3DOT: Texture Transfer for 3DGS Objects from a Single Reference Image
Authors: Xiao Cao
Beibei Lin
Bo Wang
Zhiyong Huang
Robby T. Tan
Venue: NeurIPS 2025 poster ()
Keywords: 3D editing
Primary area: applications

Abstract: Image-based 3D texture transfer from a single 2D reference image enables practical customization of 3D object appearances with minimal manual effort.
Adapted 2D editing and text-driven 3D editing approaches can serve this purpose. However, 2D editing typically involves frame-by-frame manipulation, often resulting in inconsistencies across views, while text-driven 3D editing struggles to preserve texture characteristics from reference images.
To tackle these challenges, we introduce \textbf{3DOT}, a \textbf{3D} Gaussian Splatting \textbf{O}bject \textbf{T}exture Transfer method based on a single reference image, integrating: 1) progressive generation, 2) view-consistency gradient guidance, and 3) prompt-tuned gradient guidance.
To ensure view consistency, progressive generation starts by transferring texture from the reference image and gradually propagates it to adjacent views.
View-consistency gradient guidance further reinforces coherence by conditioning the generation model on feature differences between consistent and inconsistent outputs.
To preserve texture characteristics, prompt-tuning-based gradient guidance learns a token that describes differences between original and reference textures, guiding the transfer for faithful texture preservation across views.
Overall, 3DOT combines these strategies to achieve effective texture transfer while maintaining structural coherence across viewpoints.
Extensive qualitative and quantitative evaluations confirm that our three components enable convincing and effective 2D-to-3D texture transfer. Our project page is available here: https://massyzs.github.io/3DOT_web/.

3DOT: Texture Transfer for 3DGS Objects from a Single Reference Image
Xiao Cao
Beibei Lin
Bo Wang²
Zhiyong Huang' Robby T. Tan ASUS Intelligent Cloud Services
1 National University of Singapore
University of Mississippi
{xiaocao, beibei. Lin mus edu
hawk www.rrssrcall...o
[dcahuang, robby. Cnus enu..edu.us.........
Reference Image
Target Scene
Plug-n-Play
IGS2GS
GaussCtrl
Ours
Figure 1: Comparison of 2D and 3D image- based texture editing methods. Prompts "moss- covered table" and "pink plastic bear". 2D methods Plug-n-Play [36] suffers from view inconsis- tency problem; 3D text-driven editing methods IGS2GS (37] and GaussCtrl [41] struggle preserve texture characteristics. Ours faithfully edit the texture, material appearance, and color.
Abstract
Image- based 3D texture transfer from single 2D reference image enables practical customization of 3D object appearances with minimal manual effort. Adapted 2D editing and text-driven 3D editing approaches can serve this purpose. However, 2D editing typically involves frame-by-frane manipulation, often resulting in incon- sistencies across views, while text-driven 3D editing struggles to preserve texture characteristics from reference images. To tackle these challenges, we introduce 3DOT, a 3D Gaussian Splatting Object Texture Transfer method based on a sin- gle reference image, integrating: 1) progressive generation, 2) view-cons new-consistency gradient guidance, and 3) prompt-tuned gradient guidance. To ensure view con- sistency, progressive generation starts by transferring texture from the reference image and gradually propagates adjacent views. View cowsissesstncc gradient guidance further reinforces coherence by conditioning the generation model on feature differences between consistent and inconsistent outputs. To preserve texture characteristics, prompt-tuning-base gradient guidance learns token that describes differences between original and reference textures, guiding the transfer for faithful texture preservation across views. Overall, 3DOT combines these strategies achieve effective texture transfer while maintaining structural coherence across viewpoints. Extensive qualitative and quantitative evaluations confirm that our three components enable convincing and effective 2D-to-31 texture transfer. Our project page is available here: htttts:///aass/// github. /000//0___eaat_ _ebb/
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Transferring texture from a 2D image to 3D object is valuable yet underexplored capability in 3D editing. It enables efficient texture manipulation and benefits applications such as virtual reality, CG films, and 3D games 2 31 techniques, transferring texture from a single 2D image to a 31) object remains challenging due to difficulties in ensuring view consistency and preserving texture characteristics, particularly for unseen views beyond the reference image.
28
39
34
5
Despite advances in 2D texture and 31) editing
2D image-based editing methods [36 46 25
42 perform texture transfer by finetuning a diffusion model (e.g.. DreamBooth 30 Textual Inversion [33 and editing images rendered from a 3D object to create finetuning dataset. The resulting 3D object often suffers from view inconsistency and identity loss due to the absence of constraints enforcing multi-vier coherence and identity 9
g 27 66, especially text-driven ones, guide editing using prompts derived from reference images via visual language models or manual descriptions. However, these prompts are typically coarse and miss fine-grained features, resulting in identity mismatch and inconsistent appearance across views.
preservation, shown in Figure I 3D editing methods [15] 37
41
Motivated by these challenges, we propose 3DOT, novel framework for transferring texture from a single 2D reference image to a 3D object represented by 3D Gaussian Splatting [21]. 3DOT comprises three key components: 1) progressive generation process, 2) view-consistency gradient guidance, and 3) prompt-tuning teappt-ttninn---eeeee -baing gradient guidance. The first two components enforce view consistency, while the third preserves texture characteristics.
In the progressive generation process, we first obtain reference images either by directly pasting the reference image onto the 3D object or by generating candidate views using depth-conditioned model 47 based on the unedited view's depth. The image that best matches the target attributes is then selected. To facilitate prompt tuning and sparse cross-attention, both the unedited training images and the reference images, and project them into the latent space for k-step partial diffusion. The generation begins from the reference view and progressively propagates to neighboring views, guided by sparse cross-attention on previously edited views. This strategy maximizes overlap between adjacent reference images to enforce view consistency.
remove backgrounds from
To enhance view consistency in 3D editing, we introduce vie-consistencyy gradient guidance. The core idea is to guide the diffusion model toward vies-consistent generation by minimizing texture inconsistency features in intermediate outputs. Specifically, we initialize two diffusion modules: one conditioned on reference views via cross- cross-attention, and the other guided only by text prompt. Since cross-attention the only differing component, the discrepancy between their intermediate results captures view-consistency features. During each denoising step, these features are scaled and injected as gradient guidance, steering the generation toward consistent outputs across views.
Since the reference image reveals no texture for unseen views, coarse text prompts often lead to inconsistency. To overcome this, we propose prompt-tuning- based gradient guidance that captures texture differences as additional prompt tokens. Specifically, we compute the difference between reference and unedited images in the CLIP feature space [T. encoding the texture transformation direction. This signal is injected into the diffusion denoising process as gradient guidance, enabling consistent texture transfer across views. The fine-tuned prompt improves style coherence in unseen views while preserving details in the reference view.
We evaluate our method on the face-forwarding (38] and 360-degree 3 datasets. Results show effec-
tive texture transfer with fine detail preservation and strong view consistency, Our key contributions:
3DOT, an image-based 3D Guassian Splatting (3DGS) texture transfer framework that enables efficient and flexible texture editing.
A progressive generation process with view-consistency gradient guidance to address view inconsistency across novel views.
Prompt-tuning-base gradient guidance preserves texture characteristics in seen views and enforces style consistency in unseen views.
Extensive experiments demonstrate that 3DOT achieves state-of-the-art visual quality and quantitative performance.

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Selection
Progressive Gen cenoration
Consistency & Prompt-Tuning Gradient Guidance
Stop
Stup
Snep 2
Stop 2
parh
Phud grasetion EETT
Scokeggndent gaeane
grocken
Amcrabl Thrmmel
C T.).T))
Option Generation
4304
Iterative Finetuning
pou.11
Figure 2: 3DOT. Our framework enables texture transfer from single image to a 3D object. The left panels illustrate the selection of the reference image using generative approach. Then, our method employs progressive generation process guided by view-consistency and Woompt-tuning-bie gradient guidance preserve both cross-view consistency and texture identity. I, T, and T denote the reference set, text prompt, and learned texture difference token, respectively.
2 Related Work
2D Diffusion-based Editing DragDiffusion 32 defines target edits using keypoints and replaces them with reference images. o.TTale-f-TTo--eatteee [46] combines dense DIÑO [7] features and sparse diffusion features by merging reference-view semantics with target-view structures. Plug- and-Play 36 refines fine-grained details by injecting diffusion features into DINO features, while
DiffEditor 25 improves 2D editing precision via differential equation-based sampling with regional gradient guidance. The most relevant work, SwapAnything [[4, leverages DreamBooth (30] and AdaIN [19] to encode source images and maintain style consistency during 2D edits. Although effective for image editing, these methods operate on individual views without enforcing view consistency, highlighting the need for 3D-aware texture editing techniques.
3D Editing Most 3D editing methods leverage 2D diffusion models for guidance and adopt dataset-updating strategies to finetune pretrained 3D scenes. Instnuct-NeRF2NeRF [15] and Instruct- GS2GS [37 use instruct-pix2pix 4 to guide updates for NeRF or Gaussian Splatting. GaussianEdi- tor introduces hicrarchical representations for more stable edits under stochastic guidance. Direct Gaussian Editor (DGE) [8] addresses view consistency via epipolar cross-attention, but its initial independent generation introduces artifacts. GaussCtrl 41 injects features from unedited views to preserve consistency, but this cause the diffusion model retain original textures, limiting editability. StyleSplat (20) achieves texture edits without a generative model but requires altering the 3D representation, which falls outside setting of editing fixed 3D object using single reference image. Methods that ignore view consistency
[5] 37 can be extended with image captioning. 41 inject latent reference features during denoising. However, such modifications offer only coarse control. High-quality, ddentity-preservingg edits require more precise and targeted designs.
while consistency-awant approaches [8
Proposed Method
Fig. 2 illustrates our 3DOT pipeline, consisting of three key modules: 1) a progressive generation process, 2) view-consistency gradient guidance for enforcing texture coherence across different views, and 3) prompt-tuning tertt-ttnnnn--aeee based gradient guidance for preserving object identity.
To obtain the reference image, we either generate depth- condi-oondiiooeed candidates or extract textures by directly cropping texture into object shape in a certain rendered view. the generative approach, users select the candidate that best matches the desired attributes. In the texture-based approach, extracted textures are directly mapped onto the object surface. Following [4.. both reference and unedited images are encoded into latent space to initialize the denoising process. We then apply
3

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prompt-tuning to capture texture differences between the reference and the 3D object, guiding diffusion to preserve identity. Edited views are progressively generated, starting from the reference view. The resulting dataset is utilized to finetune 3D Gaussian model and the above procedure is iteratively conducted [1]] for smooth results.
3.1 Progressive Generation
struggle to balance view consistency and editing flexibility. For example, GaussCtrl 41] conditions diffusion on unedited images to enforce consistency but often retains original textures, limiting editability. DGE avoids reliance unedited inputs but edits non-adjacent views, introducing inconsistencies.
Existing methods [3 37
To overcome these limitations, propose progressive generation process that removes dependency on unedited images and avoids isolated generation steps, achieving both consistency and flexibility.
We first generate reference images by conditioning a generative model on depth maps with background masking to ensure geometric alignment. To improve quality, we refine depth maps using dilated and blurred masks to address black-edge artifacts and apply the original mask to remove redundant content from the outputs.
For a selected reference view T and target view I, we construct a sparse reference set R2 [Tr F( excluding backgrounds. Including 1 maintains local consistency via minimal angular changes, As edits propagate to distant views, errors accumulate. For symmetric case, we can include F(I),. horizontally flipped variant of the reference, to preserve alignment with fewer conditioning views.
The generative model is conditioned on IR using weighted fused cross cross-attention:
WeightedAttn, AAtine, + (1
Σ
wy Atlni,
SER
where e denotes the image that is currently editing, Attn; denotes the attention score between images i and j. and À balances self- and cross-attention.
Partial denoising (Eq.3) begins with the reference view and progressively extends adjacent views. These edited, view-consistent images are then used to finetune the 3D Gaussian model, and the process is repeated iteratively.
3.2 View-Consistency Gradient Guidance
rely on many reference views to maintain consistency. In contrast, our progressive generation begins with a single reference and uses only a few views. To enhance cross-attention effectiveness under this constraint, we propose consistency-award gradient guidance mechanism inspired by classifier-fres guidance [16], modifying the noise estimate [16] to amplify cross-vies view signals without additional training.
Existing generative methods 47 45
30 [3]
Given target view I, and reference set R. [T,, (as in Sec. 3.1 define the denoising prediction as:
(=,,, TRRR) = (2x)))
+ wT (cb T)) ex(2,, R)) + wp (c) c2 T)),
where T is the text prompt, 9 and Ô refer to diffusion with and without fused cross-attention, and WT, are scaling factors. We perform partial denoising
2/
Eg UK (z, T.
(1-1
/a 11k.
a4 x
00///
+
VI a4 11-
T, 1R),
EK
(3)

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Unedited Scene
IGS2GS
GaussCri
Reference Image
(a) Prompt "A golden metallic bear"'
(b) Prompt white table"
Figure 3: Qualitative comparison on 360-degree scenes (material and color edits): Our 3DOT method faithfully edits 3D objects' texture based reference images.
where /. E [0, Nor < Nigh. and or is the DDIM scheduler coefficient. The latent input z' is initialized via:
a1 e V - a1+1c.
++1
In Eq. 2 the second term reflects differences between guidance with and without the unconditional prompt 16], improving adherence to textual instructions. The third term captures variations induced by reference conditioning, and amplifying it strengthens view consistency. This gradient-ban based mechanism guides generation toward coherent multi-view results, enhancing consistency without additional training overhcad.
3.3 Prompt-Tuning-Bassee Gradient Guidance
Text prompts provide only coarse control during diffusion, often leading to identity loss and inconsis- tent texture fidelity. For example, the phrase "stone bear" may yield highly variable textures across different generations. These coarse text descriptions result in view-inconsistent generations and further cause 3D inconsistency.
Among fine-tuning methods 30
learns custom token to represent object-specific textures but requires multiple images to achieve reasonable quality (see supplementary for single-image results).
45 17 13], textual inversion 13]
To address this, we introduce prompt-tuning-base gradient guidance, which reduces the need for multiple images while encoding texture differences more effectively. The key idea learn new token that captures the texture discrepancy between the unedited 3D object and the reference image, and to use this token to guide denoising toward the desired style.
Given the reference image I, and its corresponding unedited rendering i, we compute the texture difference in CLIP feature space:
+1 CLIP (L,.
We initialize the text token T using base prompt (e.g., from VLM), and optimize it by aligning with the texture difference via:
Lelpp cosine (Ai + T).
To reduce misalignment between image and text representations in CLIP space, we apply further prompt tuning in the diffusion feature space, following [26]:
Laift cp(2x. T R) TR.
(7)
5

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Metrics
IN2N
IGS2GS
CaussCtrl
DGE
Ours
Score
0.8917
0.8908
0.8638
0.8572
0.9333
(lexs(Alex) 4
0,1708
0.1683
0.1692
0.1713
0.1166
1-pips(VGG) +
0.1676
0.1594
0.1591
0.1603
0.1247
Vision-GPT f
45.5
52
48
54
76
User study
2.0375
2.4375
2.3750
2.0000
4.5750
Table I: Quantitative results evaluated by CLIP score, VGG-based and Alex-based LPIPS scores, Vision-GPP and user studies given reference image with rendered edited objects. Bold text refers to the best performance and underlined text refers to the second best performance. Detailed results can be found in Supplementary Material Section
The fine-tuned token acts as style-aware prompt enriched by texture differences. While not meaningful in textual form, it encodes critical style information for guiding generation. During inference, we extract and amplify this information at (-step via the difference:
(2x, R)
c c,( T....
and integrate it into the denoising process. The final prediction becomes:
T, (2x)
+ wT (cb(2x, T, R) <a(=x, RR) + wv (c)(zx, T, IR) cg(Ex, T)) + wT (c)(2x, ZA,, T", ..) Te,
(8)
This term strengthens style consistency across views while preserving fine texture details aligned with the reference.
4 Experiments
We compare our method with state-of- of-the-art text-driven editing approaches, including GaussCtri [4]] DGE [8]. IGS2GS (37]. and IN2N [15]. Since these methods rely text inputs,
captioned descriptions as editing prompts to enable image-based 3D texture editing functionality. For quan- titative evaluation, employ AlexNet-hased [22 and VGG-based (33 LPIPS scores (48), CLIP
score (29), and Vision-GPT score [T. supplemented by user studies. Comparisons are conducted across multiple scenes from different datasets to ensure a comprehensive assessment following [4]].
4.1 Evaluation
Quantitative For each edit, we compute AlexNet-basee and VGG-based LPIPS scores, CLIP score, Vision-GPT score, and conduct user studies, summarized in Table T Detailed per-scene scores are provided in the supplementary material. LPIPS and CLIP scores serve as perceptual evaluation metrics, measuring feature similarity between rendered edited objects and reference images. LPIPS ranges from 0 to 1, with lower values indicating better perceptual quality, while higher CLIP scores preferred. Vision-GPT assesses the faithfulness edited textures from the reasoning perspective, scoring from 0 to 100, where higher values indicate better alignment. For user studies, participants are informed of the edited object and required rate the 3D result on scale of to 5, with higher scores reflecting better quality. Quantitative results show that our method achieves the highest performance across all metrics.
Qualitative We present qualitative results of 360-degree dataset in Figs and Figs and includes reference images with texture color or material variations, while Fig features those with complex textures and significant semantic changes. Fig shows the results of ffcce-forward" case. Our method enables more precise 3D object editing without unintended texture leakage between objects. In the 360-degree color and material editing scenario (e.g., bear and table), IN2N [15] and IGS2GS 37 suffer from incorrect color saturation and inaccurate material representation, In bear scenarios (Fig 3ab. their results undersaturated, whereas in the table scenarios (Fig 3b

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Unedited
IGS2GS
GaussCrr
Reference Image
(a) Prompe "An loc frozen borse"
(b) Prompt fire"
(c) Prompt "A lego dinosaur"
(d) Prompt 'A weoden dinosaur'
Figure 4: Qualitative comparison 360-degree (complicated texture edits): Our 3DOT method successfully edits 3D objects' texture to complicated reference textures.
Unedited Scene
IN2N SS
IGS2GS
37
GaussCrrl
A
DGE
R
Ours
Reference Image
(a) Prompt "A person with bawk person"
(b) Prompi with Hulk face"
Figure 5: Qualitative comparison foce-forwarding scenes: Our 3DOT method faithfully edits 3D objects" texture to reference textures and generates the most plausible texture edits for unseen views.
they are over-saturated. None of baseline methods accurately reproduces the intended material attributes (i.e., plastic, moss in Fig. D and metallic in Fig. 3a). GaussCtrl [1] excessively preserves the original 3D object's appearance, resulting minimal modifications due to unedited reference
set. Our method effectively edits textures while achieving realistic material appearances, such as
7

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Wp 0
WR 0
Ours
Ref. Image
(a) Performance when Nip and vog
View
View
Ref. Image
Image
(b) Using Ref.1 for grompt-tunigg guidance and Ref.2 as reference image
Figure 6: Ablation studies on proposed two gradient guidances.
specular highlights the bear and the lush, velvety the table. In moss-covered table scenario [. all 3D baseline methods only attempt to edit texture while ours can also modify geometry to better match the "moss material".
In the large semantic change editing scenario (Fig + baseline methods struggle with significant transformations. DGE [8] often fails, its edits remain nearly unchanged. Its initial independent editing stage leads to inconsistent results and further causes the epipolar attention mechanism to break down in highly dissimilar views, resulting in minimal overall changes. Our method achieves precise 3D object editing with a texture style that closely matches the reference image, enabled by our proposed prompt-tuning consistency guidance, and progressive process. Prompt-tuning preserves intricate texture details, while consistency guidance and progressive generation mitigate blurriness from view inconsistency.
In the face-forward case 5 our method preserves fine details, such as the black lower eyelid and feather-like cloth in the hawk scenario (Fig Sap. IN2N [15] and IGS2GS [37] generate erroneous results due to their independent diffusion process and full diffusion steps. The independent generation process leads inconsistent images, while full diffusion steps cause excessive texture changes and identity loss. Finetuning NeRF with these inconsistent and identity-loss images can result in network collapse. For GaussCtrl 41 and DGE [. particularly in the hawk case, large texture differences break their view-consistency mechanisms, resulting in outputs that retain the original object's appearance instead of the intended modifications.
4.2 Editing Speed
We compare the editing time of our method with two baselines: the Gaussian Splatting model (GaussCtrl (T) and representative NeRF-based model (IN2N (T3]). GaussCtrl requires 15min 47s, while IN2N takes 20h 5 m1 20s. Our approach preserves the efficiency advantage of Gaussian Splatting, with an editing time of 23min 33s, introducing only modest additional overhead from the incorporation of view-consistency and prompt-tuning tuning-basedd guidance. In particular, the additional time required by our guidance components approximately 8min in total. Since our full pipeline typically involves two iterations of image editing, the added cost per iteration is about 4min. We consider this overhead efficient given the improvements texture fidelity and view consistency achieved by method.

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Ablation
LPIPS (Alex)
LPIPS (VGGT)
CLIP Score
W/O prompt-tuning (Sec 3.3
0.0355
0.0679
0.9203
W/O consistency 3.2
3.2
0.0558
0.0844
0,9340
W/O Prog. Gen.
0.1330
0,1290
0.9160
Ours
0.0351
0.0620
0.9445
Table 2: Ablation studies:
Performance evaluation when removing (1) prompt-tuning guidance, (2)
view-consistency guidance, and (3) progressive generation mechanism.
4.3 Ablation Studies
We evaluate the effectiveness of prompt-tuning- based gradient guidance and fiew-consistency gradi- ent guidance from both qualitative and quantitative perspectives. We further illustrate the effectiveness of grompt-tuning-basee guidance by using two distinct images: one serving as the reference image during image generation, and the other the reference for prompt tuning.
Prompt-tuning based Gradient Guidance We first evaluate the effect of removing prompt-tuning- based guidance by setting the guidance scale wT O, as shown in Table Sible@aadd Figure 6aa Without this guidance, the rendered images exhibit blurry surface highlights, and the distribution direction does not align with the reference image.
Additionally, we demonstrate the effectiveness of prompt-tuning guidance by using prompt token trained on reference image Ref-2 edit the 3D object and setting Ref-1 the target reference, as illustrated in Figure Ref-2 depicts bear characterized by sharp metallic edges, whereas Ref-1 shows bear with rusted metallic texture. In Figure 6bb View-1 aligns closely with Ref-1, producing a rendering that closely matches the rusted metal appearance, which demonstrates the effectiveness of the utilized fused cross-attention. Conversely, View-2, which represents an unseen viewpoint without explicit texture guidance, utilizes the learned token to guide the rendering towards the "colorful metallic bear" appearance consistent with Ref-2. This demonstrates the effectiveness of the proposed prompttuning-based guidance when editing parts of the 3D object not visible in the reference image.
View-consistency Gradient Guidance We evaluate the effectiveness of view-consistency guidance by setting WR = O, as reported in Table 2 and illustrated in Figure 6a When this guidance is disabled, the performance significantly degrades, resulting in edited images exhibiting notable undersaturation. This undersaturation primarily arises due to inconsistencies within overlapping regions in the intermediate outputs. These findings underscore the crucial role of vie-cconsistency gradient guidance in maintaining editing quality and color fidelity.
Progressive Generation To evaluate the effectiveness of the progressive generation, we disable progressive view propagation and perform editing using only the initial reference image. The degraded performance (as shown Table highlights the importance of propagating texture across neighboring views enforce view consistency and mitigate artifacts from single-view editing.
4.4 Discussion
Differences from Multi-View Diffusion While existing multi-view diffusion methods designed to generate multi-view consistent 3D objects, the task setting, constraints, and diffusion components in our work are fundamentally different. Specifically, 3DOT takes as input a single 2D reference image and a fixed 3D Gaussian Splatting (3DGS) representation. and transfers high-fidelity texture onto this existing geometry. contrast, multi-view diffusion methods are typically designed to synthesize novel views or reconstruct 3D scenes from few cesture-consisterr input images, without anchoring an explicit 3D representation. These methods rely on implicit geometry learned from priors, which makes them unsuitable for editing tasks that require consistency with a given 3D structure. Moreover, multi-view diffusion approaches assume that texture appearance consistent across views, an assumption that does not hold in texture transfer scenarios where the reference image and the 3D representation exhibit different textures. Directly applying them in this setting leads to collapsed reconstructions, highlighting the need for specialized framework such ours.

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Different Geometric Reference Image Geometry mismatches between the 2D reference image and the 3D object can occur when the reference generated via a hopth-coonitioned diffusion model with small conditioning scale factor. We consider two common scenarios:
Slightly Different Geometry: Minor deviations (c.g., slight pose or scale differences, such standing bear with legs different position) can be effectively handled by 3DOT. (1) During progressive generation, cross-attention extracts texture features while being constrained by the depth map and partially reversed latent features, preserving appearance cues from the original image. (2) During 3D fine-tuning, overlapping regions from adjacent edited views iteratively correct geometric discrepancies and reinforce consistent texture transfer.
Significantly Different Geometry: When the reference depicts a substantially different shape (e.g., rumning bear instead of standing one), transfer quality may degrade. Such cases usually result from failures in depth-conding copth-oondiiioned option generation or from unsuitable user-provided references. In practice, these poor references can be easily identified during the interactive selection stage, and regenerated using our partial denoising strategy with larger depth control factor.
In summary, 3DOT robust to minor geometric mismatches, and mitigates larger discrepancies through reference regeneration and iterative correction during 3D fine-tuning.
Limitation Dark border around edited regions occurs in some cases. We attribute this artifact to language-basee object segmentation methods (c.g.. LangSAM) to generate masks for isolating objects in the reference views. These segmentation methods often include a narrow band of background pixels near object boundaries due to imperfect boundary localization. As a result, during the diffusion- based editing stage, this narrow band misinterpreted as valid texture content, leading to the appearance of dark borders in the final renderings even after applying the soften mask, This may be
addressed by utilizing a more advanced language-based segmentation method [49
43 35 depth
as additional information [44] 50, 10 with semantic 3D representation [[8 12 The quality of
unedited 3D Gaussians also impacts editing performance. Undertrained Gaussian spheres (e.g..
floating Gaussians in empty space) degrade rendered images, disrupting the mask generation process. Incorrect segmentation can result in edits with significantly altered geometry, ultimately causing 3D Gaussian collapse.
5 Conclusion
We introduced 3DOT, a framework for image-based 3DGS texture transfer from a single reference image, underexplored capability in the 3D editing domain. To enable high-quality and view- consistent texture transfer, we proposed three key components: (I) progressive generation, (2) view- consistency gradient guidance, and (3) prompt-tuning-batt effectively address challenges of view-consistency and texture characteristic preservation during transfering process. We evaluated 3DOT across various scenes involving color, material, and large semantic changes. 3DOT consistently outperforms existing baselines, both visually and quantitatively.
gradient guidance. These components
10

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Reviews:
[{"review_id": "n9gMOGS7nT", "reviewer": "Reviewer_qSJ7", "summary": "The paper presents 3D-OTT, a novel method for transferring textures from a single 2D reference image onto a 3D object represented via Gaussian Splatting. To address key challenges like view inconsistency and poor texture fidelity in unseen views, the method leverages a progressive generation process that starts from the reference view and incrementally propagates edits to neighboring views. To improve consistency it introduces a view-consistency gradient guidance to enforce multi-view coherence during diffusion, and prompt-tuning-based gradient guidance that learns a token capturing the texture difference between the original and reference, preserving style across unseen views.", "questions": "1. The method assumes geometric similarity between the reference image and the 3D object. What happens when this assumption fails (e.g., different pose, incorrect scale)?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper addresses a relevant problem in vision. Quality is reasonable, with extensive comparisons and ablations. The method clearly outperforms prior baselines in both quantitative metrics and visual fidelity. However, it’s hard to ignore that much of the method stands on stitching together known ideas with limited methodological novelty. The paper is clear enough with the method being effectively described. The authors ablate the main methodological components showing their impact on performance. Originality is the weakest dimension, build upon existing techniques in a relatively incremental way. On closer inspection results present some artifacts especially in the form of a dark border around the edited area.", "was_revised": true, "final_justification": "The authors’ rebuttal has anddressed my comments and provided an explanation to some minor quality concerns. Give that I’m inclined to recommend acceptance for this paper.", "rebuttal": "We thank the reviewer for the thoughtful comments and the positive feedback. Detailed responses for each question are provided in the following sections.\n\n---\n### 1. Originality Concern\n\nWhile our method builds upon existing diffusion and prompt-based techniques, we believe our contributions offer meaningful and novel extensions for the 3D texture editing setting. In particular, we propose three key innovations:\n\na) Progressive Generation Process:\nTo the best of our knowledge, our method is the first to introduce a progressive generation strategy in the context of 3D texture editing. Unlike existing multi-view diffusion methods that generate all views in parallel, our approach incrementally propagates texture edits across views by using previously edited views as reference inputs. This step-wise refinement promotes spatial coherence, enhances view consistency, and supports integration with 3D Gaussian Splatting.\n\nb) View-Consistency Gradient Guidance:\nWe propose a novel classifier-free guidance format formulation that explicitly incorporates multi-view coherence signals. This is achieved by comparing multi-view-conditional and unconditional noise predictions, allowing us to extract and reinforce view-consistent information during the diffusion process. To our knowledge, this idea has not been applied in diffusion-based 3D editing.\n\nc)  Prompt-Tuning-Based Gradient Guidance:\nWhile prompt tuning is a well-established technique, we propose a novel application: encoding the texture difference between the original object and the reference image as a learned prompt token. We further extract the texture difference information by the proposed equation and apply it to guide the diffusion process in a consistent stylistic direction. We believe this is the first use of prompt tuning for controlling texture appearance in a 3D-aware diffusion editing pipeline.\n\nIn summary, we introduce three new mechanisms that are carefully tailored to address the unique challenges of 3D texture transfer, specifically view consistency and faithful texture preservation, which standard components fail to address. We believe these innovations constitute a non-trivial contribution to the field.\n\n---\n\n### 2. Dark Border Artifact Around Edited Regions\n\nThe dark border artifact is primarily caused by the use of language-based object segmentation methods (e.g., LangSAM) to generate masks for isolating objects in the reference views. These segmentation methods often include a narrow band of background pixels near object boundaries due to imperfect boundary localization.\n\nAs a result, during the diffusion-based editing stage, this narrow band is misinterpreted as valid texture content, leading to the appearance of dark borders in the final renderings. To mitigate this, we apply dilation and Gaussian blurring to soften mask boundaries and reduce the visibility of such artifacts. While these steps alleviate the issue to some extent, traces may still remain in some challenging cases.\n\nWe acknowledge this segmentation field limitation and plan to address it more thoroughly in future work by incorporating more advanced and boundary-aware segmentation techniques.\n\n---\n### Q1: Geometry Differences Between Reference Images and 3D Objects\n\nGeometry mismatches between the 2D reference image and the 3D object can occur when the reference is generated via a depth-conditioned diffusion model with a small condition scale factor. We address this in two common scenarios:\n\n(a) Slightly Different Geometry:\nIn this case, the reference image shows small geometric deviations (e.g., slight pose or scale differences), such as a standing bear with legs in a slightly different position. Our method is robust to such differences due to the following mechanisms:\n* During progressive generation, texture features are extracted via cross-attention while being constrained by both the depth map and the partially reversed latent features which preserve appearance clues of the original image. This encourages edits that align with both the 2D reference and 3D structure.\n* During 3D fine-tuning, view-consistent textures are enforced across adjacent edited views. Overlapping regions from neighboring views iteratively correct geometry discrepancies and reinforce accurate texture transfer.\n\n(b) Significantly Different Geometry:\nIf the reference image shows a completely different shape (e.g., a running bear instead of a standing one), transfer quality may degrade, as discussed in Section 4.4 of our paper. However, such poor references typically arise from failure in the depth-conditioned option generation stage. In practice, users can easily detect these cases during the reference selection stage (which is interactive), and regenerate better references using our partial denoising strategy, which conditions on depth and corrects shape biases during generation.\n\nIn summary, our method is robust to minor geometric mismatches and provides mechanisms to mitigate large discrepancies through reference regeneration and iterative correction during 3D fine-tuning."}, {"review_id": "BZtyH7nhZp", "reviewer": "Reviewer_ZPz8", "summary": "This paper proposes a novel approach for the 2D-to-3D texture transfer task, incorporating a progressive texture generation strategy, multi-view consistent gradient guidance, and fine-tuned prompt-tuning gradient guidance.The progressive texture generation ensures that the guidance image for the current view is derived from adjacent views, enhancing view consistency. The proposed View-Consistency Gradient Guidance steers the generation process toward multi-view consistency while maintaining high-fidelity texture transfer. The method achieves coherent multi-view outputs with realistic texture details.", "questions": "1. The title mentions \"3D objects\" without specifying the modality (e.g., mesh, point cloud, NeRF). Could the authors provide results on other 3D representations?\n2. How does the proposed progressive generation fundamentally differ from existing multi-view diffusion methods\n3. Can the authors provide ablation experiments for Progressive Generation?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths：\n + This paper is well-prepared and easy to follow.\n  + The experimental results are impressive.\n  + The proposed progressive generation strategy is interesting, and the two guidances are also proven to be effective.\n\nWeakness：\n1. The proposed progressive generation resembles existing multi-view diffusion approaches, particularly in its attention mechanism, with insufficient differentiation.\n2. The impact of progressive generation remains unverified due to the absence of ablation experiments.\n3. In the method section, the method's pipeline lacks clarity, e.g., the transferring process.\n4. The symbol *e* in Equation 1 lacks defination, which may confuse readers about its role in the formulation.\n5. In Fig.2: （1）The leftmost and bottom panels fail to convey key methodological insights.\n（2）The rightmost subfigure lacks detailed explanation in the main text.", "was_revised": true, "final_justification": "After carefully reviewing the authors' response and the comments from the other reviewers, I believe that most of the initial concerns have been effectively addressed. I have therefore adjusted my rating and recommend that this paper be accepted.", "rebuttal": "We thank the reviewer for the constructive feedback and thoughtful questions. Each point is addressed in detail in the following sections. \n\n---\n### 1. Clarification on Similarity with Multi-View Diffusion Attention Mechanisms\n\nWhile our modified generative model may appear similar to existing multi-view diffusion methods at a high level, we emphasize that our task settings, constraints, and diffusion components are fundamentally different.\n\nSpecifically, our method takes as input a single 2D reference image and a fixed 3D Gaussian Splatting (3DGS) representation, and performs high-fidelity texture transfer onto this existing geometry. In contrast, multi-view diffusion approaches are typically designed to generate novel views or reconstruct 3D scenes from a few same texture input images, which are not anchored to an explicit 3D representation. These methods synthesize geometry implicitly through learned priors, which makes them unsuitable for editing tasks that require consistency with a given 3D structure. In addition, multi-view diffusion methods assume cross-view texture consistency, which does not hold in the texture transfer setting where the reference image and the 3D representation exhibit different textures. Directly applying them as input leads to collapsed reconstructions.\n\nRegarding the designed diffusion components, our proposed view-consistency guidance and prompt-tuning-based guidance are training-free plugins while finetuning is required for modifications of existing multi-view diffusions.\n\n\n\nWe will revise the final version of the paper to make this distinction clearer, particularly in relation to the design and function of our attention mechanism within the 3D editing context.\n\n---\n### 2. Justification of Progressive Generation via Ablation Study\n\n\nTo evaluate the effectiveness of the proposed progressive generation process, we conduct an ablation study where we disable progressive view propagation and perform editing using only the initial reference image. In this ablated setting, each novel view is generated independently, without leveraging intermediate edited views. The results, averaged across several scenes, are summarized below:\n\n| Averaged | LPIPS (Alex) | LPIPS (VGG) | CLIP Score |\n|---|---:|---:|---:|\n| W/O Prog. Gen. | 0.129 | 0.133 | 0.916 |\n| With Prog. Gen. | 0.116 | 0.125 | 0.933 |\n\nThe detailed ablation study results on progressive generation process are listed in following three tables:\n\n|   LPIPS (Alex)       | **Pink bear** | **Golden bear** | **White table** | **Moss table** | **Ice horse** |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| W/O prog. Gen.        | 0.035    | 0.133    | 0.096        | 0.103   | 0.074    |\n| With Prog. Gen.     | 0.038  | 0.111     | 0.047         | 0.094   | 0.056    |\n|          | **Fire horse** | **Lego dinosaur** | **Wooden dinosaur** | **Hawk** |**Hulk** | \n| W/O prog. Gen.        | 0.089   | 0.125     | 0.110       | 0.254   | 0.276    |\n| With Prog. Gen.     | 0.081   | 0.123    | 0.095         | 0.237   | 0.284    |\n\n|   **LPIPS (VGG)**      | **Pink bear** | **Golden bear** | **White table** | **Moss table** | **Ice horse** |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| W/O prog. Gen.        | 0.061  | 0.134    | 0.104         |0.097   | 0.063    |\n| With Prog. Gen.     | 0.073   | 0.116     | 0.068         | 0.096   | 0.056    |\n|          | **Fire horse** | **Lego dinosaur** | **Wooden dinosaur** | **Hawk** |**Hulk** | \n| W/O prog. Gen.        | 0.085   | 0.123     | 0.096        | 0.278   | 0.292    |\n| With Prog. Gen.     | 0.085   | 0.122     | 0.092         | 0.250   | 0.289    |\n\n|   **CLIP Score**      | **Pink bear** | **Golden bear** | **White table** | **Moss table** | **Ice horse** |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| W/O prog. Gen.        | 0.962   | 0.923    | 0.936        | 0.937   | 0.919    |\n| With Prog. Gen.     | 0.926   | 0.939     | 0.968         | 0.963   | 0.922    |\n|          | **Fire horse** | **Lego dinosaur** | **Wooden dinosaur** | **Hawk** |**Hulk** | \n| W/O prog. Gen.        | 0.896   | 0.957     | 0.938        | 0.766   | 0.921    |\n| With Prog. Gen.     | 0.957   | 0.949     | 0.9593         | 0.837   | 0.913    |\n\nThese results demonstrate that the progressive generation process significantly improves texture fidelity (lower LPIPS) and semantic alignment with the reference (higher CLIP score). The improvement highlights the importance of propagating texture across neighboring views to enforce view consistency and mitigate artifacts from single-view editing.\n\n---\n### 3. Clarification on Pipeline and Texture Transfer Process\n\nWe acknowledge that the current presentation of our pipeline in the method section and figure could be clearer. We will revise both the pipeline figure and accompanying text in the revised paper to better illustrate the flow of the texture transfer process.\n\nSpecifically, our pipeline proceeds as follows:\n(1) We first obtain the reference image(s), either manually (e.g., by cropping/pasting) or automatically (e.g., via depth-conditioned generation).\n(2) The selected reference image is used to initialize the progressive generation process, where edited images for multiple views are produced using our proposed view-consistency and prompt-tuning-based gradient guidance.\n(3) These high-quality edited views are then used to fine-tune the 3D Gaussian Splatting (3DGS) representation.\n(4) We re-render the updated 3D object into new images and repeat steps (2) and (3) for further refinement.\n\nTypically, two iterations are sufficient to achieve high-fidelity, view-consistent texture transfer across the full object. We will incorporate this clarified explanation and an updated pipeline figure in the revised paper to improve the overall readability of the method section.\n\n---\n### 4. No Definition of $e$\nThanks for pointing this out. Index $e$ refers to the image that is currently being edited. \n\n---\n### 5. Clarification of Figure 2 (Pipeline)\n\n We agree that certain components of Figure 2 could be made more informative. Specifically:\n\n(1) We will revise the leftmost and bottom panels to better convey the reference image acquisition process and the rationale behind using flipped variants and sparse cross-attention.\n\n(2) We also acknowledge that the rightmost subfigure (showing the iterative editing loop) lacks sufficient explanation in the main text. We will revise the manuscript to clarify that:\n* The reference image is obtained either manually or via depth-conditioned generation.\n* It is used to initiate the progressive generation stage, guided by both view-consistency and prompt-tuning-based gradient guidance.\n* The resulting edited images are then used to fine-tune the 3D Gaussian Splatting representation.\n* This process is iterative: we re-render the updated 3D objects and apply the same editing process again. Typically, two iterations are sufficient to achieve high-quality texture transfer.\n\nWe will revise both the figure and the method description accordingly to ensure that these steps are clearly communicated.\n\n---\n### Q1: Clarification on “3D Objects” and Generalizability Across Modalities\n\nWe agree that the term “3D objects” in the title may be too broad, and we are open to revising it to “3DGS-OTT: Texture Transfer for 3D Gaussian Splatting from a Single Reference Image” to more accurately reflect our current implementation.\n\nWhile our experiments focus on 3D Gaussian Splatting (3DGS) due to its compatibility with GaussCtrl and efficient rendering, the core contributions of our method: progressive generation, view-consistency gradient guidance, and prompt-tuning-based gradient guidance, are representation-agnostic. They operate independently of the underlying 3D format and can be adapted to other modalities such as NeRF, point clouds, or meshes, by treating diffusion-based generation and 3D model finetuning as two decoupled stages.\n\nWe also plan to release a modular version of our pipeline that includes only the diffusion-based editing stage, allowing users to integrate it with other 3D representations depending on their downstream applications.\n\n---\n### Q2 (Multi-view Diffusion Methods) & Q3 (Ablation for Progressive Generation): \n\nWe believe these questions are addressed in our responses to previous discussions on the distinction between our progressive generation and existing multi-view diffusion methods, and where we provide an ablation study validating the effectiveness of the progressive generation process."}, {"review_id": "LALSdT8L0W", "reviewer": "Reviewer_Eep3", "summary": "This paper, \"3D-OTT: Texture Transfer for 3D Objects from a Single Reference Image,\" proposes a novel framework for transferring texture from a single 2D reference image to a 3D object represented by 3D Gaussian Splatting. The core aim is to address critical challenges in 3D texture editing, specifically view consistency and the preservation of texture characteristics. The framework comprises three main components: a progressive generation process, view-consistency gradient guidance, and prompt-tuning-based gradient guidance. The first two components are designed to enforce view consistency, while the third ensures the preservation of texture characteristics. The paper demonstrates that 3D-OTT effectively edits the texture, material appearance, and color of 3D objects, outperforming existing text-driven 3D editing methods in both qualitative and quantitative evaluations.", "questions": "1. As in Weakness1, the paper states that reference images can be obtained by \"directly pasting the reference image onto the 3D object\". However, the specific implementation details of this \"pasting\" or \"cropping\" process are not clearly described. If this process requires manual selection of the pasting viewpoint or extensive manual intervention.\n2. As in weakness2, it lacks an ablation study demonstrating the effect of not using this flip operation in cases where it might be detrimental, which would better clarify its impact.", "limitations": "Same as Weakness and Questions.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper presents compelling qualitative results that visually demonstrate the effectiveness of the proposed 3D-OTT method in faithfully editing 3D objects' textures, materials, and colors based on reference images.\n2. The 3D-OTT framework is well-structured with three distinct and clearly defined components: progressive generation, view-consistency gradient guidance, and prompt-tuning-based gradient guidance. These modules are logically designed and intuitively contribute to solving the identified problems of view consistency and texture preservation.\n\nWeakness:\n1. Ambiguity in \"Depth-based Generation\" and \"Initial Reference Image\" Connection: Section 3.1 describes two ways to obtain reference images: direct pasting of the initial 2D reference image and generating candidate views using a depth-conditioned model. However, the paper lacks clear technical detail on how the depth-conditioned generation process explicitly links to and maintains the specific texture style defined by the initial single 2D reference image. It is unclear how the depth-conditioned model inherently aligns its generated textures with the precise style defined by the initial 2D reference image. If the depth-conditioned model primarily relies on prompts to generate reference images, the underlying process would essentially be text-to-image-to-texture. This contradicts the paper's core premise of \"Texture Transfer for 3D Objects from a Single Reference Image,\" introducing an ambiguity in the main objective and its implementation.\n2. The method introduces a horizontally flipped variant of the reference image ($\\mathbb{F}(\\mathbb{I})_{\\tau}$) into the sparse reference set for progressive generation (Section 3.1) to counter error accumulation. This approach inherently assumes some level of symmetry. For 3D objects that are not left-right symmetric, simply flipping the 2D reference image would introduce geometric and textural inconsistencies, potentially leading to erroneous texture transfer and undermining view consistency for asymmetric features. The paper does not discuss this limitation or how it might be addressed.\n\nMinor weakness：\n1. In Equation (1) (Section 3.1), the subscript '$e$' in $Attn_{e,e}$ is used without explicit definition within the text, which can lead to minor confusion for readers.\n2. In Section 3.2, lines 144-145, the explanation of Equation (2) refers to \"differences between guidance with and without the negative prompt.\"  This phrasing is imprecise and can be confusing, as \"negative prompt\" typically denotes an explicit textual instruction to avoid certain features in Classifier-Free Guidance, rather than the absence of a conditioning signal (i.e., prompt - none prompt).", "was_revised": true, "final_justification": "Thanks the author's reply. I will raise my score to 4.", "rebuttal": "We appreciate the detailed review and questions. Our responses are provided in the following sections. \n\n---\n### 1. Clarification on Connection Between Depth-based Generation and Initial Reference Image\n\nWe thank the reviewer for pointing out the ambiguity. We would like to clarify that during progressive generation stage, the depth-based generation process is not primarily driven by prompts, but instead is explicitly conditioned on the visual content of the single 2D reference image and depth. Specifically, we employ a depth-based ControlNet diffusion model that takes both the depth map and the latent embedding of the reference image as inputs. To preserve the texture information from the reference image, we use a k-step partial denoising strategy: the reference image is encoded and reversed into noise for k steps (we set k = 400), and then denoised starting from that noisy state (see lines 46, 128, and 141). This ensures that the exact texture and shape information from the reference image is retained, rather than synthesized anew from a text prompt.\n\nRegarding the reviewer’s concern about whether the process reduces to “text-to-image-to-texture”, we clarify that this is only one optional reference acquisition mode and not central to our method. We further clarify Section 3.1:\n\n* In the manual image-to-image mode, users paste the 2D reference texture image directly onto the object’s silhouette to create a reference image. (Image-to-image-to-texture).\n* Further, we provide an automatic image-to-image mode, where users provide a 2D reference image (which may differ in shape), which is then processed via partial denoising conditioned on depth, without manual intervention. (Image-to-image-to-tetxture).\n\nThese reference images, regardless of how they are obtained, are then used in the diffusion process to propagate the texture across views, not regenerated from scratch using a prompt. Text-prompted initial reference generation is only utilized when there is no desired texture.\n\n\n---\n### 2. Use of Flipped Reference Image and Asymmetry Concern\n\nIndeed, our method includes a flipped variant $F(I_\\tau)$ to improve consistency across distant views (Section 3.1). This design implicitly assumes some degree of left-right symmetry, which does not always hold. We now clarify this limitation and explain how our method mitigates the potential issues:\n* Mitigation via cross-attention: When the object is not symmetric, the flipped image contributes little due to low similarity ($Query \\times Key$) in the cross-attention mechanism. The attention weights naturally diminish for dissimilar content, preventing flipped references from introducing significant errors.\n* Ablation results: As shown in our experiment table below on the “bear” scene, the flipped reference improves performance for the symmetric (back) side, while slightly degrading it for the asymmetric (front/head) side. Importantly, the overall performance still improves, as shown by the net gains in LPIPS and stable CLIP scores. Note, lower LPIPS = better and higher CLIP score = better.\n* Optional inclusion: The flipped reference is not strictly required. In scenarios involving strongly asymmetric objects, users or future extensions of our system could disable this component without affecting the core pipeline.\n\nWe will include a discussion of this limitation and tradeoff in the revised paper to improve clarity.\n\n\n|           | LPIPS(Alex) | LPIPS(VGG) | CLIP score | \n|-----------|:---:|:-----:|:---------:|\n| Frontside with flipped Ref.      |  0.1488  |   0.1218   |   0.8263      |\n| Frontside W/O flipped Ref.      | 0.1442  |   0.1181   |    0.8263      | \n| Backside with flipped Ref.     |  0.2287  |  0.1867    |   0.8226     | \n| Backside W/O flipped Ref.     |  0.2360  |   0.1973   |     0.8225    |\n| Frontside performance change     |  +0.0046  |  +0.0038    |   0.0     | \n| Backside performance change    |  -0.0073  |   -0.0106   |     +0.0001    |\n\n---\n### 3. No Explicit Defination of $e$ in $Attn_{e,e}$\n\n\nThank you for pointing this out. Index $e$ refers to the image that is currently being edited.\n\n---\n### 4. Clarification on “Negative Prompt” Terminology in Eq.(2)\n\nWe agree that the terminology in lines 144–145 may be misleading. Our intention was to refer to the unconditioned branch of Classifier-Free Guidance, i.e., the absence of a prompt or a null prompt, not an explicit negative textual instruction. We will revise this phrasing in the final version to use the more precise term “unconditioned prompt” or “null conditioning”, in line with standard CFG terminology."}, {"review_id": "V6PbI5L4jw", "reviewer": "Reviewer_Lzfr", "summary": "This paper introduces 3D-OTT, an innovative approach for 3D object texture transfer using a single reference image. The method consists of a progressive generation process combined with view-consistency gradient guidance and prompt-tuning gradient guidance to ensure texture view-consistency and preservation of object characteristics.\nThe progressive generation method proposed is both straightforward and highly effective, enabling the generation of high-quality reference images. The authors introduce prompt-tuning gradient guidance by leveraging texture differences in the CLIP space, creating a token for fine-tuning within a diffusion framework to bridge the gap caused by mismatches between CLIP image and text features. Their approach demonstrates superior characteristic preservation compared to established techniques such as Dreambooth or Textual Inversion, as clearly indicated in the supplementary materials. Additionally, the view-consistency gradient guidance employs intermediate result differences as gradient guidance, effectively tackling view-consistency challenges in a training-free manner.", "questions": "1. Please elaborate on the performance differences between using tokens directly obtained from the CLIP space versus tokens that are randomly initialized and subsequently fine-tuned within the diffusion framework?\n2. What is the inference speed of the proposed method in comparison to existing baseline approaches?", "limitations": "Please refer to \"Weaknesses\" and \"Questions\"", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The topic addressed is meaningful.  As discussed in related work, modifying previous text based 3D editing methods to achieve this functionality causes problematic results. By proposing 3D-OTT, they can achieve faithful 3D texture transferring. \n2. The proposed methodology is elegantly simple yet powerful. Specifically, the progressive generation approach successfully resolves the complexities associated with reference views containing original textures. The two gradient guidance strategies presented are clearly articulated and well illustrated by Figure 2.\n3. The experimental evaluations are thorough and compelling, featuring extensive numerical and visual comparisons. The ablation study, utilizing two distinct reference images, effectively highlights the robustness and utility of the prompt-tuning gradient guidance.\n\nWeaknesses:\n1. The user study currently involves a few participants. Increasing the participant pool will strengthen the reliability and significance of the user evaluation results.\n2. This work achieves good editing results, Incorporating a video demonstration would significantly enhance the presentation.", "was_revised": true, "final_justification": "After reading the authors’ rebuttal, most of my concerns have been addressed. In addition, I believe the ablation studies on Progressive Generation and Flipped Reference Image effectively respond to the concerns raised by Reviewer ZPz8 and Reviewer Eep3. Taking all aspects into account, I find this work sufficiently solid. Based on the authors’ response and the comments from other reviewers, I have decided to raise my score and recommend acceptance.", "rebuttal": "We thank the reviewer for the positive feedback and the insightful questions. We address each of your questions in the following sections. \n\n---\n### 1. User Study Participant Size\n\nWe agree that increasing the number of participants would strengthen the significance of the user study results, and we will expand the participant pool in the revised version. Now, we append results of three new participants in the end. \n\n---\n### 2. Request for Video Demonstration\n\nWe agree that a video demonstration would enhance the presentation of our results. We will include video demos on our project GitHub and release them alongside the code.\n\n---\n### Q1:  Effect of CLIP-Based Token Initialization vs. Random Initialization\n\nWe conduct an ablation study to compare two initialization strategies for the prompt used in the CLIP-guided gradient mechanism:\n* Random Initialization: The prompt is randomly initialized and then optimized during fine-tuning.\n* CLIP-Based Initialization: The prompt is initialized by directly projecting the texture difference into CLIP space.\n\nAs shown in the table below, initializing the prompt with CLIP-based features leads to significantly better performance in terms of LPIPS (Alex), LPIPS (VGG), and CLIP Score:\n\n|         | LPIPS (Alex) | LPIPS (VGG) | CLIP Score |\n|-----------|:---:|:-----:|:---------:|\n| Random Init.   | 0.1539   | 0.1251    | 0.8574       |\n| Prompt Init.   | 0.1166   | 0.1247   | 0.9333        |\n\nThis result indicates that CLIP-based initialization helps the prompt converge faster and guides the diffusion more effectively toward the desired texture characteristics, resulting in better texture fidelity and alignment with the reference.\n\n---\n### Q2: Inference Speed Comparison\n\nWe compare the inference time of our method against two baselines: the Gaussian Splatting base model (GaussCtrl) and a representative NeRF-based approach. Our method maintains the efficiency benefits of Gaussian Splatting while introducing only modest additional overhead due to our view-consistency and prompt-tuning-based guidance.\n\nIn particular, the total additional time required by our proposed guidance components is approximately 8 minutes. Since our full pipeline involves two iterations of image editing, the added cost per iteration is around 4 minutes, which we consider efficient given the improvements in texture fidelity and consistency.\n\n|IN2N|GaussCtrl|Ours|\n|:---: |:---: |:---:|\n|20h:51m:20s|0h:15m:47s|0h:23m:33s|\n\n---\n\n### Appended User Study \n\n\n\n\n\n\n\n\n\nWe have added 3 new participants as shown in following tables:\n|    User#01       | n2n | gs2gs | gaussctrl | dge | ours |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| pink bear        | 3   | 3     | 1         | 2   | 5    |\n| golden bear      | 2   | 2     | 3         | 2   | 4    |\n| white table      | 1   | 1     | 3         | 2   | 5    |\n| moss table       | 3   | 2     | 1         | 1   | 5    |\n| ice horse        | 3   | 3     | 2         | 1   | 5    |\n| fire horse       | 1   | 3     | 2         | 2   | 5    |\n| lego dinosaur    | 1   | 1     | 2         | 2   | 4    |\n| wooden dinosaur  | 2   | 2     | 2         | 2   | 5    |\n| hawk             | 1   | 2     | 4         | 2   | 5    |\n| hulk             | 2   | 4     | 1         | 2   | 5    |\n| **Avrage**     | 2.1   | 2.3   | 2.1     | 1.8   | 4.8  |\n\n|    User#02       | n2n | gs2gs | gaussctrl | dge | ours |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| pink bear        | 4   | 3     | 1         | 2   | 5    |\n| golden bear      | 2   | 2     | 3         | 2   | 4    |\n| white table      | 1   | 2     | 3         | 2   | 3    |\n| moss table       | 2   | 2     | 1         | 1   | 5    |\n| ice horse        | 2   | 3     | 2         | 1   | 5    |\n| fire horse       | 1   | 3     | 2         | 2   | 5    |\n| lego dinosaur    | 1   | 1     | 2         | 2   | 4    |\n| wooden dinosaur  | 2   | 2     | 2         | 2   | 4    |\n| hawk             | 1   | 2     | 3         | 2   | 5    |\n| hulk             | 2   | 3     | 1         | 2   | 4    |\n| **Average**      | 1.6 | 2.0   | 2.0       | 1.8 | 4.4  |\n\n|    User#03       | n2n | gs2gs | gaussctrl | dge | ours |\n|-----------|:---:|:-----:|:---------:|:---:|:----:|\n| pink bear        | 3   | 3     | 1         | 2   | 4    |\n| golden bear      | 2   | 2     | 3         | 2   | 4    |\n| white table      | 1   | 2     | 3         | 2   | 4    |\n| moss table       | 3   | 2     | 1         | 1   | 5    |\n| ice horse        | 3   | 3     | 2         | 1   | 5    |\n| fire horse       | 2   | 3     | 2         | 2   | 5    |\n| lego dinosaur    | 2   | 2     | 2         | 2   | 5    |\n| wooden dinosaur  | 2   | 2     | 2         | 2   | 5    |\n| hawk             | 3   | 2     | 3         | 2   | 5    |\n| hulk             | 2   | 3     | 2         | 2   | 5    |\n| **Average**      | 2.3 | 2.4   | 2.1       | 1.8 | 4.7    |"}]

Author rebuttal:

Meta-review: The paper proposes a method for transferring texture from a reference image to a 3D object using diffusion models. The approach progressively edits multi-view images, leveraging a reference set (previous views, the reference view, and its flipped version), enhanced classifier-free guidance, and a learned token that encodes texture discrepancies to guide denoising toward the desired style. The method demonstrates strong qualitative results.

However, several weaknesses remain:
- Clarity and presentation: The writing contains numerous typos and unclear descriptions (as noted by Reviewers ZPz8 and Eep3). Many sections (for method) are not self-contained, which may hinder accessibility to general readers. Additionally, the lack of a clear teaser figure limits understanding of the pipeline (Fig. 2 is not sufficiently informative).
- Assumptions on reference images: Despite the claim of using a single reference image, the method relies on restrictive assumptions (e.g., requiring consistent geometry with varying textures, and the need for text prompts), as highlighted by Reviewer ZPz8.
- Evaluation concerns: Results without progressive generation (as shown in the rebuttal) already outperform baselines, raising concerns about whether the baselines are too weak. Furthermore, performance discrepancies between “ours” in Tables 1 and 2 are confusing.

The rebuttal provides additional ablations and clarifies the pipeline, which improves the submission. Overall, the paper presents clear technical modules and compelling qualitative results, but clarity, writing quality, and evaluation remain areas for improvement.
Decision: Accept (poster)