TINKER: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
Authors: Canyu Zhao
Xiaoman Li
Tianjian Feng
Zhiyue Zhao
Hao Chen
Chunhua Shen
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model
3D Editing
Primary area: generative models
TL;DR: TINKER achieves generalizable 3D editing with one or few inputs without per-scene optimization and demonstrating strong potential for 4D editing.

Abstract: We introduce TINKER, a novel framework for high-fidelity 3D editing without any per-scene finetuning, where only a single edited image (one-shot) or a few edited images (few-shot) are required as input. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edited input views, TINKER delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D awareness. To drive research in this space, we curate the first large-scale multi-view editing dataset and data pipeline, spanning diverse scenes and styles. Building on this dataset, we develop our framework capable of generating multi-view consistent edited views without per-scene training, which consists of two novel components: (1) Multi-view consistent editor: Enables precise, reference-driven edits that remain coherent across all viewpoints. (2) Any-view-to-video scene completion model : Leverages spatial-temporal priors from video diffusion to perform high-quality scene completion and novel-view generation even from sparse inputs. Through extensive experiments, TINKER significantly reduces the barrier to generalizable 3D content creation, achieving state-of-the-art performance on editing, novel-view synthesis, and rendering enhancement tasks, while also demonstrating strong potential for 4D editing. We believe that TINKER represents a key step towards truly scalable, zero-shot 3D and 4D editing.

Published as a conference paper at ICLR 2026
TINKER: DIFFUSION'S GIFT TO DD-MULTI-VIEW CONSISTENT EDITING FROM SPARSE INPUTS WITH- OUT PER-SCENE OPTIMIZATION
Canyu Zhao Xiaoman Li Tianjian Feng'
Zhiyue Zhao'
Hao Chen
Chunhua 1..3
1 Zhejiang University, State Key Lab of CAD & Computer Graphics, China Zhejiang University Technology, China
Ant Group
ABSTRACT
We introduce TINKER, novel framework for high-fidelity 3D editing without any per-scene finetuning. where only single edited image (one-shot) or few edited images (few-shot) are required input. Unlike prior techniques that de- mand extensive per-scene optimization to ensure multi-view consistency or to pro- duce dozens of consistent edited input views, TINKER delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D aware- ness. To drive research in this space, we curate the first large-scale multi-view editing dataset and data pipeline, spanning diverse scenes and styles. Building on this dataset, we develop our framework capable of generating multi-view consis- edited views without per-scene training, which consists of novel com- ponents: (1) Multi-view consistent editor: Enables precise, reference-driven edits that remain coherent across all viewpoints. (2) Any-view-to -oviidoo scene com- pletion model Leverages spatial-tempon priors from video diffusion to perform high-quality scene completion and novel-view generation even from sparse inputs. Through extensive experiments, TINKER significantly reduces the barrier to gen- eralizable 3D content creation, achieving fatte-of-the-aree performance on editing, novel-view synthesis, and rendering enhancement tasks, while also demonstrating strong potential for 4D editing. We believe that TINKER represents a key step towards truly scalable, zero-shot 3D and 4D editing.
Original 3D
Rendering Edittd Gaueiia
Scene Object-Lvvel
TINKER
Generaliaale No Per-scene Finetuning One and Few-shot Editing
Figure 1:
Compared with prior 3D editing approaches, TINKER removes the necessity of labor- edited inputs for
intensive per-scene fine-tuning whehtee for generating multi-viiewconsiiie 3DGS optimization or for preserving consistency through scene-specific training. Moreover, TINK- ER is capable of performing both object-level and scene-level 3D editing, and achieves high-quality results in few-shot as well as one-shot settings. Please refer to Figures S2 for 4D editing results and Figures S8, $9, S10, S11 for more compelling visualizations.
*CZ and XI. claim equal contrihutions. CS is the corresponding author.

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(a) Locally Consistent But Globally Inconsistent
(b) Fail to Perform Referring Editing
Input Pair
Input Pair
Input Pair with Reference
Large Image Editing Model
Large Image Editing Model
Consistent
Consistent
Fail to Edit
Inconsistent
High Fallure Rate
Figure 2: (a) Recent large image editing model achieves multi-view consistent image editing by horizontally concatenating two images and editing them jointly. Although ensures consistency between the concatenated image pair, significant inconsistencies remain across different image pairs. (b) We also evaluated whether recent large image editing model can edit one half of the concatenated image by referencing the other half. The results demonstrate that the model lacks this capability.
INTRODUCTION
Benefiting from the rapid advancements in 2D diffusion models (Rombach et al., 2022: Esser et al., 2024; Labs, 2024). prevailing paradigm for 3D editing has emerged: generating multi-view consistent images via 2D diffusion-basck editing, followed by fine-tuning 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) or Neural Radiance Field (NeRF) (Mildenhall et al., 2021) to edit 3D) scenes. This pipeline has become the de facto standard in recent 3D editing approaches.
During the era when U-Net-based diffusion models dominated, many successful 3D editing ap- proaches were inspired by advances in 2D image editing, such Instruct-NeRF2NeeRt (Haque et al., 2023) and InstructPix2Pix (Brooks et al., 2023). Recently, the emergence of Diffusion Trans- former (DiT) architectures (Peebles & Xie, 2023) and Flow Matching (Liu al., 2022; Lipman et al., 2022; Albergo & Eijnen, 2022: Esser et al., 2024) has significantly advanced the field of generative modeling. Latest developments have demonstrated substantial improvements in both image and video generation (Wan et al., 2025; Esser et al., 2024; Labs, 2024), editing (Wang al., 2024a; Yu et al., 2025; Ku et al., 2024; Jiang et al., 2025; Labs et al., 2025), and even vision understanding (Zhao et al., 2025; Wang al., 2025b; Ke et al., 2024). all driven by large-scale DiT flow-based models. Theoretically, 3D editing should also naturally evolve to incorporate and ben- efit from these powerful new architectures and methodologies. However, find that current 3D editing methods have yet to fully capitalize on these recent breakthroughs. Many recent approaches, despite producing impressive results, remain constrained by conventional U-Net-based methodol- ogy (Haque et al., 2023; Wu et al., 2024b; Zhuang et al., 2024; Fujiwara et al., 2024), rather than embracing the more powerful and scalable techniques (Wang et al., 2025a; Zhang et al., 2025). Con- sequently, these methods require laborious per-scene fine-tuning, including model training or costly scene-by-scene hyperparameter tuning, and lack the ability generate edits with better diversity and visual quality now expected from state-of ofttee-of-hheaar 2D diffusion models. There remains a notice- able disconnect between the progress made in 2D editing and the latest methods in 3D editing, One key reason behind this limitation is the lack of multi-view consistent image editing datasets. For re- cent unified generation and editing models, fine-tuning with large-scale data has proven be highly effective. However, the difficulty in collecting high-quality multi-view consistent datasets hinders progress in 3D-aware or view-consistent editing tasks.
Inspired by the remarkable capabilities of large language models in addressing unseen tasks (Wei et al., 2021; Achiam et al., 2023; Guo et al., 2025), we pose a natural question: can recent large-seale image editing foundation model (Labs et al., 2025) also perform multi-view consistent editing that benefits 3D editing? The answer is confirmative. We observe that simply concatenating two im- ages input enables these models to produce highly consistent and high-quality edits across views.
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However, while this pairwise concatenation ensures consistency between the two input views, find that significant discrepancies often arise between different image pairs, thereby limiting global view consistency. A straightforward idea is to concatenate unedited image with edited one, using the latter as a reference to guide the editing process. However, we observe that current foun- dation model does not exhibit this capabiliiy, usually producing inconsistent results, and typically reproduces the unedited image without modification. We illustrate both cases in Figure 2.
To address this issue, we design a novel pipeline that amplifies the model's capability for multi- view consistent editing. Specifically, we first introduce a data pipeline to generate referring editing dataset, where an unedited image is concatenated with another view that has already been edited. Fine-tuning with this dataset encourages the image editing model to learn how to propagate the editing intent across different viewpoints, which significantly improves the editing success rate and promotes better view consistency. Furthermore, to efficiently propagate edits from sparse set of edited views to a dense set of novel views, we introduce a scene completion model, which effec- tively bridges 2D and 3D editing by leveraging video editing. The model can generate multitude of view-consistent edits from sparse inputs, thereby providing robust data foundation for data- intensive 3DGS and ensuring high-quality results. Unlike previous approaches that rely on repeated fine-tuning either to enforce multi-view consistency or to obtain setti-view--consiise for downstream 3DGS optimization, the key distinction of TINKER lies in its ability to directly produce high-quality, multi-view-consisten edited input views that can be seamlessly lever- aged for 3DGS optimization without per-scene fine-tuning. Additionally, by fully exploiting the priors embedded in foundation models, our approach is also able to enhance the overall rendering quality of 3D scenes. Furthermore, we demonstrate the sigmificant potential of TINKER for ap- plications in 4D editing. We believe TINKER paves the way for future research in generalizable, user-friendly 3D content creation.
input views
In summary, our main contributions are as follows:
We design novel pipeline that effectively elicits the multi-view consistent editing capabilities of large-scale image editing models, and introduce, to the best of our knowledge, the first multi-view consistent image editing dataset.
We introduce a sparse-view scene completion model specifically tailored for 3D editing tasks by rethinking editing problem as reconstruction problem. In addition to 3D editing, our model is capable of performing video reconstruction and 4D editing.
Our TINKER eliminates the requirement for per-scene optimization that previous methods neces- sitate to ensure multi-view consistency to generate multi-view-consisterr thereby significantly lowering the barrier for practical use of 3D editing. We hope that TINKER can serve as general-purpose foundation for future advancements in 3D editing.
edited input views,
2 RELATED WORK
2.1 DIFFUSION MODEL
Diffusion models (Ho et al., 2020; Song et al., 2020; Rombach et al., 2022: Esser et al., 2024; Labs, 2024) a powerful class of generative models that produce high-quality and diverse outputs by learning to reverse a gradual noising process. This process consists of a forward stage, where data is incrementally corrupted by Gaussian noise over multiple steps, and a reverse stage, where a neural network is trained to iteratively denoise and reconstruct the original data. Transformer- based architectures (Peebles & Xie, 2023; Vaswani et al., 2017) and dow-matching (Albergo & Handen-Eijnded 2022; Lipman et al., 2022; Liu et al., 2022) objectives have recently become the mainstream design choices in diffusion models (Esser et al., 2024; Labs, 2024), offering significant improvements in generation quality and scalability. Owing to their strong generative priors acquired by large-scale training, diffusion models have significantly advanced a variety of vision tasks, such as image and video generation (Blattmann et al., 2023; Wan et al., 2025), editing (Brooks et al., 2023: Yu et al., 2025; Labs et al., 2025; Tian et al., 2025), image perception (Ke et al., 2024; Zhao et al., 2025; Wang et al., 2025b).
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2.2 2D EDITING
To achieve image editing, some pioneering studies explored alterations to the attention mechanism within the generative model (Hertz al., 2022: Chefer al., 2023). A majority of approaches in image editing primarily revolved around inversion-based methods. These methods work by first in- verting an input image back into its latent noise representation, and subsequently use new prompt to generate the edited image (Mokady et al.. 2023; Cao et al., 2023; Song et al., 2020; Wang et al., 2024a; Rout et al., 2024). Beyond the inversion-based methods, some methods directly train models to follow explicit editing instructions (Brooks et al., 2023; Pan et al., 2023). Similarly, the paradigm of video editing largely aligns with that of image editing (Qin et al., 2024; Liew et al., 2023; Liu et al., 2024; Geyer et al., 2023; Ku et al., 2024; Khachatryan et al., 2023). Recently high-quality uni- fied models have emerged in both image (Yu et al., 2025; Labs et al., 2025) and video editing (Jiang et al., 2025). However, we observe that most mainstream approaches do not focus multi-view consistent editing (Liu et al., 2023c;b). While some recent methods (Jiang et al., 2025) can perform depth-conditioned video editing, their primary focus lies in generation, which often results in videos with large motion and dynamics, causing multi-view inconsistencies.
2.3 3D EDITING
3D Gaussian Splatting (3DGS) (Kerbl al., 2023) and Neural Radiance Fields (NeRF) (Mildenhall et al., 2021) are two widely adopted 3D representations in recent years. Early approaches typically performed style transfer by learning a mapping between the source and target scenes (Liu et al., 2023a). With the rapid advancement of 2D diffusion models, numerous 3D editing methods have incorporated them as key modules (Chen et al., 2024a; Fujiwara et al., 2024; Haque et al., 2023; Wu et al., 2024b; Zhuang et al., 2024). Some methods (Chen et al., 2024b: Decatur et al., 2024; Dong et al., 2024; Sella et al., 2023) leverage Score Distillation Sampling (SDS) (Poole et al., 2022) to per- form editing by guiding the optimization of 3D representations with gradients derived from powerful pretrained diffusion models. Nowadays, the prevailing paradigm is to leverage diffusion models to generate edit a sufficient number of views, which are then used to finetune the underlying 3DGS. However, while recent 2D diffusion models have seen significant breakthroughs in both generation quality and editing controllability, many of the latest 3D) personalization approaches still rely on early U-Net-based architectures and approaches (Kim et al., 2024; Zhang et al., 2024), thereby fail- ing to take advantage of the latest advancements in the image and video diffusion models. While a small number of recent approaches (Wang et al., 2025a; Zhang et al., 2025) have adopted state- of-the-art image and video diffusion models and demonstrated high-quality 3D editing results, they often depend on per-scene finetuning, which poses challenges in terms of efficiency and scalability. In contrast, our method not only fully leverages recent developments 2D diffusion models (Labs et al., 2025; Wan et al., 2025), but also eliminates the need for per-scene training, achieving more compelling 3D editing a simple yet elegant manner.
METHOD
We begin in Section 3.1 by elucidating TINKER'S process for both few-shot and one-shot editing. We then provide a detailed account in Section 3.2 of how we constructed our dataset and model for multi-view consistent image editing. In Section 3.3, we introduce our model for scene completion from sparse views. Appendix C.2 and C.4 further discusses TINKER's 's potential for 4D Editing and additional applications.
3.1 3D EDITING WITH SPARSE VIEWS AS INPUT
Editing with few-shot input. As illustrated in Figure 3, given an original 3DGS 9. our objective is to generate an edited 3DGS G'. We begin by rendering few videos from 9 and randomly selecting a few sparse views. These selected views are edited using our multi-view consistent image editing model to produce the edited reference views. We then estimate the depth maps of the rendered video using Video Depth Anything (Chen et al., 2025). With these depth maps and the edited reference views, we employ our scene completion model to generate the images of the other views. Since the video is rendered from G. we have access to the exact camera pose for each view, which allows the completed frames to be directly used in optimizing 9 into the edited g'. The entire editing does not necessitate any per-scene finetuning.
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Same Camera Pose Used Render
Video Depth Anything
Render
Scene
Randomly Select Sparse Views
Depth
Completion Model
Original 3DGS
Multi-view
Edited Views
Edited 3DGS
Consistent
Editing
Sparse Edited Views
Overview of our editing process. We first apply our multi-view consistent editing model to obtain coherent sparse views. Leveraging depth constraints from rendered results, we generate large number of consistent edited images. The edited images are used to optimize the 3DGS to achieve huah-quality 3D editing.
Figure 3:
Editing with one-shot input. TINKER is also capable handling a more challenging scenario: editing with one single reference view without additional training. The procedure remains identical to the few-shot scenario, with key difference being that we select only a single sparse view from the rendered videos to serve the initial reference for editing. This initial reference view is then used by the scene completion model generate an initial set edited views. These newly generated views, in turn, with the process continuing until the entire scene sufficiently covered by the generated views. As result, edited 9' is achieved by fine-tuning 9 with these generated views.
as subsequent reference views progressively propagate the edit,
3.2 MULTI-VIEW CONSISTENT EDITING
Our approach begins with the observation that the statc-of-the-art large-scale image editing model (Labs al., 2025) is capable of achieving multi-view consistent editing when provided with two concatenated images as input. This simple strategy ensures consistency between the two con- catenated views. However, it fails to enforce consistency across different image pairs, leading to global inconsistencies. A straightforward solution is to concatenate an edited image with an unedited one, prompting model to apply similar edits to the latter by implicitly referencing the former. However, find that this approach yields very low success rate, with the model frequently revert- ing to simply reproducing the original unedited image. The model is not exposed to such reference- based editing configurations during its pretraining, and therefore lacks the knowledge needed to generalize in this setting.
Inspired by the strong local consistency exhibited by the large image editing model, leverage the model itself to construct large-scale reference-based editing dataset. Specifically, we begin different views of the same scene from publicly available 3D-aware
by randomly selecting datasets (Yeshwanth et al., 2023; Baruch et al., 2021; Liu et al., 2025; Ling et al., 2024; Xia et al., 2024: Zhou al., 2018). Given 3D-aware dataset D {I.). where I denotes the i-th scene from view c, we sample 2 views L, I, E D of the same scene from different viewpoints. We omit the scene index for simplicity to indicate that views are sampled from the same scene. Subsequently. we generate editing prompts P with large language model and perform editing using model E:
I Ir = E(Concat (I,, Io); P)
(1)
To ensure that the editing is effectively applied, we compute the feature similarity between the edited and original images using DINOv2 (Oquab et al., 2023):
81 simmo (Ia), (T,)) $2 = sim(faino (L) faino (T,)) Snoedit max 82)
We discard pairs with overly high similarity scores that exceed threshold Tnoedit by 8dino > Troedit, indicating insufficient editing. Furthermore, we evaluate the similarity between the two edited views by Syn = sistency below threshold Taa by Sme Timv Finally, construct training inputs by pairing an original image with reference edited image from different view, and fine-tune the model us- ing LoRA (Hu et al., 2021) learn reference-based editing. This process empowers the model to
and filter out samples with low inter-view con-
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Fail-to-Fdit Results
Winter
Blue Wall
Snoedit diz
Base
Random Sample lorizontal Concat
Editing Model
Ghibli Cartoon
Golden House
Editing Prompts From I.I.M
Smy Tyu
Locally Consistent Pair
Inconsistent Results
(a)
Add Dataset
X Discard
LoRA
Dataset
Base
Editing Model
(b)
Replace Image Left
Flow Matching
Loss
Figure 4: (a) We leverage recent base editing model to generate a large number of consistent image pairs and discard the inconsistent and fail-to-edit results. (b) The data generated in (a) is used to fine- tune the base editing model itself. Specifically, we horizontally concatenate the input image and an edited image, and train the model using LoRA to learn how to perform referring- based editing.
generalize edits across views and achieve globally consistent results. This process and the training objective can be formulated as:
I == Concat (I,, I,), I' Concat (I I,), zo = g(I), g(I')
(3)
Loss Eoo.:: P) uzzz)
where is the Variational Autoencoder that maps images to the latent space. We employ a flow matching loss (Liu al., 2022; Lipman al., 2022: Albergo & Vanden- dijdenn, 2022; Esser al., 2024) to minimize the discrepancy between the model's 's predicted velocities and the ground truth velocities u.
3.3 SCENE COMPLETION MODEL.
While it is feasible to perform view-by-view editing by leveraging our multi-view consistent image editing model using sparse reference views, this approach is extremely time-consuming. Motivated by the recent success of video diffusion models, aim exploit their strong spatial-temporal priors to achieve efficient scene completion with sparse edited views. A natural idea to train model that edits the original 3DGS rendered video into target video, conditioned view consistent edited images. However, there are such editing datasets currently available. Therefore, rethink the problem by casting the training objective of the editing task into the reconstruction task. Specifically, we aim to train model to reconstruct the original scene from sparse views, with the goal of generalizing reconstructing the edited scene from edited views, thus achieving editing through reconstruction.
multi-
In generative modeling, several works have attempted to condition multi-view generation on ray maps that encode camera parameters. However, we find that this approach lacks sufficient geometric constraints, often resulting in view inconsistencies. Upon further analysis, we argue that depth is more suitable conditioning signal in the context of 3D. Depth not only explicitly encodes structural constraints but also implicitly carries information about camera pose. Moreover, it greatly benefits from recent advances in depth estimation.
Recent unified model for video generation and editing, VACE (Jiang et al., 2025), has explored depth-guided video editing. However, generation is their main objective, they treat depth more as a soft reference than an explicit constraint, resulting in results that deviate from the intended geo- metric structure. Although its performance is remarkable in depth- generation, this flexibility

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undesirable in 3D editing scenarios where require precise control over specific areas while maintaining geometric consistency in unedited regions. 3D editing necessitates a 3D-aware scene completion model that accurately follows depth constraints to maintain strict geometric consistency throughout the scene.
Noise
Depth Condition
Sparse Reference Views
Flow Matching Loss
Scene Completion Model
Discarded
Completed Views
Figure 5: Visualization of our scene completion model. Contours of the same color denote elements that share the same positional embedding. During training, the flow matching loss is computed exclusively between the model outputs corresponding to the noisy latents and the ground-truth truth video frames. Outputs corresponding to the conditioning tokens are discarded and do not contribute to the loss calculation.
Based on the above analysis, we develop our scene completion model tailored for 3D. To this end, we leverage the pretrained WAN2.1 (Wan et al., 2025) to train an image-to-video model that accu- rately follows depth conditions. Given the scarcity of 3D video datasets, we take inspiration from Diception (Zhao et al., 2025), which achieves strong performance with limited data. Specifically, treat the depth maps as RGB images and process them into tokens, following the original procedure of Wan2.1. 1. The reference views are also processed into tokens in the
manner. The noisy latent
tokens z / .., Z at timestep I, depth tokens D [Do, Di...., Dv and reference view tokens V are concatenated along the sequence dimension to form the model input. The training process also follows the flow matching, which is formulated as:
X Xpptt Concat (Z' D)
(4)
Loss B.zoee/Poo
(X (Xinput t) 2 1
During training, we always provide the first frame as the default reference view, and randomly select 2 additional reference views help the model learn both one-shot and few-shot scene completion. The text embedding is fixed to a constant embedding, enhancing the model to focus solely on depth-guided generation. To enable the model to associate these reference views with the target j-th frame, we assign them the same positional embedding as the target frames:
PE(V) PE(D,) = PE)
Through this design, the model effectively learns which regions of the scene correspond to the reference view, and able to leverage both depth and reference views to achieve high-quality scene completion.
EXPERIMENTS
4.1 COMPARATIVE EXPERIMENTS
Due to space limitations, the implementation details are provided in Appendix B. Building upon this implementation, we conduct comprehensive qualitative and quantitative comparison with the latest high-quality 3D editing methods (Wu et al., 2024b; Chen et al., 2024a; Zhuang et al., 2024; Lee et al., 2025) in terms of both output quality and computational cost. Our evaluation is con- ducted on the Mip-NeRF-360 (Barron et al., 2022) and IN2N (Haque et al., 2023) datasets. For each scene, we perform editing using the same textual prompt, and subsequently render the edited scenes from uniformly sampled camera poses to obtain results from different methods under identical novel viewpoints. For methods necessitating image input, follow their official preprocessing pipelines
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Source
DGE
EditSplat
CaussCtrl
IT--dditrr
Ours: Ours-One-Siie One-Shot Ours- Few-Shol
Grizzly Bear
Bronze Statue
Vincent
Van Gogh
Japanese Manga
Figure 6:
Qualitative comparisons of novel views in different methods. Our approach demonstrates both better visual quality and more diverse editing results.
Quantitative comparisons of different methods. acceptable computational cost.
Table I:
TINKER achieves superior results with
Quality
Computational Cost
CLIP-dirt
DINO
Aesthetict
On 24G GPU
Avg. Editing Time
DGE
0.102
0,948
5.747
Yes
10min
GaussCtrl
0.123
0.957
5.624
No
20min
TIP-Editor
0.084
0.875
5,397
No
35min
SpatSplat
0.102
0.956
5.661
Yes
19min
Ours-one-sher
0.143
0.958
6.214
Yes
15min
Ours-few-shoo 0.157
0.959
6.338
Yes
15min
to prepare the corresponding images. We use NeRFStudio (Tancik et al., 2023) for 3DGS optimiza- tion and rendering. Furthermore, we quantitatively evaluate the results using four metrics: CLIP Text- Text-Image directional similarity (Radford et al., 2021) used in GaussCtrl (Wu et al.. 2024b) assess semantic alignment, DINO similarity (Oquab al., 2023) between the edited renderings measure cross-view consistency, and aesthetic score (Schuhmann et al., 2022) to assess rendering quality.
As demonstrated in Figure and Table in both one-shot and few-shot settings, our approach con- sistently outperforms existing methods for both object-level and scene-level editing. Furthermore, some methods, such as TIP-Editor (Zhuang et al., 2024), require per-scene fine-tuning, making them infeasible to run on consumer-grade 24 GPUs, whereas our method works entirely without further per-scene fine-tuning and can be executed efficiently on a single consumer-gres gradee GPU. Moreover, observe that while ducing high-quality object-level edits, they fall short in performing scene-level edits. In contrast, our method supports both oeject-level and scene-level 3D editing with higher quality, even for scenes with substantial overall style changes, such as oil paintings or Black-and-whir comics.
approaches such as TIP- Edd-tr (Zhuang al., 2024) capable of pro-
4.2 ABLATIONS AND ANALYSES
We performed comprehensive set of ablation studies and analyses to better understand the fectiveness of our design choices. Specifically, we examined the effectiveness of fine-tuning our multi-view consistent editing model, assessed the effect of concatenating additional images for con- sistent editing, and evaluated the advantages of employing depth as conditioning signal over the ray-map conditioning used in prior work. Moreover, we analyzed the strengths of our approach rela- tive to existing depth-guides video generation methods. Owing space constraints, comprehensive additional results, 4D editing results, pideo-related ablation studies, and an detailed exploration of further applications, are presented in Appendix C.I, C.2, C.3, C.4 respectively. Taken together,
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Input
Before
Finelune
Large Image Editing Model
Change the style to cartoon
After Finetume
Qualitative comparisons before and after multi-view consistent image editing fine-tuning. After fine-tuning, our model can perform edits guided by the provided reference image, effectively ensuring the global consistency.
Figure 7:
Change the style to autumn
Assthelle Score
Concat Images
Images
Concat Images
Num of Images Concat
Figure 8: Effect of the number of horizontally concatenated images on visual quality. Concatenating too many images leads to a significant degradation image quality, while concatenating two images yields the best results.
comprehensive experiments not only validate the effectiveness of TINKER'S 's core design choices but also demonstrate its remarkable capabilities.
Multi-view consistent editing finetuning. To validate the effectiveness of our fine-tuning for multi- view consistent editing, compare the global consistency of the results and the model's abil- ity to align with the provided reference images before and after fine-tuning. We use Mip-NeRF 360 (Barron et al., 2022) the evaluation set, where 10 prompts referring editing, producing 20 images per prompt per scene. We then compute the DINO sim- ilarity (Oquab al., 2023) among the generated images for each prompt each scene and take the average across all scenes, which serves as measure of cross-view consistency. As shown in Figure 7 and Table 2, fine-tuning significantly improves both the global consistency of the edited results and the model's ability to faithfully follow the editing cues from the reference views.
applied each scene for
Concatenating more images for multi-view consistent editing. An intuitive approach is to concatenate all multi- view images intended for editing and feed them into the model to achieve consistent editing. However, we ob- served that this strategy leads poor results and multi- view inconsistency. Even under minimal setting, editing with just three concatenated images, the output images exhibit noticeable quality degradation. This issue arises because the underlying base model imposes constraint on the image resolution. As result, input images are au- tomatically resized to fit within the limit. When too many images concatenated, each individual image is heav- ily downsampled, leading to substantial loss detail and visible blurring. As shown in Figure 8, we compare the Aesthetic Score across different numbers of concatenated images and find that concatenating two images strikes a reasonable balance hetween consistency and visual fidelity.
Table 2: After multi-view consistent image editing fine-tuning, the edited images exhibit substantially improved multi-view consistency, while main- taining comparable text-image align- and aesthetic quality the non- finetuned results.
Before
After
DINO CiiP-dir Aesthetic
0.862
0.943
0.277
0.281
7,058
6.973

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5 CONCLUSION
We propose TINKER, to the best of our knowledge, the first general-purye purppss 3D editing framework that eliminates the need for per-scene optimization. TINKER bridges critical gap by extending the breakthroughs of 2D diffusion models into the domain of 3D editing, enabling high-quality results few-shot even one-shot settings. also introduce the first dataset and data pipeline specifically designed for multi-view consistent editing to benefit future researches. Beyond editing, TINKER also demonstrates additional versatility across tasks such as video compression and video editing, showcasing the potential of unified 2D, 3D, even 4D editing framework. We believe that TINKER offers scalable, flexible, and generalizable solution for future editing research.
ACKNOWLEDGMENTS
This work was in part supported by the National Key R&D Program of China (No. 20227D01601 60101), Ningbo Science and Technology Bureau (No. 20242221) and the National Natural Science Foundation of China (No. 62206244).
ETHIC STATEMENT
This work adheres to the ICLR Code of Ethics. The research presented this paper is based on pub- licly available datasets and models, and focus algorithmic advancements. We have analyzed the potential risks and societal impacts of our work and have not identified any significant ethical concerns. Our study does not involve sensitive data or applications with a high potential for harm. We are committed to the responsible development and application of machine learning.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our research, we have made the following resources available:
Implementation Details: A detailed description of our method and the formulation of the loss functions are detailed in Section 3. The hardware, open-source models, hyperparameters and exper- imental settings required to reproduce our main results are detailed in Appendix B. This includes learning rates, batch sizes, model architectures, and the specific computational infrastructure used (e.g., GPU type, number of GPUs).
Data: All datasets used in this study are publicly available. In Appendix B, we provide detailed descriptions of the datasets, along with preprocessing steps and links to the original sources.
Code Availability: The source code and pre-trained models for this paper will be made publicly available upon acceptance to facilitate reproducibility and encourage future research.
We believe that these resources provide comprehensive basis for the research community to repro- duce and build upon our work.
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ACM Transactions on Graphics
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THE USE OF LARGE LANGUAGE MODELS
We utilized the Large Language Model (LLM) for two specific and limited purposes. First, it was employed as writing assistance tool to improve the grammar, clarity, and readability of the manuscript. Second, it used to generate the instructional text for referring editing dataset. LLM did not contribute to the formulation of core research ideas, the development of the methodol- ogy, or experimental results and conclusions presented in this paper.
A APPENDIX OVERVIEW
This appendix presents supplementary materials complement the main paper, offering further implementation details, additional results, and extended ablations and analyses. The contents are organized as follows:
Appendix B: Implementation Details
A detailed breakdown of our implementation, covering:
B.1: Training details and dataset specifics for the multi-view consistent editing model,
including key hyperparameters and GPU configurations
B.2: Training details and dataset specifics for the scene completion model.
Appendix C: Additional Results and Analyses
A collection of further results and in-depth analyses, including:
C.I: Additional visualizations our 3D editing results.
C.2: Showcase of our 4D dynamic scene editing capabilities.
C.3: Further ablation studies and analyses validating the design choices of TINKER.
C.4: Demonstrations of other applications, such as quality refinement, video recon- struction, and test-time optimization.
Appendix D: Limitations and Discussions
A discussion of the potential limitations of our method.
B IMPLEMENTATION DETAILS
B. MULTI-VIEW CONSISTENT IMAGE EDITING DATASET AND MODEL
For the multi-view consistent image editing model, we adopt Flux Kontext (Labs et al., 2025) as foundation model and construct referring multi-view consistent image editing dataset following the procedure detailed in Section 3.2. We use GPT-o3 to generate 400 image editing instructions. The inputs of data pipeline sourced from publicly available 3D-aware datasets, including DL3DV (Ling et al., 2024), WildRGBD (Xia et al., 2024), and uCO3D (Liu et al., 2025). We randomly select two images from each scene, concatenate them, and perform editing. The results are filtered using the procedure described in Section 3.2 to determine whether they should be retained. During dataset construction, Although this may occasionally filter out some good samples, it effectively reduces the number of bad cases in the dataset, thereby benefiting model training. Specifically, we set Troedit 0.95 and
thresholds for data validity, Troedit and Time are set quite strictly.
Tap = 0.9 to ensure sufficient data quality. In total, our dataset comprises 250,000 samples, each containing two original images, one edited image, and the corresponding editing instruction. We show the input Large Vision Language Model to generate the editing prompts in Figure S12 and illustrate some data samples Figure S13, Both the dataset and the data generation pipeline will be released to facilitate further research in this area.
When fine-tuning for referring editing using LoRA (Hu et al., 2021), we apply LoRA with rank 128 to all the query, key, value, and output layers of the base model. Training is performed with dropout rate of 0.05 for 30,000 iterations on four NVIDIA H100 GPUs, using a constant learning rate of 2e-5 and the Adam optimizer (Loshchilov & Hutter, 2017).
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B.2 SCENE COMPLETION MODEL
For scene completion model, we employ Wan2.1 1.3B model (Wan et al., 2025) as the foundational backbone of our scene completion model. Our model undergoes two-stage training protocol. Initially, it is pre-trained on the large-scale OpenVid-IM Vid-IM dataset (Nan et al., 2024). Subsequently, to instill robust 3D-aware capabilities, we fine-tuned the model on curated collection of 3D-centric datasets, including DL3DV (Ling et al., 2024), Rel0k (Zhou et al., 2018). ArkitScenes (Baruch al., 2021). WildRGBD (Xia al., 2024), and uCO3D (Liu et al., 2025). Depth annotations for our training data are generated using the Video Depth Anything model (Chen et al., 2025). The training scene completion model was conducted for 200,000 iterations on a cluster of 16 NVIDIA H100 GPUs using constant learning rate of 2e-5. The evaluation datasets are from Mip-NeRF- 360 (Barron et al., 2022) and IN2N (Haque et al., 2023). Finally, we use NeRFStudio (Tancik et al., 2023) for 3DGS optimization and rendering.
Figure SI: Visualizations of edited 3DGS and renderings using NeRFSSudio.
C ADDITIONAL RESULTS
C. ADDITIONAL VISUALIZATIONS
We employ NeRFStudio (Tancik et al., 2023) to optimize the 3D Gaussian Splatting using the edited views as input, as illustrated in Figure SI. In this section, we further present additional one-shot and few-shot 3D editing results in Figure S8, S9, S10, S11. These comprehensive visualizations demonstrate that our method significantly lowers the usage barrier, enabling high-quality object- level and scene-level 3D editing of various styles without requiring per-scene fine-tuning.
C.2 TINKER FOR 4D EDITING
Beyond static scenes, TINKER's 's capabilities extend compellingly into the temporal domain, show- casing strong potential for 4D dynamic scene editing. We validate this the challenging scenes from DyNeRF dataset (Li et al., 2022), leveraging 4D Gaussian Splatting (4DGS) (Wu et al., 2024a) as the underlying dynamic scene representation. Crucially, our editing pipeline for 4D scenes re- mains the same as our editing pipeline for 3D scenes. The process begins with our sparse multi-view consistent editing technique to generate small set of edited, yet consistent, anchor views. Subse- quently, our scene completion model utilizes these sparse views to generate dense and temporally coherent images, which are then used to optimize the downstream 4DGS model, effectively propa- gating the desired edit throughout the entire dynamic sequence.
As illustrated in Figure S2, key strength of our method is its versatility, enabling both localized, object-level modifications and global, scene-level stylizations within the 4D context. TINKER can seamlessly alter the appearance of a specific object or transform the entire scene's atmosphere over time. Across all visualized scenarios, our framework consistently produces high-quality results. The
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generated edits not only visually striking in individual frames but also maintain strict temporal coherence, ensuring smooth and consistent dynamics, with the final results appearing natural and harmonious within the original scene, free of any jarring obtrusive artifacts.
Clear Window View
Add String of Lights
Soft Candlelight Glow
Vincent Van Gagh
Figure S2: Visualizations of 4D dynamic scene editing with TINKER. Our results DyNeRF quences showcase both object-level and scene-level edits, demonstrating high-fidelity, harmonious, and temporally coherent results.
C.3 ADDITIONAL ABLATIONS
Ad Advantages of depth as condition in scene completion. Existing methods predominantly rely on diffusion models to generate new views. Some approaches (Gao et al., 2024) condition on ray maps to generate missing views, while others (Wang et al., 2024b) directly interpolate between the first and last frames using diffusion models. We systematically compare our scene completion model with both types of methods to demonstrate its superiority. First, train scene completion model
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Framer
Ray Map
Depth
Qualitative comparisons of different scene completion methods. Conditioning depth produces results with one-to-one corresponding camera poses, while achieving superior geometry and detail preservation without being restricted to first and last frame inputs.
Figure S3:
conditioned on ray maps in a manner similar to prior work (Gao et al., 2024). We observe that, due to the lack of explicit geometric constraints, this approach often results in noticeable geometric distortions in the generated views. Moreover, it often generates outputs that violate the constraints of the camera ray map, or inconsistent results with original scenes. As for the second approach, interpolating between the first and last frames suffers from both visual artifacts and a lack of camera pose information for the generated intermediate views, making them unsuitable for downstream 3DGS optimization. Moreover, this strategy imposes rigid constraint input format, limiting the input to the first and last frames and thereby reducing flcxibility editing scenarios. In contrast, our method leverages depth to provide strong geometric guidance and maintain tight alignment with the corresponding camera poses. It also supports arbitrary reference views, not limited only to the first and last frames. As evidenced by the results in Figure S3 and Table S1, our method significantly outperforms the aforementioned baselines in both quality and consistency.
Advantages over existing depth-conditioned diffusion models. Several recent works, such as VACE (Jiang et al., 2025), have explored using depth as condition for video generation. However, these methods are typically trained on natural video datasets and do not pay much attention 3D- related data. Furthermore, these methods treat the depth condition as a reference rather than a con- straint that must be strictly enforced. Consequently, while they are capable of producing high-quality depth-guided video generation, the resulting outputs often fail to strictly adhere to the provided depth constraints, which not desirable in 3D settings where geometric consistency is essential. We com- pare our approach with the latest existing depth-guided video generation methods VACE (Jiang et al., 2025) in Figure S4 and Table S1, which shows that our model better understands camera motion and more faithfully respects depth constraints. In addition to supporting depth-guided video genera- tion, VACE also allows controlling the editing region via masks. We compare our method against both of these capabilities. For depth-guided video generation, the results exhibit clear multi-view inconsistencies. For mask-based editing, the results also show certain fine-grained multi-view incon- sistencies, and the quality preservation in detailed regions is significantly lower than that achieved by our method. We attribute this success to removing the text prompt input and training the model on 3D-aware datasets to strictly follow the provided depth.
C.4 APPLICATIONS
Quality refinement. We observe that our model can effectively enhance the quality of rendered results, as this refinement can also be regarded as a special variant of editing. As shown in Figure S5, by employing a prompt such "enhance the quality." in the rendering, yielding outputs with sharper details. This refinement process allows our method to improve the overall fidelity of the 3DGS reconstruction.
can guide the model to refine blurry areas
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Editing: Change the color of the bike to red
Original Scene
VACE
VACE-Mask
Tinker
Figure S4: Comparison with VACE in both depth- video generation and mask-based editing. Our method demonstrates superior multi-view consistency and better preservation of fine details.
Table S1:
Quantitative comparisons different conditions and different depth-guide video gener- ation models. Our approach achieves the best overall performance.
Imaee Similarityt
DINO
Aesthetic
VACE
0.760
0.916
5,833
VACE+Mask
0.799
0.954
6.118
Framer
0.773
0.973
6.227
Ours-Ray-Map
0.783
0.931
6.214
Ours-Depth
0.821
0.978
6.586
Video reconstruction. TINKER reconstructs high-quality videos from just the first frame and the corresponding depth sequence. As shown in Figure S6 and Table S2, our approach achieves tempo- rally coherent reconstructions with sharp details and faithful geometry, demonstrating its effective- ness across both qualitative and quantitative evaluations. The evaluation dataset consists of 1,000 videos sampled from OpenVid-1N (Nan et al., 2024), and ensure these videos for evaluation are not included in our training set. We observe that the latest model (Jiang et al., 2025) taking the first frame and depth inputs, while capable of producing high-quality video generation, fails
Before
After
5.8
Asstarkk
Prompt: Enhance the quality
Figure S5: TINKER demonstrates the ability to refine blurry regions, recovering sharper structures and finer details while maintaining overall visual consistency, as this refinement can be regarded as a special type of editing.
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to accurately reconstruct the original video content. In contrast, method significantly improves reconstruction accuracy, achieving faithful recovery of both geometric structure and appearance. Furthermore, because the model operates directly grayscale depth maps, it naturally supports a compact video representation in which an entire video can be stored as its grayscale depth se- quence and single first frame. This property highlights the potential of our method not only for high-fidelity video reconstruction but also for efficient video compression and storage.
Table S2: Quantitative comparisons of video reconstruction with first frame and depth as input.
PSNRT
SSIM
VACE
16.635 31.869
0,331
TINKER
0.941
GT
Our Recon
GT
Our Recon
GT
Our Recon
Figure S6: TINKER demonstrates the capability of high-quality video reconstruction with only the first frame and depth maps as input.
Test-time optimization. Most importantly, since method does not require per-scene finetuning, it supports test-time optimization: users can iteratively experiment with different reference views, replacing generated views of low quality in the last generation process with newly generated ones using the scene completion model. This iterative process leads to higher-quality 3D editing results.
C.5 BASELINES WITH FLUX
To rigorously isolate the contributions of our TINKER pipeline, we investigate whether prior base- line methods could achieve comparable results simply by substituting their U-Net-based 2D editors with the more advanced FLUX model. We implemented and evaluated FLUX-adapted versions of baselines, concluding that such a naive substitution is unviable.
Specifically, the FLUX-adapted methods we implement include:
Instruct-GS2GS--FLL An adaptation of iterative dataset optimization methods similar to Instruct-NeRF2NeRRt
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DGE-FLUX and GaussCtrl-FLUX Adaptations of methods reliant multi-view feature alignment, such as Feature Injection or Feature Alignment. As FLUX is a flow-based architecture, replace operations requiring DDIM Inversion with the compatible RF- Solver where necessary.
Our experiments revealed that simply replacing the editor not only failed to produce significant improvements but also introduced critical failures and prohibitive costs, shown in Table S3 and Figure S7.
Vincent van Gogh
Fail
Prior methods' attention designs are tightly coupled with the U-Net architecture, causing severe performance degradation when the editing model is directly replaced.
Figure S7:
Table S3: Quantitative comparisons of FLUX-adapted different methods. Simply replacing U-Net editors with FLUX is unviable, leading prohibitive costs and even failed due to critical architec- tural mismatches.
Quality
Computational Cost
CLIP-dirt
DINO
Aesthetic
On 24G GPU
Avg. Editing Timel
DGE-FFUX
FAIL
FAIL
FAIL
No
FAIL
GaussCtrl-FLUX
FAIL
FAIL
FAIL
No
FAIL
Instract-GS2G--FLUX
0.107
0.932
6.082
No
133min
Ours-one-shot 0.143
0.958
6.214
Yes 15min
Ours-few-shot
0.157
0.959
6.338
Yes
15min
Prohibitive computational cost. For iterative methods like GIsSS-et-SS2GS--LUXX the com- putational overhead became impractical. The high intrinsic inference cost of the large FLUX model, when compounded by the numerous dataset refinement iterations required by the Instruct- NeRF2NeRF paradigm, results in an extreme computational burden that renders the approach unvi- able for practical application.
Critical problems from different architectural design. A more fundamental failure occurred in methods like DGE-FLUX and GaussCtr-FLLUX. These methods are foundationally dependent on multi-view feature alignment (e.g., DGE's STAut, GaussCtrl's 's attention alignment), which fails due to an architectural incompatbillity between U-Net and DiT. In U-Net, attention features generally do contain dominant, explicit positional encoding. This allows cross-view attention mechanisms to match features based purely content similarity. For example, View 1 can correctly attend to the same object patch at position P2 in View 2. DiT, positional encoding is deeply integrated into the attention blocks. When naively adapting the baselines, the introduction of the DiT's strong PE fundamentally disrupts the model's ability to understand seman- tic content during multi niew alignment with prior attention designs. This prevents the model from correctly aligning the object, causing the multi-view feature alignment and subsequent editing to fail.
object patch at position yi in
In conclusion, the attention designs prior methods are architecturally coupled to the U-Net design, and their core mechanisms cannot be trivially upgraded by substituting the 2D editor. Conversely,
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TINKER demonstrates superior quality and flexibility, supporting 2D, 3D, and 4D editing, which traditional methods are unable to achieve.
C.6
USER STUDY
To quantitatively assess the subjective quality of editing results, further conduct a compre- hensive user study. We evaluate user preferences along three key dimensions: text similarity (how well the edited scene matches the semantic meaning of the target text prompt), editing quality (the visual fidelity, realism, and coherence of the edited region), and multi-view consistency. Participants are asked to rate each result on a scale from to 5, where higher scores indicate better results. In total, collected 50 evaluation user studies where participants scored 20 different editing results. As shown in Table S4, the results clearly indicate that our method is strongly favored by users and better aligns with their subjective preferences compared baseline approaches.
Table S4: We conducted user study across three dimensions: text similarity, editing quality, and multi-view consistency. The results indicate that our method better aligns with subjective user preferences.
Text Similarityf
Editing Quality
Multi-view Consistency!
GaussCtrl DGE
3.62
3.79
3.87
3.78
3.44
3.73
EditSplat
3.17
2.92
3.42
TIP-Editor
2.57
2.89
3,56
4.38
4.49
4.31
Shos
4.52
4.61
4.55
D LIMITATIONS AND DISCUSSIONS
Although our method significantly lowers the barrier to 3D editing, it still has some limitations. First, dataset is synthesized using the foundation model, which occasionally results in inconsistencies certain fine details across samples. Second, since our scene completion model operates under depth constraints, it is currently unable handle edits involving large geometric deformations. We leave these limitations as directions for future work. Nevertheless, despite these issues, we demonstrate strong performance wide range of scenarios, offering an effective solution for high-quality, efficient, and user-friendly 3D editing.
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Clipart
Illustration Style
Illustration Style
Clay Art
Beige Rug
Wood
Floor
Glossy
White
Fumiiuure
Figure S8: Additional one-shot editing results without per-scene fine-tuning.
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Fallen Snow Around Table
Polished Marble
Fallen Autumn Leaves
Chalkbourd
Zentangle Pattern
Impressionist
Western
Comic
Neat Short Beard
Camouflage
Face
Figure S9: Additional one-shot editing results without per-scene fine-tuning.
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Clay
Paper-cut Style
Ukiyo Style
Winter
Change Road to River
Add Fallen Leaves
Frost
Figure S10: Additional few-shot editing results without per-scene fine-tuning.
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Fallen Snow Around Table
Watercolor
Futurist
White
Slippers
Vincent
Gogh
Pencil
Sketch
Black
Tuxedo
Collar
Figure S11: Additional few-shot editing results without per-scene fine-tuning.
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Prompt for Editing Generation
You are an expert prompt generator for image editing. Your is analyze an input image and output list diverse, concise editing prompts. You must follow these rules strictly:
1. Generate prompts covering various styles, weather, lighting, color changes.
2. Do not suggest edits that severely distort or warp the shape of main objects.
Return object-level editing prompts only when there objects images.
4. Keep each prompt short and direct.
5. Every prompt MUST end with: keep the other things unchanged",
Your response must only contain prompts. Omit all text, including introductions and conclusions.
Generate editing prompts for this image:
Change style Vincint Van Gogh, keep the other things unchanged.
Change the weather to snowy winter, keep the other things unchanged.
Change the house to golden, keep the other things unchanged.
Change the wall to blue, keep the other things unchanged.
Figure $12: Input to multi-moda large model for the generation of editing prompts.
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8-bit Pixel
Sketch
Western Comic
Toy Style
Sunset
Figure S13: Examples from our synthesized multi-view consistent editing dataset. The dataset covers a wide variety of editing, including different weather conditions, lighting setups, and artistic styles.
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Original Images
Edited Images
white paint
Vincent van Gogh
snowy winter
Makoto
Shinkai
Japanese
manga
Figure S14: Additional scene-level editing data examples. Our data covers different viewpoints and styles.
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Original Images
Edited Images
pop art
cyberpunk
blue
interior
design
clay
comic
manga
Figure S15: Additional scene-level editing data examples. Our data covers different viewpoints and styles.
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Original Images
Edited Images
black carphone
silver silk
blue and
white porcelain
frozen ice
carbon
fiber
Figure S16: Additional object-level editing data examples. Our data covers different viewpoints and styles.
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Original Images
Edited Images
pine wood texture
clear water
glass
polished chrome
brushed
gold
Figure S17: Additional object-level editing data examples. Our data covers different viewpoints and styles.
33


Reviews:
[{"review_id": "Ak0pHy0fwU", "reviewer": "Reviewer_c8ha", "summary": "This paper, Tinker, has proosed a general-purpose 3D editing framework. Given the reconstructed 3DGS, the proposed editing pipelien will perform video depth estimation, muti-view consistent editsing, and scene completion sequentially. The edited 3dgs is of high quality under comprehensive evaluation.", "questions": "1. Since VDM has made great progress recently, 3D-aware VDM like Lyra, Gen3C, and VIST3A has also shown good capability. I wonder whether the proposed pipeline can be radically replaced by the VDM-based pipeline.\n2. Besides, 3D foundation models are getting better now. Rather than directly working on the 3DGS, I wonder whether the proposed pipeline can be improved to incorporate 3D VFMs like VGG-T / AnySplat to facilitate easier 3D reconstruction editing.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The proposed method is efficient, reasonable, and offers high quality.\n2. The writing is good.\n3. The proposed dataset will be very helpful to this field.", "weaknesses": "1. The main issue is that this method looks very complicated, though it is necessary to make the 3D editing feed-forward. Still, too many components are involved in this process.\n\nOverall, I think this is a good paper and worth acceptance. I just encourage the authors to think of the next step and tackle this task in a more elegant way.", "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": "1SQP2tOT7s", "reviewer": "Reviewer_KqtQ", "summary": "This paper introduces a framework for 3D editing that operates without per-scene fine-tuning. The approach consists of two core components: a multi-view consistent editor, built upon a large-scale image editing model and a multi-view image editing dataset, and a scene completion model that generates dense edited views. The method is evaluated on the Mip-NeRF-360 and IN2N datasets with qualitative and quantitative results.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces a one-shot or few-shot approach for 3D editing.\n2. This paper proposes a generalizable pipeline for 3D editing.\n3. The paper is well-structured and easy to follow.", "weaknesses": "1. For the multi-view image editing model, when dealing with views that have large variations, does the multi-view consistency of the edits decrease? If so, could these inconsistencies be further propagated and amplified by the subsequent scene completion model?\n2. Since the scene completion model relies on geometric information like depth maps, in few-shot or even one-shot settings with large view variations, is it prone to introducing more hallucinations or geometric distortions to fill in the missing information?\n3. Although the method eliminates per-scene finetuning, its overall editing time, as shown in Table 1, does not present a significant advantage over some baseline methods. This suggests that the inference cost of the models involved might be a bottleneck.\n4. In the supplementary video \"Edited_Novel_View_Rendering.mp4\" for the IN2N person scene, noticeable artifacts can be observed around the edges of the edited object. What is the primary cause of these edge artifacts?", "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": "WPtnEfm5R2", "reviewer": "Reviewer_yNST", "summary": "The paper introduces TINKER, a framework for 3D editing that eliminates the need for per-scene diffusion model optimization, making it highly efficient compared to traditional methods. TINKER achieves multi-view consistency from as few as one or two edited images, enabling precise edits across different viewpoints.", "questions": "As with weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The visual results are impressive and demonstrate the effectiveness of the proposed method.\n2. The use of a video model for 3D editing is an innovative approach. With video generative priors, TINKER is the first method capable of jointly editing both 3D and 4D scenes.", "weaknesses": "1. There is an over-claim of contributions. Many baselines, such as DGE and GaussCtrl, also do not require fine-tuning the diffusion model. Therefore, this should not be considered a unique contribution of TINKER.\n2. The majority of the baselines use InstructNerf2Nerf or ControlNet as the base 2D editors, whereas TINKER utilizes the FLUX model. It is unclear where the true improvement lies: is it in the advanced 2D editing model, or is it in the proposed pipeline? What if these baselines were equipped with the FLUX model?\n3. AIGC tasks, such as 3D editing, necessitate a comprehensive user study to assess its performance in terms of human preference and subjective quality.\n4. More visualizations of the collected training dataset should be presented to give readers a clearer understanding of the data and its characteristics.", "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": "qT0IYQUDi0", "reviewer": "Reviewer_RuqL", "summary": "This paper proposes a feedforward 3D scene editing pipeline and introduces a multi-view consistent image editing dataset. The method adopts a state-of-the-art large image editing model to generate sparsely edited views, and then employs a finetuned video model to reconstruct the scene based on the estimated depth maps of the original scene and the sparse edited views. The results demonstrate strong multi-view consistency, and the approach does not require per-scene optimization.", "questions": "Based on the hypothesis mentioned in the weakness section, could the authors provide further insights or explanations regarding this limitation? Specifically, can the proposed model support large geometric deformations? If not, what are the potential directions to improve or extend the framework to handle such cases? I would be happy to hear the authors’ thoughts on this.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper proposes a feedforward pipeline for 3D scene editing, which differs from previous per-scene optimization-based editing methods.\n2. By leveraging the power of 2D editing models, Tinker preserves the identity and consistency of multi-view sparse images; moreover, by exploiting the capability of a video diffusion model, it achieves precise reconstruction by concatenating depth maps and sparse views with full attention.\n3. The writing is clear and easy to follow.", "weaknesses": "The method appears to struggle with structural editing of scenes. In particular, it is difficult to perform large geometric changes or significant deformations. This limitation arises because, during the scene completion stage, the approach relies on the depth maps of the original videos. Such dependency introduces inconsistencies between the edited views and the original depth maps when large deformations occur, which likely leads to degraded reconstruction quality.", "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)