for fair comparison, & Haque 2024) with CtrlColor Liang et al. (2024). It is apparent that the strategy of iteratively substituting 2D images is inadequate for the coloring task. In the replacement experiment, the same attention injection strategy as in DGE Chen et al.) (2024a) to enhance the denoising process during diffusion inversion. This allows obtain edited images from various views, which serve
In Figure 3
replace IP2P Brooks et al. (2023) in I-gs2gs) Vachha
employ
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Defommation
Reshupe
Pur party
Direct Decoding
Heeative Decoding
"Moke colorfut"
Reference Image
MEP
Viive
Decoding
lterative Decoding
Panllel Decoding Function
Triplane
Figure 4: Visualization the ablation study of iterative decoding. Direct decoding impairs the model's 's predictive capability regarding the posi- tional changes of the 3D Gaussian.
Figure Visualization of the ablation study of parallel decoding function. (Left) The triplane de- coding strategy used for ablation. (Right) Display of the reference image and editing results.
Oblers
Progressive Editing
Free Mixing
"ake look like Fauvion patttting
Make Run sumpleines
Make him lough
Gine kim austache
Variation Fusson
Figure 6: Schematic of the flexible editing process. The operation demonstrated in Free Mixing involves blending two set variations along the x-axis with different weights. In practical uses, the range and intensity of variations can be adjusted to personal needs to control the results.
reconstructed data for DGE. We observe that although the carefully designed attention injection strategy significantly improves consistency across different views, discrepancies in the size of the replacement target (red ball) under various views still persist. These discrepancies led distortions in the final reconstructed ball.
Table displays a quantitative comparison between VF-Editor and baselines. Due the extensive time required for editing by baselines, we randomly select 100 3D-instruccion pairs for testing in GObj and RObj, respectively. Csim and Coon than I-gs2gs, yet its IS is considerably lower. We speculate that this is due to the consistency constraints across different views, which reduce the diversity of the results. contrast, VF-Editor, by accommodating rather than restricting diversity, significantly promotes diversity while ensuring editing quality. Furthermore, attaining the highest IAA indicates that our method's 's editing results
evident that on RObj and GObj. DGE achieves significantly higher
aligned with human preferences. Additionally, find that the convergence process of Po is hardly affected when facing multi-domain data, demonstrating its universality. In subsequent experiments, train Pg exclusively on multi-domain data.
4.3 ABLATION STUDY
We design a set of iterative parallel decoding functions to decode the variation, and now we conduct ablation experiments to verify their ef- fectiveness. Firstly, modify iterative decod- ing to direct decoding, with results shown in Table and coding fails to effectively achieve the expected goals when dealing with instructions that require
Table 3: Quantitative results of the ablation experi- ments the iterative parallel decoding functions.
Method
ISt
Caimt
Ccont
IAA
Direct Decoding
4.71
0.254
0.801
5.21
and Figure 4 It is evident that direct de-
Triplane
4.57
0.246
0.782
5.09
VF-Editor-M
4.66
0.259
0.803
5.22
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Test Unseen Sample
the Unseen Instruction
Seen Samples
Unseen Samples
Make it look like Fauvism painting" (Caia)
Seen Instruction
Unseen Instruction
"Turn him into the Volkien (Egas)
"Turn him into clown""
"Apply clown maksup his face""
Figure 7: Experimental results on the unseen data. It can be observed that the Pa trained within VF-Editor exhibits a certain degree of generalization capability, demonstrating its potential.
displacement of 3D Gaussians (although the quantitative metrics appear relatively unchanged). In contrast, the results for editing instructions that only modify the appearance of 3D Gaussians are almost unaffected. We hypothesize this is primarily due to the intercoupling of various unstructured attributes within the 3D Gaussians. If all attributes are changed simultaneously, the model tends to alter the appearance the 3D Gaussians to meet demands rather than moving them.
Secondly, we replace the parallel decoding function with the triplane for further experimentation, and the corresponding results are presented in Table Bad Figure To visually observe the differences, we specifically select triplet from the training set and use the stored noise as input to generate outputs corresponding to the reference edited image. It evident that using the triplane represent the variation field results in less distinct boundaries between different regions in the output, creating an overall blurred state. hypothesize that this is primarily because 3D Gaussians that are spatially proximate tend extract highly similar features from the triplanes, which are then decoded into similar variations. This issue becomes increasingly severe as the number 3D Gaussians in the scene increases. Our parallel decoding function does not impose any prior constraints between adjacent 3D) Gaussians, allowing for the learning of more refined variations.
4.4 FLEXIBILITY OF THE EDITING PROCESS
Compared to traditional methods, one advantage of VF-Editor is its ability to achieve flexible editing effects. Thanks the interpretability of the variations, we can manipulate the variations in various ways to achieve the desired effects, such as merging variations, controlling the intensity of variation, and selecting local variations. As shown Figure 6 we perform various manipulations on the variations generated by Pu. resulting in diverse results: 1) Different editing strengths can be achieved by scaling the variations; 2) variations generated from different instructions can be combined to produce new editing results; 3) users can freely give different editing effects to different areas.
4.5 GENERALIZATION CAPABILITIES
To verify that Pe possesses generalization pabilities rather than merely memorizing the distilled knowledge of 2D editing, conduct experiments on the unseen sample and instruc- tion. We collect new test comprising 50 reconstructed objects, 50 generated objects and 10 generated scenes. As shown in Figure 7 we test the generalization of Po obtained through distillation using Edin and Luds, respectively. The corresponding quantitative results are presented in Table It can be seen that Po demonstrates commendable generalization capabilities. Furthermore, we observe that Pa is capable of predicting reasonable editing results when the test instruction is semantically similar to one present in the training set.
Table 4: Quantitative results on the training and test data. There is slight decline in the quality of the editing results on the test set, but it still remains good level.
Dataset
1St
Caiit
Coo't
IAAT
Training Sct Test Set
4.69
0.268
0.795
5.24
4.56
0.241
0.790
5.16
4.6 DISCUSSION AND PROSPECTS
We observe that using Leds alone causes the model to collapse to a single solution per instruction, while naively combining it with Lain leads divergence. We attribute this to the nature of SDS,

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Published as a conference paper at ICLR 2026
which provides implicit verification rather than explicit supervision. Nonetheless, Lade allows Pa to learn robust baseline with good generalization, without requiring offline triplet collection. Effectively integrating Ldin. and Lids may further enhance VF-Editor's capabilities. Additionally, we collect 3,348 3DD-instruction pairs to train Po- Although the model generalizes well to unseen in-domain data, it does not yet support out-of-domain editing. In future work, we aim expand the knowledge coverage of Pa efficiently. Lastly, while VF-Editor demonstrates effectiveness in object addition, relocating existing primitives may occasionally have minor impact on surrounding regions. Introducing a dedicated primitive generation branch may further improve performance.
CONCLUSION
We present VF-Editor, a novel framework for flexible editing of 3D Gaussians by predicting spatial and appearance variations. It distills multi-source editing knowledge into a unified variation predictor, enabling precise editing across multiple scenes and instructions. To support efficient variation prediction, we introduce variation field generation module and set of iterative parallel decoding functions. VF-Editor offers hopeful direction for real-time 3D editing in open-vocabulary settings.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China under Grant 42394060 and 42394064.
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APPENDIX
A OVERVIEW OF THE APPENDIX
Here, we supplement some detailed information that is not explained in the main but may be of interest to the reader, including:
App.
B
Some preliminary knowledge;
App.
Rules for drawing 3D variations;
App.
More training details;
App.
All instructions used our training;
App.
More details about our custom dataset;
App.
Additional experimental results;
App.
Use of LLMs;
App.
Boundary conditions of generalization;
App.
In-depth analysis of some structural designs;
App.
Fine-tuning with fewer triples;
App.
Further evaluation;
App.
Runtime comparison.
In addition, we provide the Demo Demo.mp4 in the supplementary materials and simultancously up- load source code to the anonymous repository: htttps://anonnnn: 4open. wew.ecciieenceeeeeeeee VF-Editor
Ethics Statement This research uses only publicly available datasets that contain no personally identifiable or sensitive information. No human or animal subjects are involved. All experiments are conducted in accordance with institutional and conference ethical guidelines.
Reproducibility Statement We provide detailed descriptions of the model architecture and hyperpa- rameter settings in Section 4.1 and App. D Our code will be released to facilitate reproducibility.
B PRELIMINARY KNOWLEDGE
B.1 3D GAUSSIAN SPLATTING
3D Gaussian Splatting has revolutionized the field of 3D representation, employing anisotropic 3D Gaussians for efficient and intricate radiance fields modeling Kerbl et al. (2023). It explicilly represents the radiance field as a mixture Gaussians p = {(4s, 0šs, Cr Tr) r:) where μ4 E R the mean, a is the opacity, s; R is the scale matrix, G; R is the view-dependent RGB color computed from Spherical Harmonic coefficients, r; E R3x3 is the rotation matrix. With & denoting the queried point, and Σ, denoting the Gaussian's covariance matrix calculated as Σ, T,85s the Gaussian function is defined as:
Gi(r) = - (a-we
In the rasterization process, the 3D Gaussians are splatted screen-space 2D Gaussians following EWA Splatting Zwicker et al. (2001). Denoting the given screen point, Σ/ as the covariance matrix of the 2D Gaussian, the sereen-space Gaussian function (u) e (u- x (u-m) The splatted Gaussians are blended following the volumetric rendering model formulated as:
C= Σ c. (1- IEN
(8)
where o, = (c:g:() for the given screen point u. The process is implemented with a blsed CUDA rasterizer, which allows real-time differentiable rendering of 3D Gaussian Splatting.
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B.2 DDIM INFERENCE
Denoising Diffusion Implicit Models (DDIM) Song et al. 2021) reformulate the stochastic reverse process of DDPM et (2020) into deterministic non-Markovian mapping, enabling high-quality samples with far fewer steps. Let
a = II - 3.), ==1
ap
(9)
and (o( () denote the model's s predicted noise. The DDIM update from 24 to 2t-1 then given by
3t vI
die (o 1)
T4
+ V --- (x)
(10)
Vāe
Crucially, by setting the variance of each transition to zero, DDIM produces fully deterministic trajectory. During inference, can further "skip" intermediate timesteps- selecting only S < T of the original 7 steps-whie typically yields perceptually comparable images with an order-of- magnitude reduction in sampling cost. The determinism of DDIM not only makes sample generation reproducible but also facilitates smooth interpolation between latent representations.
B.3 DIFFUSION INVERSION
In the original DDPM framework Ho al. (2020), the forward noising process is defined by
g(<, 1x-1) = W(n v1 3r 11-1, 3,1),
(11)
which admits the closed-form marginal
ax mo) = Muss van wo, (1-ai)1) ā. II (1-3)
(12)
i=1
To invert a given image zo back into its noise latent Ir, one simply "runs" this forward process: for 1 1, 1,...,?. T,
Zt Vā zo + - N...1.
(13)
We obtain the ground truth latent different noise levels T[o:T With these noised latents, we can correct the trajectory of the backward process by aligning the predicted previous sample ž.-1 with the ground truth previous sample It-1 to obtain the variational noise in each DDIM step:
V Va--1 Vā
VI āi E v.t) / āt-1 - of EZ v.
-1-1
(14)
Tt Tz-1 &
1i--a-11((10 az / 0 for all / to make the sampling process align with the original DDPM, we can obtain edited latents s 2/oor with the following equations
By setting (11 0t
1 FT
1/1
Va-- (z)
-2--v1- c. Esla 3 t)) v 1 es(2), v/ + E.
(15)
Vāa
under new conditioning y' (e.g. text, masks, or style codes), enabling precise, content-preservii edits of the input image.
B.4 SCORE DISTILLATION SAMPLING
Score Distillation Sampling (SDS) Poole et al. (2022 is technique that turns pretrained diffusion model into a powerful "teacher" for training a separate, fully differentiable network- often used to represent 3D scenes or other complex modalities. Instead of learning from raw data, the student network learns to match the denoising behavior of the diffusion model: at each noise level, it adjusts its parameters so that its predictions of what clean sample should look like align with those
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of the frozen diffusion teacher (as shown in Equation 6). By doing this across all noise scales, SDS effectively transfers the rich generative knowledge embedded in the diffusion model-without ever retraining it enabling the student network to produce samples that the teacher considers highly plausible. This approach unlocks efficient, high-fidelity synthesis in new domains (e.g., text-to-3D) by distilling the diffusion model's learned prior into specialized downstream network.
C 3D VARIATION VISUALIZATION
For the purpose of observation, we present the 2D projections of the 3D variations in the main text. The projection rules for different attributes are as follows:
1) Spatial Position We project the original and edited positions of each 3D Gaussian onto a 2D plane and connect these two points with a line segment to represent the change in spatial position. The opacity of the line segment indicates the magnitude of the displacement, with higher opacity corresponding to larger displacement. The color of the line segment represents the direction of the displacement. We uni- the
"Put party hat on her"
Variation Predictor ~ 0.3s
formly set three standard direction vectors plane, each corresponding to pure red, pure green, and pure blue, and then interpolate to obtain the color corresponding to a specific direction.
8
8a
8,
8c
8,
Visualization of the Variation
2) Opacity a: We project the original position of each 3D Gaussian onto 2D plane and draw a circle the projection point represent the change in opacity. If the opacity increases, the circle is colored red; if the opacity decreases, the circle is colored blue; if the change in opacity small, the circle appears close to white. As shown in Figure to form paty hat, large number of 3D Gaussians around the head of the character have increased opacity.
Figure 8: 2D visualization of the 3D variation.
3) Scaling Coefficient s: Similar to opacity, we use red to represent an increase in the scaling coefficient and blue to represent a decrease. For convenience in presentation, we take the average of the scaling coefficients along the y. and axes before proceeding with subsequent calculations.
4) Color c: We present the variation of each 3D Gaussian's color rather than its resultant quantity. Specifically, project the original position of each 3D Gaussian onto 2D plane and then draw a circle at the projected point to represent the color variation. The opacity of the circle is related to the magnitude of the color change; the less the color variation, the more transparent the circle. Additionally, we scale and translate the RGB variation to the range of 0 to to represent the color of the circle. As shown in Figure X the colors of large number of 3D Gaussians at the head transition to red, forming paty_hat.
5) Rotation Quaternions We find that simple projection strategies fail intuitively represent the variation process of rotation quaternions in 2D plane. Therefore, we do not display this process in the main text. As shown in Figure 88 we map the first component of the change of quaternion to opacity and the remaining three components to color.
D MORE TRAINING DETAILS
Here, we provide additional details regarding the training of VF-Editor-M. VF-Editor-S, in compari- son, only differs by a reduced number of training epochs without any other modifications. For Lui-- the model trained on the collected triplets for 600 epochs. The initial learning rate is set to le-4 and halves every 100 epochs. The AdamW Zhou et al. (2024) optimizer is used, with the weight decay set to 5e-3. For Lyss. the model is trained on all 3D-instruction pairs for 500 epochs. The initial learning rate is set le-4 and halves every 100 epochs. The AdamW optimizer is also used, with the weight decay set to 5e-3. For IP2P. the guidance scale and condition scale are set to 7.5 and 1.5, respectively. The noise schedule time step / decreases linearly the number of epochs increases.
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Table 5: 3D-instruction pairs used in the collection of triplets.
Type
Instruction
make it look like it's covered in moss
make its color look like rainbow
RObj
make its color look like gold
make it look wooden
Turn him into a clown
Put party hat him
Turn him into the Tolkien Elf
GObj
Turn his hair orange
Turn his clothes blue
Turn his pants green
Put party hat on her
Turn her into the Tolkien Elf
Turn her into a clown
Make it look like Van Gogh painting
Make him bronze statue
Make look like Fauvism painting
Scene
Give him red hair
Make him marble statue
Replace the sunflower with a red ball Make it colorful
We leverage an aesthetic scorer Yi al. (2023) to filter out low-quality edited results during the triplet collection process. Specifically, for each instruction targeting particular subset, after gathering batch of triplets, we use Yi et al. (2023) to evaluate all edited images and retain only those triplets whose edited images rank within the top 50% of scores.
For the Variation Field Generation Module M, we set the number of attention heads to 16, with a head dimension of 64. For the Iterative Parallel Decoding Function F., use single attention head with a head dimension of 64. The output dimensions of Fi and F2 are set to 3 and 11, respectively. In the collected triplets, the noise has shape of 1 4 64 64, and the image has shape of 1 3 512 * 512. To strike balance between efficiency and visual quality, we set the number of sampling steps the diffusion model 50. During data collection, we employed multiple diffusion models, including IP2P Brooks et al. (2023), CtrlColor Liang et al. (2024), and Stable Diffusion 2.1 (Inversion) Rombach et al. (2022a). When computing Lade- we set guidance_scat condition scale to 6.5 and 3.5, respectively, and linearly decay the time step f from 800 to 100 over the course of training.
and
E ALL INSTRUCTIONS
We collect 32,566 triplets through DDIM inference and Diffusion inversion, employing InsIntruccionn pairs as shown in Table (able during the collection process. Additionally, we design more instructions while using Cea, train Po, illustrated in Table
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Table 6: 3D-instruction pairs used during training with Cads.
Type
Instruction
Make him wear fashion sunglasses
Turn him into the Tolkien Elf
GObj
Make her wear fashion sunglasses
Turn her into the Tolkien Elf
Make him wear fashion sunglasses
Give him a mustache
Turn him into the Tolkien Elf
Seene
Make him laugh
What would he look like as a bearded man
Give him cowboy hat
Figure 9: Some images generated by SD3.
F CUSTOM DATASET
To train Po. we collect a set of 3D data. The number of 3D Gaussians contained in each custom data ranges approximately from 5,000 to 50,000. Here, we describe the data from the GObj subset and part of the data from the Scene subset.
Generated Objects (GObj): First, we utilize GPT-4 al. (2033 generate instructions describing cartoon characters with different appearances. These instructions are then input into SD3 Esser (2024) to generate the corresponding images. Finally, we use the image-to-3D generation model V3D Chen et al. (2024c) to convert these images into 3DGS. A portion of the images generated by SD3 is shown Figure We will release all images and 3DGS data publicly.
Reconstructed Scenes (Scene): We construct three sets of custom scene data to further evaluate the generalization capability VF-Editor. These include: 1) doll grayscales We initially capture 203 images of a dinosaur doll and crop their dimensions to 512x512. Then, we use MOLLMAP|Schon berger & Frahm (0166) to compute the pose each image and convert all images to grayscale. We employ Mini-splatting Fang & Wang (024) for the reconstruction of these images, with the sh degree set to 0 and the samplingfact actor set 0.1. 2)
This scene contains 197 training images, with the remaining processing steps being the same as those in dol doll.grayscee
3) funflower: This scene is the color version of sun flower grayscale. Some of the training images from these three scenes are shown in Figure 10
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doll grayscale
hullower_grayscanl
sunflower
Figure 10: Some images from the custom scene data.
"make its color look like rainbow"
'make its color look like rainbow"
"make its color look like rainbow"
"make its color look like gold" "
"Turn him into the Tolkien Elf"
"Turn him into clown" "
Figure 11: Interpolation of variations.
G ADDITIONAL EXPERIMENTAL RESULTS
G.1 INTERPOLATION OF VARIATIONS
In the main text, provide the editing results obtained after blending the variations. Here, supplement with more detailed interpolation results of the variations to validate the smoothness of the generated variations. The experimental results shown in Figure II where it can be observed that interpolating between the two variations with different weights produces natural intermediate results. In Demo.mp4, provide additional editing results.
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make its color look like rainbow"
Inferencel
Inference2
Inference3
'make color look gold"
"covered
'inake look wooden"
Tuurn her into clown"
Inferencel
Inference2
wear
Turn the Tolkien Elf
fashion sunglasses
fasshion sunglasses
the Tolkion Elj"
Figure 12: Results of multiple rounds of editing on the same sample.
'Put a party hat him
GaussianEditor- Wonder3d Wooderrd automatic
GaussianEditor dindsodnEddto--Woddd3dddd VF-Editor-Trellis manual
manual
Figure 13: Comparison with GaussianEditor.
G.2 MULTIPLE INFERENCES
In Figure 12 present the results of multiple inference specific 3D model under identical and varying instructions to evaluate the diversity and stability of our method's editing outputs.
G.3 COMPARISON WITH GAUSSIANEDITOR
GaussianEditor Chen et al. 22224) proposes the optional integration of an external 3D generator Long et al. (2024) to assist with object insertion tasks. However, considering that none of the other baselines utilize such auxiliary generators, we adopt the pure GaussianEditor algorithm for fair comparison 3 and Table Here, we additionally present editing results that incorporate the auxiliary generator, as illustrated in Figure [3 can be observed that relying solely on the automatic pipeline of Editor struggles to produce semantically reasonable object insertions. When the object location manually specified, the default holistic optimization often compromises the appearance of the original scene. In contrast, also present results using the external Trellis Xiang et al.) (2025) module, where the inserted object manually placed a semantically coherent location, serving as visual reference for comparison.
in Figure
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Turn the bear grizzly bear
Make him look like Spiderman
Make it rasty
Figure 14: Fine-tuning results on unseen instructions.
Table 7: The results of different grouping strategies.
IS f
Csim f
Con
IAA f
Random sampling
4.24
0,227
0.881
5.27
Farthest point sampling
4.05
0.196
0.973
5.20
Spatial-color k-means
4.21
0.227
0.879
5.22
G.4 FINE-TUNING RESULTS
Figure 14 presents the results of model fine-tuning three novel instructions encountered during pretraining. For each out-of-domain instruction, fine-tuning for 10-20 hours enables the model to acquire the desired new editing capability without compromising its original performance. After fine-tuning, similar editing operations can be performed in under 0.3 seconds.
G.5 MORE ABLATION EXPERIMENTS
To further investigate the impact of the tokenizer on model performance, we conduct two ablation studies the GObj subset.
(1) Changing the grouping strategy in the tokenizer.
We implemented two new grouping methods: (i) selecting anchor points using farthest point sam- pling (FPS), technique commonly used in point-cloud models, followed by clustering; and (ii) spatial-color k-means clustering, where both the color coefficients and the positions of the Gaussians are equally weighted (0.5). The experimental results are shown in table 7 We observe that FPS leads the weakest performance, while the remaining two methods yield comparable results.
We hypothesize that this is mainly because in 3DGS scenes, the Gaussian primitives are typically denser in central regions with complex structures and sparser near scene boundaries. Due to its distance-aware mechanism, FPS tends to select anchor points along the scene's ' global contour. As result, sparse boundary primitives are frequently chosen as anchors, leading to a relatively fixed and uneven distribution of anchor points. In contrast, uniform (random) sampling does not consider spatial distances and therefore selects anchors more frequently from the dense central regions, resulting in more diverse distribution of anchor points. In addition, although the performance of random sampling and spatial-colos k-means clustering is similar, random sampling followed by clustering requires substantially less computation.
(2) Further analysis of group size.
We then fix the anchor sampling strategy to random sampling and vary the group size (adjusting the number of groups proportionally) to examine its influence on model performance. As shown in table & within reasonable range, different group sizes have only minor impact performance.
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Table The results of the ablation study on group size.
Group size
IS 1
Csim
Ccon f
TAA
64
4.24
0.228
0.879
5.27
128
4.24
0.227
0.881
5.27
192
4.22
0.223
0.876
5.24
Training
Test Sample
Test Sample
Test Sample
Instruction: "make color like gold"
Figure 15: Experimental results of fixing the instruction while changing the edited 3D model.
H USE OF LLMS
In manuscript, employed the LLM perform grammatical checks the written content. Furthermore, we utilized GPT-4 to generate the image synthesis prompts required for our study, as detailed in Section F
BOUNDARY CONDITIONS OF GENERALIZATION
Here, we investigate the boundary conditions of generalization. We conduct two sets of experiments: (I) fixing the instruction while changing the edited 3D model, and (2) fixing the 3D model while varying the semantics of the instruction. As shown in Figure 15 and 16 observe the following: (1) For general- penerrl-purpose
purpose instructions, the model is able to produce reasonable editing results even on
out-of-domain data. (2) As the test instruction gradually deviates semantically from the original one (e.g., "make its color look like a rainbow")) the model's ability to follow the instruction also degrades. In Figure
[6] when the test instruction remains semantically related the training instructions- such as "apply vivid spectrum of colors" -he editing outputs remain reasonable and exhibit distribution distinct from the outputs obtained using the original training instruction. However, when the semantics of the test instruction differ substantially from those seen during training- for instance, "make the lighting dramatic and moody" the model's behavior begins to diverge from the instruction. We suspect this occurs because the model lacks the ability to generalize entirely unseen semantic concepts that were never learned during training.
J IN-DEPTH ANALYSIS OF SOME STRUCTURAL DESIGNS
Here, we provide additional in-depth analyses related to the structural design.
(1) Why use transformer blocks with self-attention in the Variation Field Generation Module
(M), but without self-attention in the Iterative Parallel Decoding Function (F))
Our Variation Predictor can be viewed as a structured factorization of the 3D editing operator:
M is responsible for extracting global correlations from the input scene and generating global variation field.
F then performs lightweight per-primitive update decoding for each Gaussian primitive.
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Input
"make its color look like
"apply a vivid spectrum of colors"
"boost the saturation and add vibrant gradients
"make the lighting dramatic and moody"
rainbow"
Figure 16: Experimental results fixing the 3D model while varying the semantics of the instruction.
This factorization analogous to the common mncoee-llatete -ecoode structure in representation learning: the variation field SA serves global, low-dimensional, cross-primitive latent basis, while F are only responsible for conditional decoding (conditional readout).
Under this design:
Why M needs self-attention. The variation field must aggregate structural information from the entire scene. Given the presence a tokenizer, self-attention is the most effective global information aggregation mechanism, it can capture cross prssmstviiiiiee correlations with a controllable computational budget.
Why FF intentionally avoids self-attention. Once a global variation field is available, introducing self-attention inside F would force all Gaussian primitives to interact pairwise. This would increase the complexity from O to O(N²), which becomes prohibitively expensive for large-scale scenes. At the same time, we would like each primitive's 's update to remain conditionally independent given the global field so that decoding can be fully parallelized. Therefore, FF uses only cross- cross-attention, conditioning variation field. This keeps the decoding complexity linear in N, which is crucial for large scenes.
the same shared
(2) Why do separate Fi (mean) from F2 (csabe/pccity/oooorr/ottaaa)"""
This design motivated by the analytical structure of the 3DGS render (Eq, 7 in App. B}. The rendered color C (u, 8, r) admits the following Jacobian under a first-order Taylor expansion with respect to the parameters:
[Jm JuA] JAu
Jo
A (s,
JAA]
The non-zero off-diagonal blocks indicate that the geometric position j and the appearance attributes are intercoupled:
Changing μ alters the distribution of primitives in screen space, which turn affects the effective contribution of their opacity/scale/colo..
Changing appearance attributes likewise affects how gradients propagate along geometric directions.
Existing 3DGS reconstruction works Charatan et al. 2024): Lan et al. (2025) have also adopted similar strategies to avoid letting the appearance parameters (scale, color, opacity) "absorb the emror.
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'nake color bright turqusise"
Figure 17: Fine-tuning with fewer triples.
Against this background, our Fi/F2 design can be interpreted approximate block-coordinate update/ block-diagonaliiztion of the Jacobian:
Fi first predicts the geometric position 4 preventing geometric gradients from being overwhelmed distorted by appearance parameters.
F2 then updates the appearance attributes after the geometry has been stabilized.
This structure alleviates the optimization instability caused by the strong coupling between geometry and appearance, enabling the system to handle both geometric edits and appearance edits more reliably.
As shown in Figure 4 and Table
Directly decoding all attributes jointly is prone to failure under displacement- -ttpeeeettttteeeeeeeeee edits.
Our iterative decoding remains stable across almost all types of edits.
K
FINE-TUNING WITH FEWER TRIPLES
foreseeable that once the model has learned a particular category edit, acquiring unseen concepts within the same category should become substantially easier. To verify this intuition, conduct simple experiment. We introduce a new instruction, "make its color bright turquoise" containing the unseen concept "bright turquoise". Following the procedure described Section 3.2.2 we collect only 25 triplets for this new instruction and fine-tune the model. As shown in Figure 17 the model successfully learn the new concept with ease. This demonstrates that for edits within the same category, even novel concepts can be learned with very few training examples. For instance, once the model has learned "make its color look like gold," learning "make color bright turquoise" becomes much easier.
L FURTHER EVALUATION
To further validate the effectiveness of our method, we evaluate the editing results from multiple perspectives.
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Table 9; Consistency scores across views computed using GPT-5,
Method GPT scoreedit
1-gs2gs
6.3
GaussianEditor
6.7
DGE
7.5
VF-Editor-M
8.3
Table 10: The results of the user study.
Method
Aesthetic Quality 1
Instruction F Following
Faithfulness
T-gs2gs
4.7
5.1
6.4
GaussianFditor
5.0
6.0
5.9
DGE
6.4
5.9
7.5
VF-Editor-M
9.3
(1) 3D Geometry Preservation. To assess whether our edits affect the underlying 3D structure for non-geometric edits, we simplify the 3DGS representation into point clouds and compute the Chamfer Distance and F-score (T 0.01) between the point clouds before and after applying two representative -on-gemmetric edits ("make its color look like rainbow" and "make its color look like gold"). Across 100 trials, the average results are: Chamfer Distance 2.4273e-05, F-score 0.9730. These outcomes indicate that the geometry of the 3D model remains almost entirely intact.
(2) Multi-view Consistency Checks. To provide a more comprehensive assessment view con- sistency, introduce feed the resulting images into GPT-5, using a carefully designed prompt to score their cross-view consistency. The average scores the 11 test cases reported in table 9 The full prompt:
metric called, GPT scorCedit- We render some edited views and
You are expert 3D graphics evaluator. You will be given several rendered images same object from different viewpoints. Your task is to assess multi-view consistency: how well these images appear to come from single coherent 3D model.
Instructions:
Evaluate whether the object's shape, proportions, ge- ometry, materials, textures, and global structure remain consistent across all views. 2. Ignore Rendering Artifacts: Do not consider lighting, shadows. background, camera angle mismatch, or rendering noise-ont evaluate the intrinsic object identity and structural consistency.
1. Consistency Definition:
3. Scoring Rule (0-10):
O: extremely inconsistent; views clearly depict different objects
moderately consistent; some mismatches exist but the main structure aligns
10: perfectly consistent; all views depict the same coherent 3D model
4. Output Format: Respond only with single number from 10, representing
your consistency score.
Now evaluate the consistency of the provided images.
(3) User Study. We also conduct user study using a questionnaire- evaluation. For each question, volunteers are shown videos rendered from the edited results of different methods, along with the corresponding editing instruction, all in randomized order. Participants rated each result in terms of Aesthetic Quality, Instruction Following, and Faithfulness to the Original 3D Model, using scale from (worst) to 10 (best). We collect scores from 11 volunteers across sets of data, and the averaged results reported in the table T0
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Table 11: The editing times of different methods.
Time (8)
Training Set
Test Set
1-gs2gs
275
275
Gaussiandditor
463
463
DGE
210
210
VF-Editor-M
216
0.3
M RUNTIME COMPARISON
Here, we compare the editing times of different methods. Since all existing baselines perform optimization on per-scene basis, we report both the amortized editing time the training set and the editing time on the test set separately for a comprehensive comparison, as shown in table [1
28


Reviews:
[{"review_id": "uDIanNPaKr", "reviewer": "Reviewer_Ze3B", "summary": "The paper presents VF-Editor, a native, feedforward approach for direct 3D Gaussian Splatting (3DGS) editing by distilling multi-source 2D editing knowledge into a unified variation predictor network. Instead of using indirect, view-by-view projection-and-reconstruction pipelines prone to cross-view inconsistencies, VF-Editor predicts per-primitive attribute variations (position, scale, opacity, color, rotation) using learned features and incorporates probabilistic flows. The authors introduce a variation field generation module and parallel decoding functions, with extensive experiments demonstrating improved flexibility, consistency, and real-time operation across editing tasks. The paper also provides ablation studies and assesses generalization to unseen data and instructions.", "questions": "See the questions listed above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper responds to a well-recognized limitation in 3DGS editing: the cross-view inconsistencies inherent to indirect, 2D-edit-then-project pipelines. The authors' framing is accurate and well-motivated, making a convincing case for the need for direct, native 3D editing.\n2. The feedforward variation predictor architecture is novel. The random tokenizer, transformer-based variation field generator, and parallel iterative decoding functions offer a clear path to efficient, scalable editing. The distinction between mean and other attributes in decoding further reflects architectural care to handle attribute coupling.\n3. VF-Editor's distillation pipeline can combine knowledge from diverse 2D editors, such as IP2P for DDIM-based edits and CtrlColor for style/color tasks, with training informed by different datasets. This unification of editing capabilities is practically impactful.\n4. The experiments cover a range of editing instructions, including both qualitative and quantitative reporting (Table 2, Table 4), and address both baseline comparisons and ablations. Figure 3 demonstrates per-instruction failure modes for baselines, while Figures 4–6 visualize the contribution of each architectural choice and VF-Editor's flexibility. Figure 6, in particular, makes the interpretability and composability of the approach tangible, which is a strong point.", "weaknesses": "1. Although the Related Work section is relatively comprehensive for 3DGS and 2D distillation methods, several highly pertinent and recent methods are missing. In particular:\n    - 3DSceneEditor (Yan et al., 2024) is another fully 3D-based native editing pipeline leveraging Gaussian Splatting. This work should be directly compared with or discussed in Section 2 and as a baseline in Section 4.2/Table 2.\n    - Gaussian Splatting in Style (Saroha et al., 2024), which introduces neural style transfer techniques directly into the 3DGS domain, offering valuable insights for stylization and attribute manipulation. This work should at least be discussed contextually.\n    - ManiGaussian (Lu et al., 2024) and Depth-Regularized Optimization for 3D Gaussian Splatting (Chung et al., 2024) are also relevant for scene dynamics and editing accuracy. Their omission affects the completeness and currentness of the empirical/positional claims, suggesting the need for updated discussion and possibly additional comparative experimentation.\n2. The claims about generalization (Section 4.5, Table 4, Figure 7) are largely limited to within-domain samples or instructions that are semantically related to those seen during training. There is explicit acknowledgment (Line 475)  that the model \"does not yet support out-of-domain editing\" but no serious attempt is made to systematically characterize the failure modes or boundary conditions of generalization. This diminishes the universality and practical significance of the claimed contributions.\n3. While the structure of the predictor and the use of zero-init linear layers are explained, the choice of the specific architecture (transformer blocks with and without self-attention) and the separation of decoding for position mean vs. attributes is mostly justified by empirical ablation rather than solid theoretical backing. For instance, Section 3.1 references attribute \"intercoupling\" but does not model it explicitly or provide a concrete analysis of when/why the architecture remains robust to different types of edits. It would strengthen the work to either formalize these choices further or relate them to established results in representation learning or signal decomposition.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "anfmLf8Eg7", "reviewer": "Reviewer_rKQ5", "summary": "This paper presents VF-Editor, a novel framework for \"native\" 3D Gaussian Splatting (3DGS) editing. The core idea of VF-Editor is to reframe 3D editing as a feed-forward variation prediction problem. Instead of predicting a new 3D scene, the method learns a single \"variation predictor\" ($\\mathcal{P}_{\\theta}$) that directly outputs the change (or variation, $\\Delta$) for all attributes of each 3D Gaussian based on a text instruction.\n\nThis predictor is trained by distilling knowledge from 2D editors. The training process collects {initial noise}-{instruction}-{edited 2D image} triplets. The resulting system, VF-Editor, can perform 3D edits in ~0.3 seconds.", "questions": "Since $\\mathcal{L}_{din}$ trains on single-view edits, how does the model handle conflicting 2D edits for the same 3D-instruction pair during training? For example, if IP2P produces a red hat from the front view but a blue hat from the side view for the \"put on a party hat\" instruction, how is the model learn under such data?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Predicting changes (Δ) instead of the final result is a smart and natural fit for 3D Gaussian Splatting. Since 3DGS is made up of explicit, editable primitives, it makes more sense to directly modificate their parameters rather than trying to infer 3D edits indirectly from 2D images.\n\nThe feed-forward nature provides a significant speed-up (0.3s) over iterative optimization methods.", "weaknesses": "Data Dependency: The entire framework is built on offline triplet collection ($\\mathcal{L}_{din}$). Table 1 indicates that 28,932 triplets were required for only 20 instructions. This approach seems to scale very poorly for a truly \"open-vocabulary\" editor. The paper admits in Sec. 4.6 that it does not support \"out-of-domain editing\" without fine-tuning (Fig. 14). This suggests the model is learning a mapping for a fixed set of instructions, not a general-purpose, compositional understanding of language.\n\n\nLimited Generalization: The \"unseen instruction\" results in Fig. 7 (\"Apply clown makeup\") are semantically almost identical to a training instruction (\"Turn him into a clown\"). This feels less like true zero-shot generalization and more like interpolation within a learned semantic space.\n\nSupervision Quality: The training relies on 2D editors. DDIM is not perfect. If the 2D editor (e.g., IP2P) produces a low-quality or inconsistent edit for a given view, this flawed supervision is baked into the triplet and, by extension, the 3D model. The paper doesn't discuss how it handles or filters \"bad\" 2D edits during the triplet collection phase.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "moyLYPL6pP", "reviewer": "Reviewer_jMsT", "summary": "The paper introduces VF-Editor, a feed-forward 3D Gaussian Splatting editor that predicts per-primitive variations (position, scale, opacity, color, rotation) to apply edits directly in 3D rather than via multi-view 2D edits and back-projection. It distills knowledge from 2D editors into a variation predictor composed of a transformer-based variation field generator and two iterative parallel decoding functions. This enables fast editing and fine grained control. Most crucially, the method targets reduced cross-view inconsistencies, flexible composition of edits (called by the authors \"variation fusion\"), and real-time performance. Limitations include ood generalization gaps and occasional local artifacts when relocating primitives.", "questions": "- If possible, please include a small user study focused on edit correctness and preservation of untouched regions. This would address proxy-metric brittleness and could change the confidence in the method's real-world reliability.\n\n- Can you maybe expand on generalization beyond the current object-centric distribution and clarify scalability limits? A concrete plan or preliminary results on more diverse scenes (indoor/outdoor, cluttered layouts, articulated subjects) would better support open-vocabulary, scene-level editing. Relatedly, how sensitive is performance to the tokenizer's group size and to Gaussian count, and do you foresee adding or removing primitives during editing to reduce spatial entanglement when relocating content?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The main strength of the paper is a neat problem reformulation: instead of predicting edited Gaussians outright, the proposed pipeline predicts per-primitive variations and composes them with the source. This gives a controllable, native 3D editing interface and sidesteps multi-view back-projection issues. Such a framing, together with the random tokenizer and the iterative, parallel decoders for position versus other attributes, feels fresh within 3DGS editing and is well-motivated by the representation's explicit structure. \n\nI am no expert in the area, but the technical design looks coherent and lightweight to me, and the training recipe seems well executed to distill multi-source 2D priors into a single feed-forward predictor.\n\nThe evaluation is broad and includes competitive quantitative results, as well as targeted ablations that isolate the benefits of iterative decoding and the parallel decoder. There are clear demonstrations of flexible control like strength scaling, locality, and variation fusion. I would have liked to see even more impressive results, but the ones shown in the demo compare very well to prior art nonetheless. The reported runtime (sub-second) and linear decoding complexity with respect to primitive count are also practical positives for interactive use.\n\nIn terms of clarity, the paper does a good job with pipeline schematics and equations making the approach easy to follow. \n\nOverall, to the extent of my expertise and knowledge in the field, I think the paper is a meaningful step that removes a common source of inconsistency in indirect pipelines and could influence further research on 3DGS.", "weaknesses": "I think there are a couple areas for improvement that are worth discussing. These cluster around data coverage, evaluation, and metodology. \n\n- The training data is well assembled but perhaps is still small and skewed toward objects, with only a handful of scenes; admittedly the authors note lack of ood support (e.g. new categories or environments), which constrains claims of universality and open-vocabulary editing. A more convincing path may add diverse indoor/outdoor scenes, articulated humans, and CAD-like geometry, and report generalization beyond the curated distributions and six scenes used for training and evaluation. Expanding the test suite and including open-vocabulary or paraphrased instructions would make the generalization story more robust than the current check limited to objects.\n\n- On evaluation, the work relies mainly on CLIP-direction metrics, IS, and an aesthetics score, plus qualitative examples; these are useful but weak proxies for 3D editing fidelity and structural preservation, and can \"over-reward\" appearance changes that do not respect geometry or multi-view consistency. Adding 3D-aware measures (e.g. normal/geometry preservation for non-geometric edits and multi-view consistency checks) would better support the native 3D claim. A small user study focused on edit correctness and obstruction of original content would complement automated metrics.\n\n- Methodologically, I suggest running a deeper ablation. The random tokenizer and group size choices could introduce sampling variance across runs or scenes; alternative tokenization and sensitivity to Gaussian count would make the lightweight/linear story more compelling.\n\nFinally, some limitations are acknowledged and suggest concrete next steps. Relocating primitives can nudge nearby regions, which hints at limited spatial disentanglement; introducing local attention fields could mitigate this. Overall, the core idea looks promising, but the paper would be stronger with broader scene coverage and out-of-domain tests, 3D-aware metrics and user validation; these seem feasible within the current framework and align with the stated goals.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ChP4w8x4PL", "reviewer": "Reviewer_giZT", "summary": "The paper proposes VF-Editor, a novel method for editing 3D Gaussian Splatting (3DGS) scenes directly by predicting attribute variations. Instead of relying on indirect editing through 2D image manipulation, VF-Editor distills knowledge from 2D editors into a native 3D editor. A variation predictor, composed of a variation field generation module and parallel decoding functions, is trained to efficiently estimate attribute changes for each 3D Gaussian. The paper emphasizes the method's flexibility, efficiency, and ability to learn from diverse 2D editing strategies, leading to consistent and high-quality 3D edits.", "questions": "- Many details are missing like dimensions, size, exact steps, what noise model is used, more details on losses, etc.\n\n- Are there specific types of edits that VF-Editor struggles with (e.g., large structural changes, view-dependent effects, highly detailed textures)? What are the failure modes?\n\n- More elaboration on the fundamental difference with prior works that utilise 2D diffusion priors on 3D editing tasks.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-The core idea of directly predicting attribute variations for 3D Gaussians, guided by distilled 2D editing knowledge, is novel and addresses limitations of existing indirect editing methods.\n\n-The design of the variation predictor, with its transformer-based components, is very fast since it is feedforward rather than relying on computationally expensive techniques. \n\n-The parallel decoding functions promise efficient processing.\n\n-The paper presents some qualitatively impressive results, demonstrating the ability to perform various editing operations (e.g., style transfer, object replacement) while preserving scene details.", "weaknesses": "-Uses a 2D diffusion prior from models like DDIM has been used in some other 3D scene editing works, such as:\n[CVPR 2023] DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models\n[NIPS 2024] In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment\n\n-A more thorough ablation of the different components in VF-Editor (e.g., contribution of the random tokenizer, importance of different loss terms) would be useful.\n\n-While claiming \"real-time editing\", the paper lacks precise runtime measurements or a performance comparison against other editing methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)