Nano3D: A Training-Free Approach for Efficient 3D Editing Without Masks
Authors: Junliang Ye
Shenghao Xie
Ruowen Zhao
Zhengyi Wang
Hongyu Yan
Wenqiang Zu
Lei Ma
Jun Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Computer Vision
3D Editing
3D Generation
Flow
Image Editiing
Primary area: generative models

Abstract: 3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits practicality. To address these challenges, we propose \textbf{Nano3D}, a training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view renderings, and further introduces region-aware merging strategies, Voxel/Slat-Merge, which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves superior 3D consistency and visual quality compared with existing methods. Based on this framework, we construct the first large-scale 3D editing datasets \textbf{Nano3D-Edit-100k}, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models.

Published as a conference paper at ICLR 2026
NANO3D: A TRAINING-FREE APPROACH FOR EFFI- CIENT 3D EDITING WITHOUT MASKS
Junliang Ye*
Shenghao X
Ruowen Zhao'
Zhengyi Wang'
Hongyu Yan
Wenqiang 5.2
Lei Ma
Jun Zhu 1.3f
'Tsinghua University
Pekingg University
ShhengShu
*HKUST
$CASIA
User: replace the chicken with dog
FlowEdit
Editing Prompt
User: Removing the choir
User:
Voxel Merge
Holding sword
Replacing the cogle hesc with lion hoad
SLat Merge
3D assets
Input
Nano3D
Output
Task-1 :Reevving
Task 2:AAdding
Replacing
Figure 1: Highly-consistent 3D objects edited by Nano3D. Our framework supports range of training-fre free and part-level tasks especially on shape, including removal, addition, and replacement, while only requiring users to provide source 3D objects and instructions, without any mask.
ABSTRACT
3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail preserve unedited regions. Most methods rely editing multi-view render- ings followed by reconstruction, which introduces artifacts and limits practical- ity. To address these challenges, we propose Nano3D, training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view render- ings, and further introduces region-aware merging strategies, Voxel/Slat-Merge which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves su- perior 3D consistency and visual quality compared with existing methods. Based this framework, construct the first large-scale 3D editing datasets Nano3D- Edit-100k, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models.
INTRODUCTION
Generative models for 3D asset creation have made tremendous progress Lai al. (2025); Chen et al. (2025b 2024e); Wang et al. (2023). leading to widespread applications across entertainment, robotics, and healthcare. In particular, recent rectified flows (reflows) Liu al. (2022). such as TRELLIS Xiang et al. (2025). achieve high-quality 3D object generation by embedding heteroge- neous representations into unified latent space while explicitly disentangling geometry and ap- pearance. Beyond generation, editing (i.e., revising the intended region while keeping other regions unchanged) also valuable users usually need to refine existing assets rather than regenerate entirely new ones, which requires multiple unpredictable iterations to obtain satisfactory result.
"Equal contribution Corresponning author.

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For example, TRELLIS generate diverse plausible appearances easily with style modeiiedd text or image prompts, such as texture and material, but fail to reliably repeat identical geometries.
In image editing, increasing number of powerful models have recently emerged, including GPT- 4o Hurst et al. (2024), Flux.1 Kontext Labs ct al. (202)). and Nano Banana Fortin et al. 2025).
A closer look the evolution of these models reveals clear three-stage development paradigm.
Stage introduced training-free image editing algorithms/ Hertz et al. (2022). which demonstrated the feasibility of editing without model finetuning. Stage 2 focused on the automatic construction of large-scale. high-quality paired editing datasets, providing the foundation for supervised learn-
ing Brooks al. (2023). Stage leveraged these datasets train feedforward image editing models capable of real-time inference and high fidelity generation.
In contrast, 3D object editing still remains bottlenecked in the initial stage (i.e., algorithm). Specifi- cally, existing methods, such as those based on Score Distillation Sampling (SDS) Sella et al. (2023) or the "multi-view editing then reconstruction" paradigm Qi et al. (2024), struggle maintain con- sistency across views or attributes and usually demand time-consuming optimization. This leaves us wondering: can 3D objects be edited versatilely, efficiently and consistently in a training-free man- ner using only pretrained generative models, as achieved in 2D images? Resolving this problem will allow 3D object editing to enter a virtuous cycle of data expansion and training models capable of flexible asset customization, thereby accelerating the whole field toward maturity like 2D images.
We propose Nano3D, a training-free free 3D editing algorithm designed for constructing paired 3D edit- ing datasets. Drawing inspiration from the training-fres frren 2D editing method FlowEdit Kulikov et al. (2024), Nano3D leverages the first stage of TRELLIS to generate an iterative trajectory from input edited voxel representations, thereby enabling efficient training-free 3D editing.
To further enhance source consistency between the original and edited objects, we introduce a region-aware merging strategy, Voxel/Slat-Merge, applied after TRELLIS's two-stage geometry and appearance editing. Based on simple connectivity analysis, this strategy adaptively identifies mod- ified voxel regions in the edited 3D object and integrates them back into the original object. This effectively merges the edited content while preserving the structure of unedited regions.
Building on the Nano3D algorithm, we design an efficient pipeline for large-scale construction of 3D editing datasets and generate a high-quality dataset of 100,000 samples Nano3D-Edis 100k.. Our work addresses two long- standing gaps in the 3D editing domain the lack of training- free editing algorithms and the absence of large-scale tthereyy laying solid foundation for the third stage of 3D editing: training feedforward models under 3D editing supervision.
Overall, our contributions can be summarized follows:
We make the first attempt to introduce FlowEdit to 3D object editing, demonstrating that the powerful priors of 3D object generative models can also support effective training-free editing (like 2D images)
We propose Voxel/Slat-Ne Merge, a region-aware merging strategy that automatically preserves source consistency in the non-edited regions of 3D objects.
We develop user-friendly 3D editing framework, Nano3D, which achieves state-of-the-ar edit- ing performance without requiring any manual masks.
Building upon Nano3D, we curate the first large-scale 3D editing dataset Nano3D-Edit-10m comprising over 100,000 high-quality samples to support further research and development.
2 RELATED WORK
2.1 2D IMAGE EDITING
With the advent of large-scale 2D generative models, image editing has shifted from manual pixel- level operations to controllable semantic-level manipulation. Early approaches modify noisy latents via inversion to balance new details with original Prtuctrrs/Ment et al. (2021) I): |Mokady et al. (2023); Abdal al. 2019), while others finetune generative models curated editing pairs to enable in-
struction following Brooks et al. ) Wei et al. (2024); (Sheynin et al. (2024). Localized editing
has also been explored through attention map manipulation Hertz et al. (2022): Tumanyan et al.
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Couairon al. 2022), , and adapters have been introduced to inject additional conditions for enhanced controllability Ye et al. (2023); Ju et al. (2024); Mou et al. (2024). More recently, rec- tified flows (reflow) Liu et al. (2022): Esser al, (2024) have enabled high-fidelity synthesis with few sampling steps. To support reflow-based editing, RFSolver Wang et al. (2024) approximates ODEs via higher-order Taylor expansion while preserving structures through attention replacement, whereas FlowEdit Kulikov et al. (2024) introduces an inversion-free strategy by interpolating be- tween sampled noise and the source image.
2023
2.2 3D EDITING
Compared to 2D image editing, maintaining spatial consistency is substantially more challenging in 3D. Many approaches adopt score distillation sampling (SDS) Poole et al. (2022) optimize
3D representations using gradients from pretrained 2D diffusion models Sella et al. (2023) L et al.
al. (2024); Chen et al. (023). Others edit multi-view images and reconstruct them with large reconstruction models (LRMs) ) Qi et al. (2024); Chen et al. (2024a);
(2024): et al. (2024c);|Palande))
[Barda et al. (2025); Huang et al. (2025): Erkoç et al. (2025); Bar-On et al. (025); Zheng et al.
(2025 : Li et al. (2025); Gao et al. (2024). directly manipulate triplanes
bridge between 2D
and 3D Kathare et al. (2025); Bilecen et al. (2025). Inspired by InstructPix2Pix Broooks et al. 2023),
several works construct paired 3D) editing datasets for supervised training Ye et al.
(2025)
; Xu al.
(2023)
To enable finer control, diverse conditions such as sketches Mikaeili et al.
(2023)
; Liu et al.
2024 Guillard et al. 20211, part-level masks Chen al. (2025a); Yang et al.
2025a b): Wang
et al. 2025a). and point-based dragging Chen et al.) (2024b); Xie et al. (2023); Lu et al. (2025)
have been explored. Beyond manipulating individual objects, extending these editing capabilities (0 complex 3D scenes has also garnered significant attention Chen et al. (2024d) Wang et al. (2025b).
In this work, we unlock their potential for versatile and consistent 3D editing a training-tree and user-friendly manner.
3 PRELIMINARY
3.1 FLOWEDIT
FlowEdit Kulikov et al. (2024) is a text-guided image editing method tailored for text-to-image flow models. is characterized by being inversion-free. optimization-free and model-agnostic. Rather than relying on traditional inversionn-rcoostrruiioo processes that often introduce distortion, FlowEdit constructs an ordinary differential equation (ODE) trajectory in the latent space from the source prompt to the target prompt. This trajectory enables direct evolution of image representations over the velocity field. By leveraging a weighted combination of the source and target velocity fields, FlowEdit ensures shorter editing path and stronger structural preservation throughout the editing process. Therefore, given a source image, along with the two conditions before and after editing (e.g,, the text describing the image single view rendered from the 3D asset), the pretrained generative models can adopt FlowEdit to output the target image. We provide a more detailed descriptions of FlowEdit's editing process in Sec. A.13
3.2 TRELLIS
TRELLIS generates 3D objects through a two-stage geometry-appearee decoupling pipline. In stage 1, it predicts sparse structure from noise, which represents geometry by voxel occupancy grid {ss) where s; E {0. 1........ N 1)3, the grid spatial length and N is the number of active voxels. In stage 2, TRELLIS predicts a structured latent (SLat) based on s, which further incorporates appearance information, represented by {(,,, / where 2; RRC is the aggregated multi-view DINOv2 feature for the i-th voxel, with C as the feature dimensionality.
METHOD
4.1 OVERVIEW
common approach is to edit rendered images a 3D object and reconstruct with generative model, but this often breaks geometric consistency. To address this, we introduce FlowEdit into
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Overview
Stagel:
Stage2: Latent-lusse Structure Edit
Editing Drosage, Reasore
Youudfox
Edit
Merge
3D aosets
Edit Nose!
Flow
Edit Stat
Edited 3D ussets
Stage1
Output
Imput
10
Strvel
Stage
Merge
Flow
0 1 10
10
Encode
Mergo
Imape
Edit Emsays
Emberdiings
Sre-SLat
The Nano3D pipeline. The original 3D object is voxelized and encoded into sparse structure and structured latent respectively. Stage modifies geometry via Flow Transformer with FlowEdit, guided by Nano Banana-edited images. Stage generates structured latents with Sparse Flow Transformer, supporting TRELLIS -ini-eeeeneett appearance editing. Voxel/Slat-Merg further en- sures consistency across both stages before decoding the final 3D object.
Figure 2:
To further ensure geometric and appearance con- sistency, we propose Voxel/Slat-Merge (Sec. 4.3) which detects edited regions and integrates them with unedited ones. Finally, we present a trainin--fres user- user-friendly pipeline (Sec. (4.4) that also supports large-scale dataset construction. By combining FlowEdit with Boxel/Slat-Merge Nano31) achieves geometrically consistent and semantically faithful 3D object editing within TRELLIS.
the first-stage generation TRELLIS (Sec. 4.2
4.2 FLOWEDIT
Inspired by FlowEdit's success in 2D image editing, extend it to 3D object editing by integrating into TRELLIS stage 1, leveraging the pretrained generative prior to establish an editing path between source and target objects instead of starting from noise. The input is the source 3D voxel grid Sarc. TRELLIS applies the FlowEdit algorithm to output the edited target voxel grid Stgt, by treating the rendered front-view image Care and the modified target front-view image Ctyt as the source control condition and the target control condition respectively. Specifically, this process is divided into the following two stages:
Front-View Image Editing. Given the editing instruction tzt and the rendered front-view image Carc of the source 3D object, we first utilize the advanced 2D image editing model Nano Banana to edit Care, thereby obtaining the edited front-view image Cagt of the target 3D object.
Voxel Editing. Subsequently, we consider the voxel grids Sare: Stgt of the source and target 3D objects, and define the noise-to-voxel generation trajectories Pt. gr. With Cares Ctst serving as conditions, the velocity fields of (Pt, Carc), off (gt, Ctgs) of these trajectories are predicted by the pretrained Flow Transformer from TRELLIS stage 1. Then FlowEdit adopted establish an editing trajectory s, with timestep & [0, by aligning Pt. de to start from the same sampled noise e ~ ();
8t Sec + gt Pt:
2 S5e + (v, Ggg) Ug (pr, Carc)) dt.
(2)
where Ps = (1 t)s + te, 9: = (1 t)stgt + tc.
Such rectified flow-based trajectory gradually moves toward $u Stgt under the semantic guidance provided by the velocity field differences, and preserves the source geometry consistency by starting
from 81 Sare rather than directly generates Styt from random noise.
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4.3 VOXEL/SLAT- MERGEE
Voxel-Merge. We observe that the voxel edited by FlowEdit sometimes still exhibit minor geometry inconsistencies with the source 3D object, egg., when editing a dragon to remove its wings, the result may not only modify the wings but also inadvertently alter other unrelated regions. To this end, we further introduce a region-aware merging strategy, VoxeI-Merge, which takes the source 3D voxel grid Sare and the FlowEdit-edited voxel grid ffc as inputs, and outputs the final target voxel grid Stgt that merges the desirable edited regions from fee with the non-edited regions from Sare. Specifically, it defines a difference map g via an element-wiss wise XOR operation between 8snc and 8fc:
g( (ire(1) O @f((1) = { if Sre (1) # Sfe(1),
Vi
O, if s = 8 (e))
where all modified elements of Sfe are explicitly marked with 1, and connectivity analysis is em- ployed on such elements to group them into distinct regions. Regions larger than the threshold т are then selected, separating the desired edited regions from those irrelevant modifications. Next, binary mask m initialized, with elements corresponding to the selected regions set to 1 and the rest to O. Finally, another XOR operation is performed between the mask and Sarci
8 src ① + Stgl-
(4)
thereby transferring the correct edited regions onto Sare with the non-edited regions preserved.
Now that the geometry consistency is sufficiently achieved, we proceed to feed the merged voxel grid Segt logether with the edited front-view image Ctge into TRELLIS stage 2, leveraging its pretrained Sparse Flow Transformer to output the target SLat 2tgt- To similarly ensure the generated SLat Zggt are consistent with Sare encoded from the original 3D object, we also introduce SLat-Merge by reusing the mask m during the Voxel-Merge stage and performing:
Zave T
Zigh'
Therefore, SLat-Merge outputs the final merged target SLat Zigt by combining the appear- ance features of both the non-edited and desirable edited regions from the input 2rre and Eagts preserving the appearance consistency.
4.4 NANO3D
As illustrated in Fig. Nano3D builds upon TRELLIS to enable decoupled geometry and ap- pearance editing of 3D objects. The input object is voxelized and, along with DINOv2 Oquab et al.) (2033) features, encoded into a structured latent representation via VAE|Kingma & Welling (213)) Meanwhile, we use Nano Banana with the front view of a 3D asset and editing instructions as input to generate the edited front view. In TRELLIS-Stane
1, we replace the standard flow iteration with FlowEdit, which takes the source object's voxel and the before/after front views as input, and outputs the edited voxel. We then apply Voxel-Merge ensure geometric consistency. In TRELLIS-SAee -SSage 2, the edited voxel and edited front view jointly guide TRELLIS to generate the final SLat. At this stage, we further adopt Mlat-Merge to guarantee both geometric and texture consistency. Finally, the edited SLat is decoded by the VAE to reconstruct the target 3D) object.
Data Construction Pipeline. As illustrated in Fig. 3, we extend Nano3D by constructing a complete and streamlined 3D editing data generation pipeline. The process consists of the following stages:
1. Image Sampling from Existing Datasets: We sample views from publicly available 3D asset datasets Xiang et al. (2025); Deitke et al. (2022). For each asset, the frontal view is selected the editing target.
2. Instruction Generation via VLM: An editing instruction is automatically generated ing the vasion-language model Qwen-VL.-2.5 Bai et al. (202)) based on three predefined prompt templates:
Add: add <something> to <somewhere>
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Qwen2.5-m 5-
Replace <the chickere
with dags
No
Sample From Image Dataset
Data Filter
Trellis
Nano3D
Image
Source Mesh
Edited Mesh
Figure 3:
Data Construction Pipeline. The figure shows our pipeline. first sample images from the dataset and prompt Qwen2.5-VIL LL to generate editing instructions by completing templates. Trellis then generates 3D meshes from the images. Finally, the image, instruction, and mesh are fed into Nano3D, and the resulting 3D assets are filtered for quality.
Remove: remove <something> in <somewhere>
Replace: replace <something> with <something>
The model fills in these templates with visual context from the image to produce diverse and semantically grounded instructions.
3. 3D Asset Generation via TRELLIS: Given the selected image, we use TRELLIS to reconstruct the corresponding 3D asset. Instead of using the original mesh, choose to regenerate the source mesh via TRELLIS for two reasons: (1) obtaining the structured latent (sLat) from the original mesh requires rendering ~150 views, which inefficient; (2) the reconstructed sLat still diverges from the original mesh due to the inherent loss in TRELLIS's VAE encoding. Using the TREILLIS-eeconstruceed mesh ensures consistency and reduces computational overhead.
4. Image Editing via Nano-Banana or Flux-Kontext: The generated instruction input into Nano-Banana or Flux-Konters synthesize the edited target image.
5. 3D Editing via Nano3D: The original 3D asset, the source image, and the edited image are fed into Nano3D, which outputs edited 3D asset.
5 EVALUATION
5.1 SETUP
Implementation Detail. Our method is implemented on TRELLIS. The sampling step is fixed 25, and FlowEdit is configured with mmaz = 15, Pmmin = 0, and Nang = The CFG guidance scales for ef (p:) and of (q:) are set to 1.5 and 5.5, respectively, with À set to 0.5. For both Voxel-Merge and Slat-Merge, is set to 100. For the construction of Nano3D-Edie --i---iitkk 100k, employ 32 A800 GPUs for inference, utilizing the Qwen2.5-vl-72B API to generate editing instructions and Kontext perform image editing operations. The creation of each editing pair required approximately five minutes, and empirical observations revealed two key findings: first, the vast majority of failed cases originated from errors in the image editing stage, whereas successful adherence to instruc- tions at this stage led to very high success rate in the subsequent Nano3D editing process; second, the predominant computational cost arose from the Flexicube module, which consumed nearly 4.5 minutes per pair, while the preceding steps required only about 30 seconds. Based on these observa- tions and in order to further reduce computational overhead, we adopted a storage strategy which only the SLAT (Structured Latent) representation and the voxel sum of each asset are preserved, thereby allowing users to flexibly decide whether to directly train on SLAT or to employ Flexicube to convert SLAT into explicit GLB meshes for downstream applications. To improve dataset qual- ity, we use Qwen2.5-VL-4 VL-7B to automatically filter edited images based on instruction compliance. Non-compliant samples returned the pool for re-sampling.

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Add shield the penguin's left hand.
Remove one leaf from the right side of the stem.
Remove the backpack from the character's back.
Replace the shield in the character's hand with sword.
Remove the handle from the lamp.
Remove the chimney from the train
Replace the jacket with yellow down coat
Add satellite dish on top of the house.
Remove the dragon horn headpiece from the character's head.
Figure 4: Qualitative results. We present three edit types object removal, addition, and replace- ment. In each case, Nano3D confines changes to the target region (red dashed circles) and produces view-consistent edits, while leaving the rest of the scene unchanged. Geometry stays sharp and textures remain faithful in unedited areas, with no noticeable artifacts.
Baseline. We select three representative state-of-the-an methods as baselines: Vox-E based on SDS, Tailor3D based "multi-view editing then reconstruction", and TRELLI, which leverages a RePaint- method. For all baselines, we strictly follow their original implementations and use the official codebases obtain the results reported in this paper.
Dataset. Our Nano3D-Edit-1Nn dataset comprises two sources of image data: images collected from the internet and rendered views from the Trellis-500K dataset. During dataset construction, follow the methodology of 3D-Alpaca Ye et al. (2025), employing Qwen2.5-VL. to automatically annotate 3D assets and classify them accordingly. We then perform class balancing across dis- tinct categories. ultimately selecting 100K image samples. We select 100 representative cases from the F-do3IDFEdit- 100k dataset for the experiments and demonstrations this section.
Metric. We systematically evaluate the edited 3D objects from three perspectives: source structure preservation, target semantic alignment, and generation quality. For source structure preservation, assess non-edited regions against the original 3D object using Chamfer Distance (CD) Fan et al. (0177). For target semantic alignment, we employ the DINO-I adherence to the target edited image. For generation quality, rendered multi-view images to measure fidelity and diversity.
al. (202T) metric to quantify FID Heusel et al. (2017)
5.2 MAIN RESULT
Qualitative Comparison. As shown in Fig. Nano3D not only strictly follows editing instruc- tions but also maintains perfect structure consistency with the source 3D object across multi-view images. In contrast, Tailor3D introduces noticeable geometry distortions and appearance artifacts. Vox-E produces results that are overly blurry, smoothed, and misaligned with the target semantic. TRELLIS, though showing relative improvements, still suffers from several issues, such local detail corruption, shape enlargement, and incorrect orientation. These findings demonstrate that our method delivers impressive and steady visual effects beyond the reach of existing methods.

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Replace the stock green natebooks with stack books,
Make the backpack the carrying bigger.
Holding
Remove the wings.
Replace the headbar E with crown.
Input
Tailor3D
Vox-E
Trellis
Nano3D
Figure 5: Qualitative comparison. Our method achieves the best editing performance with faithful instruction semantic alignment and perfect original structure consistency across multi-view images.
Table Quantitative comparison. Our method achieves the best structure consis- tency, semantic alignment with the target edited image, and generation fidelity.
Table 2: User study. Given that most users favored TRELLIS and Nano3D, the results for Tailor3D and Vox-E are omitted from the table for clarity. As shown in the table, our method is strongly preferred by participants, significantly outperforming TRELLIS.
Method
CD4
DINO-It
FID
Tailor3D
0.037
0.759
140.93
Vox-E
0.782
117.12
Method
Prompt
Visual
Shape
Algn.
Quality
Preserv.
TRELLIS
0.019
0.901
49.57
TRELLIS
32%
21%
5%
Instant3DiT
0.014
0.879
56.73
Nano3D
68%
79%
95%
Nano3D
0.013
0.950
27.85
Quantitative Comparison. As shown in Tab. Nano3D outperforms all baselines, achieving the lowest CD and FID and the highest DINO-I score, indicating superior structural consistency, perceptual quality, and semantic alignment, seen in Fig. 5
User Study. To assess editing quality and usability, conducted study with 50 participants. Each round presented the original 3D object, task instructions, and results from Tailor3D, Vox-E, TRELLIS, and Nano3D. Participants selected the best method based Prompt Alignment, Visual
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Src Voxel
Thres=100
Thres=50
Thres=30
Figure 6: Ablation study on т. The leftmost voxel represents the pre-editing state, with the edit- ing instruction being to remove the wings. The three voxels on the right correspond to the masks generated during the voxel-merge stage r 100. = 50, and = 30 (from left to right). As observed, when = 100, the detected mask most accurately aligns with the editing regions, while lower values include irrelevant non-editing areas.
Quality, and Shape Preservation. As shown in Tab. Nano3D received the highest preference across all criteria, demonstrating superior semantic alignment, visual quality, and shape fidelity. For clarity, Tailor3D and Vox-E results omitted, as user choices mainly favored TRELLIS and Nano3D.
v.s. 3D-Alpaca. High-quality 3D editing requires consistency in both 2D image appearance and 3D structure-that is, the rendered images before and after editing should remain coherent, and the 3D assets themselves should preserve structural integrity throughout the editing process. The 3D-Alpaca dataset lacks both aspects, leading to significantly lower data quality compared ours. To quantify this, randomly sample 500 edited pairs from each dataset and evaluate text-image alignment using CLIPScore Hessel et al. (2021) and ViLT R- R-Precision Kim al. (2021). Specifically,
VLM to infer the caption of the edited asset based the original asset's caption and the editing instruction. As shown in Table our Nano3D-Edit-100 consistently outperforms 3D-Alpaca across all metries.
Table 3: Semantic alignment comparison between NANO3D-EDITT- and 3D-Alpaca.
CLIPScore
ViLT R-Precision R@5
ViLT R-Precision R@ 10
3D-Alpaca Nano3D-Edit-10m
28.42
33.6
40.2
39.71
45.3
52.4
5.3 ABLATION STUDY
Voxer/Slat-Merge. We sequentially validate the effectiveness of Voxel-Merge and Slat-Merge strategies. As shown in Fig. 7 relying solely FlowEdit leads to geometry misalignments and deformations, accompanied by missing, blurred, and distorted appearances, resulting in obvious in- consistencies with the original 3D object. Incorporating Voxel-Merge substantially improves the overall performance, restoring geometry and enhancing cross-view global consistency, but leaving appearance issues unresolved. With the additional incorporation of Slat-Merge, local visual quality is further enhanced, and appearances exhibit greater consistency before and after editing. These re- sults indicate that our methods effectively exploit the advantage of in 3D objects, ensuring more reliable consistency.
decoupling
Ablation on т. We further compare different values т, as shown Fig. The leftmost voxel represents the pre-editing state, with the editing instruction being to remove the wings. The three voxels on the right correspond the masks generated during the voxel-merge stage for r 100, = 50, and 30 (from left to right). As observed, when = 100, the detected mask most accurately aligns with the editing regions, while lower values include irrelevant non-editing areas.
CONCLUSION
In this work, we present Nano3D, a training- free and user-friendly framework for localized 3D ob- ject editing, supporting operations such object removal, addition. and replacement. By integrating FlowEdit into the TRELLIS pipeline and introducing region- awware merging strategies (Voxel/Slat- Merge), Nano3D achieves geometrically consistent and semantically faithful edits. Extensive

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replace the hat with helmet
remove the chimney
replace chicken with deg
add monkey on the chair
Source Mesh
FlowEdit
FlowEdit
+VxeeMerge
FlowEdit
VoxelMerge SLatMerge
Figure 7: Ablation study on Voxel/SLat- Our methods sequentially ensure geometry and appearance consistency, demonstrating their complementary roles.
periments demonstrate state-of-the-arr performance across diverse editing tasks. Furthermore, we construct Nano3ID-Edit-100k. the first large-scale dataset tailored for 3D editing, enabling future research on feedforward DiT-based editing models.
Limitation. Nano3D demonstrates strong performance in 3D editing tasks, but has the following limitations: it supports only localized edits; the VAE TRELLIS introduces reconstruction loss; and the overall performance is constrained by TRELLIS's generative capacity. We view these limitations as important directions for future research.
ETHICS STATEMENT
We propose Nano3D, a training-free framework for precise and coherent 3D object editing without masks. Building on this framework, we construct the first large-scale 3D editing dataset, Nano3D- Edit-100k 100k, which contains over 100,000 high-quality 3D editing pairs. This dataset is built upon publicly available data and will be released as an open resource for the research community. We declare no competing interests.
REPRODUCIBILITY STATEMENT
This paper presents a training-free 3D editing pipeline and, based on it, constructs 100K-scale 3D editing dataset. We will release all code, annotation scripts, and the dataset as open-source resources.
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A APPENDIX
A.1 LLM USAGE STATEMENT
The authors used ChatGPT-5 exclusively for grammar checking and language polishing of the manuscript text. All technical content, experimental design, data analysis, and scientific conclu- sions are the original work of the authors. The LLM was not involved in generating scientific ideas, conducting experiments, or interpreting results.
A.2 TEXTURE EDITING
As shown in the Figure g include experiment on texture editing. Specifically, we render front-view image of the source mesh and perform texture edits on the rendered image using an image editing model Labs et al. (2025). We then feed the edited image together with the original mesh into Trellis to produce textured Gaussians. Finally, we bake the resulting texture onto the input mesh. This procedure enables texture editing while preserving geometric consistency.
Input Mesh
Editing Mesh
Plastic, futuristic design transparent elements.
Low-poly design with sharp, angular shapes.
Rusty, vintage design with an abandoned, post-apocalyptic feel.
Figure 8: Nano3D perform texture editing without altering the geometry.
A.3 DEFORMATION EDITING
As shown in the Figure consistency.
Nano3D supports deformation- based editing while preserving overall
A.4 SCENE EDITING
As shown in Figure 1O Nano3D successfully handles simple scene edits, with indoor examples on the left and outdoor examples on the right. For complex scenes, however, Nano3D alone is insufficient because the SLAT representation in TRELLIS cannot encode high-complexity inputs. To address this limitation, we adopt block-wise editing strategy. We partition a complex scene into uniform blocks, each constrained identify the block containing the target region and apply Nano3D only to that portion. Using this strategy, we are able edit an entire scale scene. As shown in Figure [T] we replace building with garden and subsequently replace the fountain within the garden with sculpture. These results demonstrate that our Nano3D supports coherent and effective scene-level editing.
level of complexity that TRELLIS can encode. We then
A.5 ABLATION STUDY ON FRONT- VIEW SELECTION IN NANO3D
To investigate how the choice of front-vies rendering influences Nano3D's ' editing performance, we conduct the ablation study illustrated in the Figure [3 Starting from mesh with a fixed pose, render four distinct viewpoints and apply Nano3D editing to each view independently. The results demonstrate that Nano3l produces plausible edits even when the input is fully rear-facing view
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Input Mesh
Editing Mesh
Input Mesh
Editing Mesh
Ruise the character's 's arms outward into T-pose
Raise the character's arms forward and bend the elbows slightly, bringing both hands together in front of chest
Figure 9: Nano3D is capable of deformation; it can modify a character's pose as required while maintaining consistency.
Input Mesh
Editing Scene
Input Mesh
Editing Scene
Add television the table.
Remove the helicopter from garden.
Figure 10: Two examples of scene editing using Nano3D are shown: the left illustrates indoor scene editing, while the right illustrates outdoor scene editing.
Input Scene
Editing Scene-1
Editing Scene-2
Transform the house into garden, with fountuin of garden.
Replace the founsain in the garden with sculpture.
Figure 11: Two examples of block- wise complex scene editing using Nano3D are presented. In the left example, a building within the city is replaced with garden featuring central fountain. In the right example, the fountain in the garden is replaced with sculpture.
(second last row), despite a minor reduction in consistency. For the remaining three srie-oriented views, the method yields highly coherent and consistent outcomes. These observations indicate that our method is robust to variations in the input viewpoint.
A.6 MULTIVIEW 3D-FLOWEDIT
To address the challenges posed by complex occlusions and asymmetric objects, we incorporate multi-view guidance mechanism into the FlowEdit pipeline. Specifically, we independently per- form image editing on both the frontal and rear views of the input 3D assets. These edited images are then jointly injected into FlowEdit guidance signals. As shown in Figure 12 this strategy significantly enhances the robustness of Nano3D. For instance, the system successtully achieves si- multaneous editing of spatially distinct Seatures-sune back- tterrby condensing a conventional two-stage workflow into a single-pass process. Conse- quently, this approach substantially improves the overall robustness of Nano3D..
fist the front and backpack the
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Input Mesh
Editing Mesh
Replace the weapon in the character 's right hand with red boxing glove, and add waist bag the character 's back.
Illustration of the multi-view guidance mechanism in Nano3D. To handle complex clusions and asymmetric objects, we independently edit the frontal and rear views of the input 3D asset and inject them jointly into the FlowEdit pipeline. This strategy enables simultaneous editing of spatially distinct features-suce as adding a fist on the front and a backpack on the back-in single-pass process, significantly enhancing the system's robustness.
Figure 12:
A.7 ADAPTIVE THRESHOLD ESTIMATION
To address the limitation where a fixed threshold - = 100 fails to capture regions for fine-grained edits-such the subtle transformation of human ears into elf ears-we propose an Adaptive Threshold Estimation algorithm. This method automatically adjusts t according to the granularity of the specific editing task.
Specifically, our algorithm dynamically determines the optimal threshold during mask generation. We analyze the connected components of the potential edit regions; the areas of all connected components are smaller than the current threshold, we iteratively halve the threshold value (r <- [r/2]). This process repeats until at least one connected component exhibits an area greater than or equal to ensuring that relevant editing regions are captured.
As illustrated in Figure 14 we demonstrate this using Nano3D to replace human ears with elf ears. With fixed threshold of 100, the algorithm fails to generate a mask because the areas of the largest connected components are [33, 30, 13, 6, 6, 6]. all of which fall below 100. This results in an unchanged output. In contrast, our adaptive approach detects this discrepancy and iteratively reduces f from 100 to 50, and subsequently to 25. At r = 25, the regions corresponding to the ears (areas 33 and 30) successfully identified, generating accurate edit mask.
In summary, this adaptive strategy effectively extends Nano3D's capability to handle fine- ane-grained structural changes. By eliminating the need for manual hyperparameter tuning, it ensures robust performance across editing tasks of vastly different scales.
A.8 ADDITIONAL EVALUATION RESULTS FOR THE EDITING DATASET
In this section, we provide a comprehensive analysis of the Nano3D-Edit 100k dataset statisties and fine-grained performance evaluation. This analysis covers the distribution of editing types, the complexity of editing instructions, and per-category semantic alignment metrics.
Distribution of Edit Types. To ensure the model's robustness across diverse editing scenarios, curated the dataset to maintain balanced composition of editing operations. As shown in Table 4 the dataset comprises approximately 40% addition instructions, with removal and replacement op- erations each accounting for 30%. This balanced distribution prevents the model from overfitting to specific editing patterns.
Instruction Complexity Analysis. We assess the difficulty of the natural language instructions to understand the dataset's complexity profile. To ensure an objective and scalable assessment, we employed Vision-Language Model (VLM) an evaluator. The VLM was prompted score the
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"Remove twin tails."
Mesh
Input View
Editing Mesh
Editing
Editing Mesh
Editing Mesh
Figure 13: Ablation study on the impact of front-view selection in Nano3D. The results demon- strate that Nano3D produces plausible edits even when the input a fully rear-facing view (second to last row), despite minor reduction in consistency. For the remaining three side-oriented views, the method yields highly coherent and consistent outcomes. These observations indicate that our method is robust to variations in the input viewpoint.
Input Mesh
Editing Mesh
replacing human ears with elf ears
Figure 14: An example our adaptive threshold estimation algorithm shown the figure, where the task is to replace human ears with elf ears. The results indicate that, with the incorporation of this algorithm, Nano3D can adaptively handle editing tasks exhibiting large variations spatial scale, without requiring any manual hyperparameter tuning.
difficulty each instruction on a scale from 0 (simplest) to 5 (most complex) using the following prompt:
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Input Mesh
Editing Mesh
Input Mesh
Editing Mesh
PrEditor3D (cvpr2025)
Nano3D
Replace the rock on the with pizza.
Figure 15; We conduct visual comparison between Nano3D and PrEditor Erkog al. 2025 Given that PrEditor is not open-source, we utilize the results displayed on its official project page for this comparison. As illustrated in the figure, PrEditor exhibits noticeable lack of multi-view consistency (highlighted by red boxes), which is particularly evident in the rear of the car and the side mirrors. In contrast, our method achieves highly consistent editing results. These observations demonstrate that Nano3D significantly outperforms the baseline in preserving global coherence.
Table 4: Distribution editing types in the Nano3D-Edit- 100k dataset. The dataset maintains balanced distribution across three primary editing operations: adding, removing, and replacing objects.
Edit Type
Count
Share (%
Add
40,000
40,0
Remove
30,000
30.0
Replace
30,000
30.0
Total 100,000
100.0
"You evaluator for 3D object editing tasks. Assess the difficulty of the following instruction on a scale from to 5: Instruction: (instruction). Output strictly only the single digit."
The resulting distribution is presented in Table 5 The statistics indicate that the majority of instruc- tions (53.87%) fall within the moderate difficulty range (Score 3). The distribution also retains significant portion of simpler instructions (Scores 0-2) and complex instructions (Score to simu- late real-worke usage scenarios, while extreme cases (Score and 5) remain rare.
Per-Category Semantic Alignment. Global metrics often mask category-speciff performance nuances. Therefore, we report the fine-grained semantic alignment results in Table
Metric Note: It important emphasize that the ViLT R@5 and @ 10 metrics in this table are calculated via intra-class retrieval (retrieving the target from candidates within the same cate- gory). This presents significantly more challenging fine-grained discrimination task compared to the global evaluation metrics reported in the main text (Global R R@5; 45.3), where candidates drawn from the entire dataset.
The results reveal that our method achieves highly stable and robust performance on object-centric categories, such as Food and Personal Item. Conversely, performance is relatively lower categories characterized by high visual ambiguity structural complexity, such Weapon, Building, and Plant. These findings highlight the challenge of fine fnn--gained semantic alignment in complex structural domains and suggest meaningful directions for future optimization.
Analysis:
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Table 5: Distribution of scores in the dataset (Total N 100, 000).
The majority samples fall
into Score 3, while extreme scores (1 and 5) are rare.
Score
Count
Percentage (%)
12,982
12.98
56
0.06
14,674
14.67
3
53,870
53.87
18,390
18.39
5
28
0.03
Total
100,000
100.00
Table 6: Reports the per-category semantic alignment evaluation. It is worth noting that the R@5
and R@ 10 metrics in this table are calculated within each category (intra-class retrieval), which
presents more challenging fine-grained discrimination task compared to the global evaluation met- rics ::: 45.3) reported earlier.
Semantic Alignment Metrics
Category
Count
CLIPScore
ViLT R@5
ViLT @@ 10
Human
20,755
40.17
34.2
44.4
Weapon
11,021
35.05
14.8
20.2
Furniture
10,442
39.12
33.6
45.4
Personal Item
10,277
38.70
39.0
52.2
Animal
10,186
39.59
32.4
42.6
Vehicle
9,376
38.47
28.4
36.6
Building
9,005
37.56
26.6
35.6
Electronic Device
5,283
38.52
29.6
39.2
Plant
4,441
38.67
29.0
37.8
Food
3,622
39.61
45.6
57.0
A.9 MORE VISUALIZATION RESULTS
In Fig. T6 we showcase additional editing results covering three types of operations: addition, removal, and replacement. As illustrated in the figure, method, Nano3D, effectively preserves both geometric and textural consistency of the 3D assets before and after editing.
A. CHOICE OF 3D REPRESENTATION VOXEL VS. VECSET
To investigate the compatibility of FlowEdit with different 3D representations, we conducted com- parative experiments using Hunyuan2.1 HHnyuannD et (2025), which utilizes a VecSet-based representation Zhang et al. (2023). Our analysis reveals a fundamental incompatibility between the global nature of VecSet and the localized editing requirements of FlowEdit.
Specifically, we observed that VecSet representations tend to entangle geometry globally; modifying local region via flow matching often disrupts the overall structural integrity, leading fragmented or collapsed geometries. In contrast, the voxel-based representation employed in TRELLIS naturally supports spatially localized modifications. The voxel grid allows FlowEdit to manipulate specific regions without propagating gradients to unrelated areas, thereby preserving the structural fidelity of the unedited parts. This structural decoupling makes voxel representations significantly more suitable for free-frre localized editing tasks.
A.11 IMPACT OF GUIDANCE CONSISTENCY ON 3D EDITING
The quality of training-fre free 3D editing is heavily dependent the multi-view consistency of the 2D guidance images. We analyze this dependency by evaluating FlowEdit on datasets with varying levels of 2D consistency, such 31>-Alpaca Ye et al. 2025
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As illustrated in Figure 17 inconsistent 2D renderings (e.g., discrepancies in object scale or position between pre- and post-edit views) pose significant challenges. When the guidance images lack spatial aligmment, the flow trajectories (p; and 9:) diverge, causing the editing algorithm to fail in establishing a coherent velocity field. Under such conditions, the model either ignores the editing instruction (dominance of the source structure) or produces geometric artifacts (complete loss of structure), depending on the magnitude of the noise injection (Nmaz). These findings underscore the necessity of our Nano3D pipeline, which ensures rigorous 2D-3D alignment prior the editing stage.
A.12 THE PROMPT USED TO GENERATE EDITING INSTRUCTION FROM THE RENDERING
As shown in the Table. 7 we present an example of constructing editing instructions with a VLM. A strict template is used to constrain the VLM and prevent it from generating instructions beyond Nano3D's capabilities.
A.13 FLOWEDIT
Given a source image Esrc X sre and target image Tigt ~ Xtgt with corresponding conditions Care: Ctgt, their flow matching-basee Lipman et al. (203) generative trajectories pt, qt are respec- tively defined as
Pt = (1 - t) + teares
9: = (1 ligt letgt-
where / E [0, 1] is the timesteps, éxro: Etgt .N(,,)) 1) are the randomly sampled noise, and these
trajectories are differentiated with to obtain the velocity fields ve Care), Up (e(yt, Cegt):
dpt
ve(Pe, Care)
Esrc Parcs
(8)
dt
dgt -Egt Figt-
(e Ctgt) di where the trajectories infer the images reversely and gradually by integrating the velocity fields from noise. In practice, the real velocity fields ve (pe, Care), Us (4t,, Cags) cannot be computed directly. Existing approaches convert them conditional velocity fields Lipman al. (2023) and train model 0 to predict of (Pt, Care), uy (qt, Ctgt).
Unlike the generation from noise image, FlowEdit directly defines editing trajectory s, from the source image to target image by first aligning the starting noise of their generative trajectories (i.e., e Care Etgt). which is based on the assumption that most regions of the source and target images are same, except for the edited regions. Then, we reformulate by combining Eq. and Eq
Ft Fare + q: Pc-
(10)
where ø1 Esee and vi Ftgt, and its velocity field is
dx 0e dt
vy (qt, Crgs) - (pe, Carc).
(11)
Therefore, this editing trajectory starts from the source image at = 1 and gradually moves toward the target image at = 0, guided by the semantic provided the velocity field differences in Eq, [I] At each 6, the computation of u; requires of (Pt. Care), of (gt, Ctge), and predicting them further depends on Pt. 4.. which usually obtained by inverting the source image to the t-th timestep Wang et al. (022)). However, the additional inversion steps incur significant time cost. In contrast, FlowEdit shifts to constructing Pt using the forward process in Eq. 6 and computes ge using Eq. I0:
P: = (1 t)xere + teg.
(12)
+ Esrc-
(13)
where e ~ (0, I) is the sampled noise at the t-th timestep. This substantially improves the efficiency of the editing process, and since both the computation of qt, ef (qt. Ctgt) start from the intermediate latent of source image rather than the original noisy latent used in generation, the edited target image retains the structure consistency with source image.
The complete editing process is assembled in Alg. T In this paper, we leverage FlowEdit for 3D object editing, transforming the edited entity from the image x to voxel s.
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Algorithm 1 Sampling mode of FlowEdit
Input: Tare, Caze; Cryi
Output: Figt
Init: æ1 Jirr
for i + to
Et I)
Pt - (1 11-th Isa + tEL
qt 2t Ism Pt
1/ (pt, Carc) <- Modelo (pt, Cares t)
" (9t, Ctgt) Modelo (gt, Ctgt: t)
21 o (gt
Ut (Ptx Carc)
It-1 f Ft. + vidl
Return: Tigt
Table 7: The prompt used to generate editing instruction from the rendering
Editing Action
Prompt
Given an image, generate short "replace" type editing instruction in the format:
Replace [original object/pard/patter/ with [new element]
Additional Requirements:
The [original object/part/pattemn must already exist in the image.
It can be an entire object, a part of an object, a geometric shape, or pattern.
The [new element] should clearly dilfer from the original and fit naturally into the image. It can be another object, different part, new shape, text, new pattern. Avoid replacing with intangible elements (e-g., gases, smoke, light, shadow). Do not change colors replacements must not involve altering the color of any existing element.
Replace
General Rules:
Keep the instruction short and clear. No exira explanation or description.
Given image, generale short 'remove; type editing instruction in the lormal:
Remove (object/part]
Additional Requirements:
The [object/part] must already exist in the image. It can be the whole object or specific part of an object (e.g.. handle of a cup, branch of tree).
Remove
The removal should be visually noticeable and affect the composition of the image. Avoid removing intangible elements (c.g., light, shadow, gases, smoke).
General Rules:
Keep the instruction short and clear.
No extra explanation or description.
Given an image, generate short "add" type editing instruction in the format: Add [element] [location]
Additional Requirements:
The [location] can be:
an existing object in the image. a position within the image (e.g.. top left, bottom center), specific part/position of an object (e.g.. handle of cup, roof of house). The [element] should blend naturally into the image and not appear abrupt. It an object, text, pattern, other visual addition. Avoid adding gases, smoke. or other intangible elements.
Add
General Rules:
Keep the instruction short and clear. No extra explanation or description.
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Replace the bucket buildozer blade.
Remove the veil from character's head.
Remove the feathers from the bird.
Add statue to the top of the pyramid.
Replace the chimney with bell.
Remove the hat from the character's head.
TA
Add white cushion to the seat of the chair.
Add padlock he chest.
Remove the red flog from the top of the chest.
Add basket the front of the scooter.
Add shield warriar's hand.
Remove bottom drawer.
Replace bottom part lomp with rectangular stone base.
Replace the front wheel with wooden wheel.
Add trousers to the character.
Open the wooden door from
carrot the snowman's mouth.
Add white flag to mast of the
the archway.
We present additional editing results involving addition, removal, and replacement. Edited regions are highlighted with red dashed circles. As shown, Nano3D achieves high editing consistency, preserving geometry and texture outside the edited areas.
Figure 16:
image
Edited image
Source image
Edited image
example1
example2
Figure 17: A bad case sampled from the 3D-Alpaca Ye et al. (2025) dataset shows that its image consistency before and after editing is poorly maintained.
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Reviews:
[{"review_id": "y7wN6B0GFy", "reviewer": "Reviewer_wt7j", "summary": "This paper proposes Nano3D, a training-free 3D object editing pipeline that plugs FlowEdit into TRELLIS’s first-stage voxel generator and then preserves untouched content via region-aware merging at both voxel and SLAT levels (Voxel-Merge / SLat-Merge). Nano3D reports the best quantitative performance among baselines. A user study (n = 50) also found that participants prefer Nano3D across prompt alignment, visual quality, and shape preservation.  Lastly, the paper further introduces Nano3D-Edit-100k, a 100k-pair dataset built with an automated pipeline.", "questions": "* In Figure 5, the scale of the rendered object seems to change compared with Trellis (smaller). Is it because of the method or the plotting issue? In addition, why does Trellis show some degeneration on the objects compared with Nano3D?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* **Simple and effective approach**: This paper mainly focuses on keeping non-edited regions, and the proposed approach is simple but effective. Compared to directly extending FlowEdit to TRELLIS, analyzing issues (such as changing connected regions) makes sense, and the Voxel & SLat merge helps to resolve this.\n* **Impressive empirical results**: Nano3D leads on CD/DINO-I/FID (Table 1) and is preferred in the user study (Table 2); ablations show Voxel-Merge and SLat-Merge contribute additively. From the qualitative results, the non-edited parts are clearly maintained without harming the generation quality.\n* **3D Editing Dataset**: They specify a practical 100k-pair pipeline, achiving better alignment than 3D-Alpaca. Such a dataset and tool may be helpful for future work.\n* The overall writing and illustrations are clear to follow and understand.", "weaknesses": "* The method uses \"input 3D object’s front view\" as a source condition. Thus, for the edits on the unseen part, it may lead to problems. In addition, while merging helps preserve the rest, the 3D editing quality may hinge on the quality of that 2D edit. It is good to see that the paper itself notes many failures originate in the 2D editing stage, but there’s no controlled study of viewpoint sensitivity (e.g., edits specified from non-frontal views or multi-view edits). A better analysis of the method's robustness will help to understand the boundary of the method.\n* The method highly relies on the Trellis representation, which makes it hard to extend. Specifically, the input must be the object style of Trellis and is generalizable to complex scenes or objects.\n* The compared baselines are too old and narrow. For example, Vox-E is a work from ICCV2023. For an informative comparison, more up-to-date works should be considered, such as PrEditor3D (CVPR 2025) or recent works.", "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": "qUWx3stk2D", "reviewer": "Reviewer_oayJ", "summary": "This paper introduces a training-free 3D editing framework based on current powerful generative models like TRELLIS and Nano-Banana. In contrast to prior methods relying on 2D multi-view editing, they adapt FlowEdit into TRELLIS thus mitigating the problem of multi-view inconsistency to some extent. To enhance consistency, the authors propose Voxel/Slat-Merge strategy to preserve unedited regions via connectivity analysis. The framework enables a pipeline for constructing Nano3D-Edit-100k, the first large-scale 3D editing dataset. The paper provides evaluations on qualitative visuals, quantitative metrics (e.g., Chamfer Distance, DINO-I, FID), and user studies to demonstrate superior performance over baselines like Tailor3D, Vox-E, and TRELLIS. The work aims to advance 3D editing toward feedforward models, mirroring the evolution in 2D image editing.", "questions": "1. Generally I like the whole pipeline. But why is the framework called “Nano3D”? What is the motivation behind this? I can’t really decode the meaning behind this. And I don’t think it’s appropriate to include Nano because of Nano-Banana.\n2. How sensitive is Nano3D to the choice of front-view rendering? Could incorporating multiple views in FlowEdit guidance improve robustness for complex occlusions or asymmetric objects?\n3. Nano3D-Edit-100k balances 10 categories, but what is the distribution of edit types (add/remove/replace) and instruction complexities? Could releasing per-category metrics help assess dataset quality?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. **Stable and efficient data generation pipeline:** It effectively adapts FlowEdit from 2D to 3D editing within TRELLIS, demonstrating that pretrained generative priors can enable training-free, inversion-free edits. \n2. **Significant Dataset Contribution:** Nano3D-Edit-100k fills a critical gap in 3D editing data, with automated construction using VLMs for diverse instructions and quality filtering. The comparison highlights the semantic alignment, making it a valuable resource for future supervised models.\n3. **Comprehensive and extensive evaluation.**", "weaknesses": "1.  **Limited novelty**: it is appreciated to smartly integrate pretrained models into such framework. but the novelty is really limited, which introduces some concerns about the contribution to the research community.\n2. **Limited edit operations**: now it only supports static add/removal/replacement, can’t support deformation and so on.\n3.  **The claim of “without mask” is not convincing**. As in the voxel/slat-merging stage, it requires a difference map and a threshold to preserve original structure. This is actually still a mask, which could be easily got because the property of supported editing operations.", "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": "F7b6JVdRed", "reviewer": "Reviewer_zHpX", "summary": "Nano3D is a training-free framework that enables precise and coherent 3D object editing without masks. It combines FlowEdit with TRELLIS for localized edits and introduces Voxel/Slat-Merge strategies to maintain structural consistency between edited and unedited regions.\nAdditionally, the authors present Nano3DEdit-100k, a large-scale dataset of over 100,000 high-quality 3D editing pairs, laying the foundation for future feed-forward 3D editing models.", "questions": "My questions are in the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "[1] The paper clearly communicates its main claims and presents a valuable contribution in the form of zero-shot 3D editing.\n\n[2] The quality of the presented results is also qualitatively impressive.", "weaknesses": "1. The proposed system shows a strong dependency on **FlowEdit** and **Trellis**, but the paper fails to clearly convey the fundamental nature of the models being utilized.  \n   - In this system, *FlowEdit* functions as an **image editor**. What matters is not merely that FlowEdit is used, but rather that **an image editing module** is employed — and its inputs and outputs should be clearly formulated.  \n   - The key insight readers should gain from this paper is the use of a **3D representation**, not simply the use of Trellis. Regardless of whether Trellis or another model is used, it is crucial to **define its input, output, and equations** clearly. In fact, in Section 3.2, there is only a brief mention of `z_i`, with no explanation of what `p_i` or `C` represent.  \n\n2. In Section 4.3, the explanation of **Voxel-Merge** is difficult to follow, as it assumes readers already know all the terms and omits formal definitions, which significantly reduces readability. Specifically, what does `s(i)` refer to?  \n\n3. **Voxel-Merge** is a proposed method, yet the explanation provided in Lines 203–210 is insufficient.  \n\n4. The claim that *\"We obtain voxels that are fully consistent before and after editing\"* is not a factual statement but rather an assertion. Isn’t the method still **dependent on thresholding**?  \n\n5. Overall, the paper gives the impression of **assembling multiple existing models** to perform a single task. I would encourage the authors to describe their ideas in a **more generalized and conceptual form**. As it stands, the paper reads more like a **technical report of an empirical study** than a research paper presenting a novel framework.", "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": "Aix3Z0Mjvs", "reviewer": "Reviewer_dUSo", "summary": "Motivated by FlowEdit and TRELLIS, the authors propose Nano3D, a training-free framework for precise and coherent 3D object editing without requiring masks. The method first uses a closed-source image generation model to edit the front view of the input image (e.g., removing, adding, or replacing objects). It then performs 3D editing on both geometry and appearance, and leverages an XOR operation between sparse structures to effectively filter the regions that require editing. This pipeline ultimately achieves relatively high-fidelity 3D edits.", "questions": "Please refer to the Weakness.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is very clearly written, and the method is easy to understand (except for Section 4.2).\n\nThe visual results are impressive, significantly enhancing the credibility of Nano3D.\n\nThe authors curate the first large-scale 3D editing dataset, Nano3D-Edit-100k, which will be valuable to the research community.\n\nThe ablation studies thoroughly demonstrate the role of each component, and the contribution of each module is both intuitive and theoretically well-motivated.", "weaknesses": "The method relies on FlowEdit, which takes the front-view edited image as input. Does this imply that editing content on the back side of an object is not supported?\n\nThe authors use a fixed threshold τ = 100 to generate the edit mask. However, different editing tasks involve regions of vastly different scales—for example, a subtle edit like replacing human ears with elf ears should likely use a much smaller τ. This suggests that τ is a task-dependent hyperparameter requiring manual tuning, which limits practical usability.\n\nThe mask generation relies on geometry-induced changes in voxel occupancy. Consequently, the method cannot handle pure texture edits that preserve geometry (e.g., changing the color or pattern of an object without altering its shape).\n\nPossibly due to my limited familiarity with FlowEdit, I found the descriptions in Sections 4.2 and 4.4—regarding the integration of FlowEdit and TRELLIS—to be overly brief, which created some difficulty in fully understanding the technical details.", "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)