GOATex: Geometry & Occlusion-Aware Texturing
Authors: Hyunjin Kim
Kunho Kim
Adam Lee
Wonkwang Lee
Venue: NeurIPS 2025 poster ()
Keywords: 3D
Mesh
Texturing
Primary area: applications
TL;DR: Novel method for 3D mesh interior painting

Abstract: We present GOATex, a diffusion-based method for 3D mesh texturing that generates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-aware texturing framework based on the concept of hit levels, which quantify the relative depth of mesh faces via multi-view ray casting. This allows us to partition mesh faces into ordered visibility layers, from outermost to innermost. We then apply a two-stage visibility control strategy that progressively reveals interior regions with structural coherence, followed by texturing each layer using a pretrained diffusion model. To seamlessly merge textures obtained across layers, we propose a soft UV-space blending technique that weighs each texture’s contribution based on view-dependent visibility confidence. Empirical results demonstrate that GOATex consistently outperforms existing methods, producing seamless, high-fidelity textures across both visible and occluded surfaces. Unlike prior works, GOATex operates entirely without costly fine-tuning of a pretrained diffusion model and allows separate prompting for exterior and interior mesh regions, enabling fine-grained control over layered appearances.

GOATEX: Geometry & Occlusion-Aware Texturing
Hyunjin Kim" " KRAFTON AI
Kunho Kim
Adam Lee" Wonkwang Lee 1,4 Scoul National University
NCC AI
PC Berkeley
A wooden forest in sabie with lsavv--corvedl
post repossallllssii rushad
A laserions home theator with push
lanterrn olow
Unified Prompting
Dual Inner & Outer Prompting for Fine-Grained Layered Appearances
Figure 1: We present GOATEX, the first occlusion- -waare 3D mesh texturing method designed to synthesize realistic mesh textures for both exterior and occluded interior regions.
Abstract
We present GOATEX, a diffusion-based method for 3D mesh texturing that gener- ates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-awar texturing framework based on the concept of hit levels, which quantify the relative depth of mesh faces via multi-view ray casting. This allows us to partition mesh faces into ordered visibility layers, from outermost to innermost. We then apply two-stage visibility control strategy that progressively reveals interior regions with structural coherence, followed by texturing each layer using pretrained diffusion model. To seamlessly merge textures obtained across layers, we propose a soft UV-space blending technique that weighs each texture's contribution based on view-dependent visibility con- fidence. Empirical results demonstrate that GOATEX consistently outperforms existing methods, producing seamless, high-fidelity textures across both visible and occluded surfaces. Unlike prior works, GOATEX operates entirely without costly fine-tuning of a pretrained diffusion model and allows separate prompting for exterior and interior mesh regions, enabling fine-gra -reigraindd control over layered appearances. more qualitative results, please visit our project page this link
"qqual contribution
*Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
High-quality mesh textures are critical for creating digital assets across a wide range of applications, including gaming, animation, augmented reality (AR), and virtual reality (VR), as they significantly enhance the believability of virtual environments and improve both user experience and immersion. Importantly, the realism isn't 't just needed for surfaces that are immediately visible- it also matters for inner details that users might see as they move around, interact with the environment, or change their viewpoint. For example, in architectural visualization, it's not enough to have well-textured house façades; interior features like walls, doors, and furnitures also need detailed textures, since users may examine them closely during a VR walkthrough. Similarly, in vehicle simulation, while the exterior body of a car or bus must appear authentic, interior textures, such as dashboards, seats, and ceiling panels, must also be rendered with high fidelity to support seamless and immersive experience.
To address these increasing demands for realistic mesh texturing. recent advancements in text-to- 10
image (T2I) diffusion models [4] 37 40
9 49 have played a critical role, advancing image creation by enabling artists and developers to synthesize highly detailed images directly from textual descriptions. Building upon this progress, text-to-texture generation
35
20
46
34
28
29
14
43
52 based on 2D image diffusion priors have emerged, aiming to alleviate the labor-intensive process of manual asset creation and make high- fidelity texture generation more accessible. These methods usually operate by unprojecting multi-view rendering of the outer surface onto the mesh's ' UV map. leveraging 2D diffusion priors to achieve high- quality texture synthesis. Moreover, techniques such as iterative painting [39] 8 47 25 22 (1) and further enhance view alignments, reducing visible seams and artifacts.
approaches (39 6
27
53
22
21
multi-view sampling 6 27
However, texturing interior surfaces remains a significant challenge, as existing methods that rely on multi-view unprojection lack access to occluded or internal geometries, often resulting in untextured regions. Several methods 8 27 have attempted to address this gap with Voronoi-based filling techniques; however, such methods often result in visible seams and inconsistent surface textures. More recent works 53
have explored UV-space refinement and inpainting by fine-tuning diffusion models. These approaches are capable of texturing internal surface regions; however, they often fail to produce high-quality and plausible interior surfaces due to their reliance on UV space representations, which lack explicit geometric context, and the limited availability of training data that captures the high variability of UV map structures with high-quality interior surface textures.
To this end, we propose GOATEX, the first arclusion-aware mesh texturing method that explicitly targets the underexplored challenge of inner surface texturing. Our key idea to treat the mesh as a layered structure and progressively reveal surfaces from the outside in, guided by ray-based visibility analysis. We begin by casting multi-view rays to compute hit levels, which quantify the relative depth of mesh regions and partition the surface into ordered visibility layers, facilitating a subsequent process of rendering pipeline that progressively exposes occluded geometry. To preserve the overall shape and identity of the object throughout the progression, strategy that combines residual face clustering with normal flipping and backface culling. This allows new interior surfaces to be exposed without distorting the mesh's global structure. Each layer is then textured independently using MVD module (27] with pretrained depth-conditioned diffusion propose UV-space blending scheme based on view dependent visibility weights, which avoids seams and style inconsistencies. Experiments show that GOATEX produces high-qualit textures across both visible and occluded regions, while enabling fine-grained control over layered appearance through separate prompts for exterior and interior surfaces.
introduce two-stage visibility control
model (54). To merge these textures seamlessly,
The contributions of this paper are summarized as follows:
1. To the best of our knowledge, we the first to introduce and address the task of generating real- istic textures for occluded interior surfaces of 3D meshes alongside exterior regions, a practically important yet still underexplored challenge in the 3D mesh texturing literature.
2. We propose GOATEX, ray-based occlusion-aware framework that textures both exterior and interior regions without requiring tuning of pretrained diffusion models. Our method also supports dual prompting for inner and outer surfaces, enabling fine- fine-grained control over layered structures.
3. User studies and GP'T-basdd evaluations show that GOATEX is strongly preferred over existing methods, achieving state-of-the-ar texture quality across both visible and occluded surfaces.
2

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2
Related Work
2.1 Texture Generation via 2D Diffusion Priors
While significant progress has been made in mesh texturing, earlier methods 30
33
16 5] were often limited by their dependence on scarce high-quality 3D datasets. More recently, the focus has shifted toward zero-shot texture generation using publicly available text-to- image (T21) diffusion models 40 35 [54], enabling effective texture synthesis without extensive 3D training data. Many current methods employ a project-and-inpaiiee strategy [39
7 45
25 22 211.
synthesizing initial textures from a canonical view and subsequently inpainting missing regions. For instance, TEXTure [39] and Text2Tex [ incrementally generate seamless textures using depth- conditioned diffusion models guided by trimaps. However, such methods often face cross-view inconsistency due to limited global geometric context. To address this, TexFusion ( proposes a Sequential Interlaced Multi-view Sampler (SIMS), integrating multi-view appearance cues during denoising for improved consistency. Other approaches [24 55 optimize UV maps directly through multi-view renderings and score distillation sampling (SDS) 33). although these methods typically require substantial computational resources. SyncMVD (27 further improves coherence through a
Multi-View Diffusion module employing view synchronization techniques [2]
26 23 [50]. Similar
to these prior works, our GOATEX leverages powerful 2D diffusion priors but uniquely addresses occluded interior regions. Our method explicitly identifies and progressively textures internal surfaces by employing ray-based visibility analysis to systematically expose and handle inner surfaces.
2.2 Geometry-Aware Texture Generation via 3D Mesh-Based Training
Recent advances leverage large-scale 3D datasets, such as Objaverse [22 M. enabling training of sophisticated texture generation models directly textured meshes. FlashTex [13] introduces LightControlNet to disentangle lighting effects from surface materials, improving relighting capa- bilities. Paint3D [53] employs a coarsc-to-fing to-sne generative framework, initially synthesizing textures with a aware 2D diffusion model and subsequently refining them through dedicated UV In- painting and UVHD modules. Similarly, TEXGen [52] adopts hybrid 2D-3D strategy, directly training a specialized UV-space generative model without coarse-to- to-fie approach. Geometry-awan approaches like Hunyuan3D-Paint (57) and CLAY [55] utilize mesh geometry conditions, such as normal and position maps, to guide diffusion models that generate multi-view tiled images used for texturing. Specifically, Hunyuan3D-Paing introduces canonical normal and coordinate maps into the diffusion process, incorporating reference and multi-view attention mechanisms, while CLAY synthesizes diffuse, roughness, and metallic maps conditioned on normal maps and reference images. Cleta3DTextureGar [3] builds upon these concepts, initially generating multi-view tiled images conditioned on geometry, and further enhancing the texture quality through an additional UV space inpainting network. Despite these advancements, current methods primarily focus on exterior surfaces or, even when generating interior textures in UV space, face difficulties producing plausible interior surfaces due to lack of high-quality interior texture data.
3 GOATEX: Geometry & Occlusion-Aware 3D Mesh Texturing
Texturing the interior surfaces a 3D mesh poses a unique challenge: these regions are often fully occluded from external viewpoints and receive little to coverage in conventional rendering- pipelines. To this end, we propose GOATEX, an occlusion-aware framework for texturing of both exterior and interior mesh regions. A schematic overview of our pipeline is shown in Fig.
3.1 Ray-Based Hit Level Assignment for Layered Geometry Decomposition
Our method begins with estimating the relative visibility depth of different regions of the mesh which allows to progressively expose and render from exterior to interior surfaces during subsequent texturing stages. This achieved through two key steps: (1) grouping mesh faces into structurally coherent regions called superfaces, (2) assigning hit level each superface based on ray casting.
Superface Construction. Directly assigning hit levels to individual faces can result in fragmenta- tion, where adjacent faces belonging to the same planar region are assigned different hit levels due to the mesh's 's composition of numerous small faces (as in top-left pane of Fig. 2). undermining the generation of semantically consistent and plausible textures in the subsequent stages. To address this,
3

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Level
Level
Hi Level4
Alla Based Oooogmentation
Anigiment
Original
Weight
(1) Superiace Construction
(2) Hit Level Assignment
Ray Casting
pathormendl
Origina Soi
Cl Elabiiing
Digh Realerig Sadiios Callig)
(4) Toxture
Input Mesh
Narmad Fllped
Synthesis
(3) Progressive Visibility Control
(5) Weighted UV Blending
Output
Figure 2: Overall Pipeline of GOATEX. (1) Our framework begins with superface construction,
grouping fine-gras grained faces into coherent regions. (2) We then assign each superface a hit level by casting rays from multiple viewpoints, indicating its relative depth within the mesh ($3.1). (3) Based on these hit levels, we apply visibility control strategy, combining residual face clustering with normal flipping and backface culling, progressively reveal interior geometry without disrupting the object's s structural integrity ($3.2). 2 (4) For each hit level, we synthesize textures using a depth- conditioned multi-view diffusion (MVD) module (27. (5) We employ a soft, visibility-weightee UV- blending strategy to merge textures across levels, ensuring seamless and coherent appearance ($3.)).
we use Xatlas library [51 to oversegment the mesh into connected, low-curvature regions, referred to as atlases. Each atlas is treated as a superface and serves as the basis for hit level assignment.
Hit Level Assignment. Next, cast rays from multiple viewpoints, recording both their intersec- tion order and directional influence on the surface. These information are then aggregated across all rays determine the most representative hit level for the superface. Each ray's influence is weighted by the cosine similarity between its direction and the face normal, with more direct (i.e., near -)) intersections given greater importance and having a stronger impact on the hit level assignment.
Formally, the influence weight W(f, k) of ray r with intersection order k on face f is defined as:
W(, = Σ max (-n(J) -((),
TERA())
where (f) is the set of rays intersecting face f with intersection order k. nff) the normal vector of the face ff and d(r) is the direction of the ray r. Finally, we aggregate these directional weights to determine the hit level H (SF), the most dominant intersection order for the superface SF:;
H(FF)) arg max Σ W(f.k).
FESF.
3.2 Visibility Control for Structurally Coherent Layered Texturing
Once each face has been assigned unique hit level based its visibility depth, straightforward texturing approach is to render and texture faces independently by hit level, starting from the outermost faces at the lowest hit level and proceeding inward to those the highest. That is, each hit level k. one could simply construct the set of faces prait UH(SF SF, assigned to that level, render their depth maps, and condition a depth-condiitoned 2D diffusion model to generate textures for them.
However, this naive strategy suffers from critical drawback: as illustrated in Fig. the face clusters associated with higher hit levels become increasingly sparse and fragmented. This leads to incomplete and disjointed geometry in the rendered depth maps, which deviate from the coherent structures typically found in natural objects. As a result, the diffusion model receives out-of-diittibutiinn (OOD) inputs, impairing its ability generate plausible textures for interior regions.
We address these issues with a two-stage visibility control strategy: (1) residual face clustering to construct denser, progressively revealed geometry; and (2) normal flipping with backface culling to

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preserve global shape. These components together ensure that each depth map preserves both the structural fidelity and contextual coherence necessary for robust texture generation.
Residual Face Clustering. To address the sparsity, we redefine the set of faces rendered at each stage by adopting residual face clustering strategy. That is, rather than rendering only the faces uniquely assigned to hit level k, we render the full set of untextured faces remaining from previous levels. The resulting residual face F is defined as:
k-1
FR
Finit
where F' is the complete set of mesh faces. Analogous peeling layers of an onion, this formulation enables the progressive exposure of deeper yet untextured geometry for subsequent texturing.
Normal Flipping & Backface Culling. Residual clustering mitigates sparsity but doesn't prevent the monotonic drop in visible faces across deeper hit levels (see Fig. [] middle). This results in increasingly sparse depth maps that lack structural cues, yielding out-of-distribution inputs for the diffusion model and degraded texture quality.
To this end, introduce technique based normal flipping and backface culling. At each hit level k we keep all mesh faces but flip the normals those already textured earlier levels, turning front-facing surfaces into backfaces relative current rays, or vice versa. Due to the backface culling, faces whose normals point within 90º of the view direction are removed. Thus, flipped surfaces that previously faced the camera (e.g., on the near side) become temporally hidden, as if removed from the scene. Meanwhile, faces once back-facing (e.g., on the far side) may now face the camera and become visible. As result, this view-dependent behavior reveals untextured interiors, while preserving object structure and enhancing per-view visibility. Formally, rendered faces at level k are:
FA = 1 U (F PF
where F and F denote flipped versions of F and FT respectively.
3.3
Weighted UV-Space Blending for Layered Texture Synthesis
Finally, for each hit level kt we generate one texture by rendering multi-view depth maps using the visibilityccontroleed face cluster Fg. These depth maps are then used to condition pretrained texture generation module (MVD module (27] resulting
distinct texture map UV& for each hit level.
A straightforward way to combine these per-level textures is to simply overwrite or average them across hit levels. However, as illustrated in Fig. such naive approaches often result in noticeable visual artifacts or the loss of clear boundaries between interior and exterior surfaces.
To address this, we introduce a UV-space texture merging scheme. For each view at hit level k, compute a UV-space weight Wr(w) based on the absolute cosine similarity between the view direction and the face normal, reflecting how much given view contributes to each texel in the final texture.
The total visibility weight Wa for each hit level k is computed by aggregating contributions across views and further normalized across the hit levels using modified softmax function:
e Wa O MK
We = Σ (
We
(5)
Σ O M₁
where M₁ is a binary UV mask indicating valid texels at hit level k and H is the total number of hit levels. The normalized weight Wt represents the relative influence of hit level k on each texel.
The final merged texture UVp is computed as:
H1 UVF = Σ W. O UV
1=1
where UV& is the texture generated at hit level k. This soft blending method effectively suppresses artifacts caused by abrupt transitions simplistic averaging across independently textured regions.
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Experiments
4.1 Implementation Details
For all experiments, we use Stable Diffusion 1.5 [40] combined with a depth based ControlNet [54] to generate multi-view images from input text prompts, where prompts are augmented with view- specific cues (39 27 to match orientations. Each view is rendered at a resolution of 768 x 768, with a corresponding latent resolution of 96x96. The latent UV texture map has a resolution of 512x512, and the final RGB UV texture map is generated at x024x 1024. For visibility and texture synthesis, the maximum hit level set to 4; we define 16 hemispherical views (8 equatorial 45%, elevated at 45"); Rendering is done with Py PyTorch3D
38
Please find Appendix A1.1 for more details.
4.2 Experiment Setup
Dataset. For evaluation, we curate a diverse set of 139 assets from Objaverse 12] and 87 assets from Objaverse-XL XL selecting 226 high-quality meshes with detailed interior geometries to assess the performance of our method. Each mesh is normalized and rich prompts that capture both exterior and interior characteristics, we generate mesh captions using GPT-40. A detailed description of the captioning process provided in Appendix A2.1
unit bounding box. To provide diverse
Metric. Mesh texturing literature 8 6 27
53
52
commonly adopt FID 19 KID 4 and CLIP-
based metrics 18 [5] such as CLIP-I and CLIP-T, to evaluate visual quality and text alignment. However, these metrics are ill-suited to our setting for two main reasons. First, ground- tround-truth textures are often unavailable especially in occluded mesh regions- rendering reference-batd based metries like FID/KID and CLIP-I inapplicable. Second, recent work [48] 56 I shows that CLIP-based models are sensitive to rendering artifacts and correlate poorly with human judgment in texture assessment.
Therefore, we adopt both a user study and a GPT-based evaluation [48] [56] to assess text alignment and perceptual quality. Specifically, we conduct an A/B preference test, in which participants or a family of GPTs (c.g.. GPT-4o-mini, GPT-4o, G,T-4.1, and GPT-o3) view side-by-side renderings of each method's textured mesh including both exterior and interior views, and select the result that better matches the textual prompt and exhibits higher texture quality. See Appendix A3 for the details.
Baselines. We compare our method against publicly available project-and-inpaint approaches (TEXTure 39 and SyncMVD (27) as well as the methods based on UV inpainting (Paint3D [53] and TEXGen (22) For TEXGen, since it requires both a mesh and an RGB UV texture map input, we unwrap the mesh and initialize the UV texture map using the output generated by SyneMVD. An
analysis of TEXGen with respect to the input UV map is presented in Appendix A1.2
4.3 Analysis on Visibility Control & UV Texture Merging
Visibility Control To assess the effectiveness of our visibility control strategy ($ 3.2). we conduct analysis by ablating residual face clustering and normal flipping. As illustrated in Fig. 3 disabling both components (left pane) results in sparse and fragmented face clusters, particularly evident at hit levels 2 and 4. Introducing residual face clustering alone (middle pane) mitigates the sparsity but still exhibits fragmentation, especially at hit level 4. These limitations result in visible seams in the synthesized textures (e.g., along the edges of a chair) and incomplete reconstruction in occluded areas (e.g., floor surfaces). contrast, applying both techniques (right pane) produces coherent clusters that reveal interior geometry while maintaining overall object structures in the conditioning views. This reduces artifacts and leads to the generation of semantically meaningful and visually consistent textures, underscoring the importance of both components for high-quality interior surface synthesis.
UV Texture Blending We further evaluate the effectiveness of our UV texture merging strat- egy ($ (.33). In our visibility control scheme, the same mesh region can be textured across multiple hit levels, each with varying visibility confidence depending on the angle between the viewing ray and the surface normal. This overlap introduces challenges in merging textures from different levels. As shown in Fig. 4 naive strategies, such as direct overwriting or uniform averaging. fail to account for these confidence variations. They either indiscriminately replace textures or blend them without regard to visibility, leading to noticeable artifacts and loss of structural detail, particularly at inner and

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Hit Level
2evel
Hit Level
Hit Lovel 4
Depth
Image
Depth
Image
Depth
Image
Textured
Output
Residual Clustering
X
Residual Clustering O Normal Flipping X
Residual Clustering O Normal Flipping O
Normal Flipping X
Figure 3: Analysis on our two visibility control techniques, residual face clustering and normal flipping. (Left) Removing both leads to sparse, fragmented face clusters and causes visible seams (e.g., around chair edges). (Middle) Residual clustering improves cluster density but still suffers from fragmentation hit level 4, suffering from incomplete textures (e.g., floor regions). (Right) In contrast, combining both techniques results in improved visibility segmentation, effectively revealing interior structures while preserving the overall shape and structural coherence of the object.
Overwriling
Averaging
Blending (Ours)
View
View 2
View
View
View
View 2
View
View
View
View
View
View
Exterior
Interior
Figure 4: Analysis on UV texture merging techniques. Compared to naive approaches, our blending strategy preserves clear structural boundaries and significantly improves texture quality.
outer boundaries. In contrast, our weighted blending approach leverages hit-level-specific visibility confidence guide the merging process. This enables smooth and fine-gr fineggrained transitions between layers, significantly reducing artifacts and enhancing overall visual quality.
4.4 Qualitative Comparison with Baselines
We qualitatively compare our method against existing approaches, as shown in Fig. [5] TEXTure and SyncMVD struggle to synthesize plausible interior textures due to their reliance view-based gener- ation followed by unprojection. This paradigm inherently lacks access to occluded geometry, leading to heuristic-based filling (e.g., Voronoi-based extrapolation) that results in simplistic, inconsistent textures and visible seams. In contrast, UV-space generation methods such as Paint3D and TEXGen show improved performance in interior regions by operating directly in UV space. However, they still exhibit limitations, often producing low-frequency textures like flat colors or repetitive patterns, because they are unable to differentiate between interior and exterior surfaces within the UV map. This design limitation reduces semantic richness and structural coherence in occluded areas.
Among all methods, GOATEX produces the most visually appealing and semantically coherent textures across both exterior and interior regions. This is achieved through its ray-based layered surface decomposition, which explicitly exposes occluded geometry, followed by visibility control strategies that progressively reveal hidden surfaces. These components provide precise geometric context, enabling the diffusion model to generate rich and plausible textures. Unlike basclines that
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Input Mesh
TEXTure
SyncMVD
Paint3
TEXGen
Ours
dtwervenn forge lesr img served From volcor srace with charred woodon konel,, and glowing rol rand patterr swirting inside.
lacapere shoat with polls hiages slle intorir emirrideresl with dragon monifs gold rarsed.
Flayndo puittorns
paros mmltngang
doep
veiye losld the Vlack
brigit Vartage
desiveurrd footoring broess-accettdl dils.
Figure 5: Qualitative comparisons. Our GOATEX significantly outperforms all the baselines in interior surface texturing, while maintaining competitive in texturing exterior regions (boxed area the top right of each object). This balanced approach makes GOATEX robust solution for both visible exterior and occluded interior surface texturing.
00ATix
86.1%
13.9%
va TEXT
GOATES
50.2%
49,8%
va TEX
G0ATix
SyD
GOATeS
83.3%
16.7%
PainGD
$1.3%
18.7%
Pain3D
96.5%
TEXGes
GnATRe
96 96.4%
13.0%
TEXGo
20
40
60
60
100
20
60
80
100
Profirence Bark (%)
Profirenoe Raic (N)
Figure 6: Preference rates of our method over the baselines, as judged by human raters GPTs.
rely on heuristics or operate blindly UV space, GOATEX performs structurally informed texturing of all surfaces, resulting in consistent and high-quality outputs across the entire mesh.
4.5 Quantitative Comparison with Baselines
Next, we present quantitative comparison results Fig. GOATex achieves strong and consistent preference from human raters across all comparisons. On the other hand, GPT-based evaluations rate

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its relative advantage lower when compared to TEXTure and SyncMVD. We speculate that this is because GPTs tend favor results with smoother interiors- as those produced by TEXTure and SyncMVD, which either leave the interior unpainted or extend exterior textures inward through texture bleeding or heuristic Voronoi- based filling- over methods that explicitly paint interior regions, including Paint31), TEXGen, and our GOATex. This suggests that GPTs may place greater emphasis on surface smoothness than on interior completeness and consistency.
To further quantify the agreement between human and GPT-based evaluations, we measured two types of metries: (1) correlation using Pearson's 's (-1 S r S 1, where r 1 indicates perfect correlation), and (2) inter-rater agreement using Cohen's 's K (N = 1 indicates perfect agreement). Pearson correlations between GaT- and human ratings were: GPT-4o-mini: 0.22, GPT-4o: 0.31, GPT-4.1: 0.43, and GPT-o3: 0.34. Cohen's 's A. values (averaged over K > 0) were: GPT-to-GPTT 0.54, user-to-user: 0.31, and user-to-GPT: 0.27.
The results reveal two key trends: (1) more advanced GPT models (e.g., GPT-4.1 and GPT-o3) exhibit stronger alignment with human raters, and (2) GPT models demonstrate higher internal consistency than human raters themselves. Interestingly, in 17 individual evaluation cases, user-to-GPT agreement exceeded 0.5, and in two cases, GPT-o3 achieved perfect agreement
1.0) with a human rater, indicating that GPT-based judgments can closely reflect human perception in specific contexts.
4.6 Ablation Study
To better assess the contribution of each component our occlusion-awarc texture generation framework, we conduct an ablation study by progressively adding modules to the baseline texturing method (SyncMVD (27)). Following the same protocol as the quantitative evaluation in Sec. [.55 each ablated variant is compared against SyncMVD via A/B preference tests using GPT-based evaluators. The overall win rates of the ablated configurations over the baseline are summarized in Tab. [
The results show consistent performance gains the proposed components are progressively incor- porated. A slight drop is observed when residual face clustering is applied without normal flipping and backface culling. This behavior is expected, as residual clustering alone cannot fully resolve occlusions. In certain cases, geometrically adjacent faces may be grouped into the same hit level when one fully occludes the other from all external viewpoints. Consequently, the occluded face cannot be textured during its designated rendering stage and is subsequently excluded once that level completes, leading to missing interior details. In contrast, incorporating normal flipping and backface culling effectively suppresses already-textured outer surfaces while exposing previously hidden inner faces, ensuring that occluded regions become visible and correctly textured in subsequent stages.
Table 1: Ablation study of the proposed framework. Each component is cumulatively added on top of the baseline (SyncMVD (27). and evaluated via A/B preference tests using multiple GPT-based evaluators. Scores denote the win rate (%) over the baseline, showing consistent improvements as components are integrated.
Method
4o-mini
40
4.1
Avg.
Baseline (SyncMVD) 27
+ Hit Level Assignment
82.50
66.67
75.00
77.50
75.68
Superface Construction
84.62
70.00
75.86
89.74
80.27
+ Soft UV Merging
79.49
82.05
90.00
95.00
86.49
+ Residual Face Clustering
77.50
72.50
88.89
84.84
81.17
Normal Flipping & Backface Culling (Ours)
86.84
92.31
86.67
97.50
91.16
4.7 Separate Prompt Conditioning for Inner and Outer Surfaces
key advantage of GOATEX is its ability to support distinct prompts for exterior and interior mesh regions: Since textures are synthesized independently for each hit level via our text-guided MVD module, the textual prompt can be specified separately for each level. demonstrate this capability in Fig. [ and Fig. 7 where we assign one prompt to the outermost layer (hit level 1) to control the extenior appearance, and another to deeper layers (hit levels 2) to govern interior textures. As illustrated, GOATEX produces stylistically distinct and contextually appropriate textures for

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seveeredd with snow avttildee
covered with oersslde.
Feverrsd
Exterior
Interior
with candle ongyovved with eurling pattorrss.
A leress framed lextera
with Vislet candle.
With stained glass candle.
Exterior
Interior
Figure 7: Dual prompting for fine- tine-grinne interior regions texturing. Unlike previous work, our framework naturally supports distinct text prompt conditioning for synthesizing semantically coherent and stylistically diverse textures across exterior and interior mesh regions.
interior regions, reflecting the semantics of the specified prompt. This feature introduces new axis of controllability, empowering users induce rich stylistic variation across surface layers through prompt design. It broadens the expressive potential of 3D assets and enables more diverse, imaginative 3D scene generation.
Discussion
We introduced GOATEX, novel diffusion-basee framework for 3D mesh texturing that addresses the challenge of generating high-quality textures for both exterior and occluded interior surfaces. By leveraging ray-based visibility analysis to decompose the mesh into ordered layers, GOATEX enables progressive, occlusion-aware texturing from the outside in. Our two-stage visibility control mechanism ensures structural consistency during layer exposure, while the use of pretrained diffusion models ensures both quality and flexibility. A key strength of our approach is its support for separate prompting of inner and outer surfaces, enabling fine-grained stylistic control and expanding the design space for 3D content creation. Experimental results demonstrate that GOATEX outperforms existing methods in both texture fidelity and semantic coherence, particularly in regions that were previously difficult to access or stylize. We believe that GOATEX opens up new possibilities for controllable, high-fidelity mesh texturing, especially in applications requiring immersive and detailed environments. Future work may explore extending our framework to dynamic scenes, integrating material properties beyond texture, or further improving blending strategies for more complex topologies.
Despite the potential, our method has one primary limitation. Our current hit-level assignment is determined purely by geometric visibility (i.e., ray-intersection depth) and does not explicitly account for semantic coherence. Consequently, in complex geometries such as objects with thin openings or nested cavities, semantically unified regions may be divided across multiple hit levels, which can in turn cause minor texture discontinuities at their boundaries. In practice, however, our residual face clustering, view-dependent normal flipping, and soft UV-space blending effectively mitigate most of these artifacts, producing semantically plausible and visually coherent textures. Nevertheless, incorporating semantic-aware refinement into the hit-level assignment, e.g., grouping superfaces belonging to the same semantic volume using pretrained part- part-segmentation models, could further improve cross-region consistency. Future work may also explore integrating soft blending directly into the denoising process to enable simultaneous multi-view and multi-hit-level texturing for tighter cross-level coherence.
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Acknowledgments and Disclosure of Funding
Kunho Kim was supported by Institute for Information & communications Technology Promo- tion(IITP) grant funded by the Korea government(MSSTT) (RS-2024-00398111 15, Research the reliability and coherence of outcomes produced by Generative Al). Kunho Kim would like to thank Chanran Kim (Pseudo Lab) for the GPU support. Hyunjin Kim thanks Gihyun Kwon, Joo Young Choi, Juhyeong Seon for their helpful discussions.
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For existing datasets that re-packaged, both the original license and the license of the derived asset (if has changed) should be provided. If this information is available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: We use only pre-existing datasets (Objaverse and Objaverse-XI.)
Guidelines:
The answer NA means that the paper does release new assets.
Researchers should communicate the details of the dataset/code/model as part of their
submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [Yes]
Justification: We conducted study using Amazon MTTurk and used the results quantitative evaluation metric.
Guidelines:
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The answer NA means that the paper does not involve crowdsoureing nor research with
human subjects.
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According to the NeurIPS Code Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
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Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [No]
Justification: We do not obtain IRB approval.
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The answer NA means that the paper does not involve crowdsourcing nor research with
human subjects.
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may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing. editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
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Please refer to our LLM policy pattps://neunne wwweeorrsss.c//oooeeeeeeee ooc/oooferences/2225/////////// for what should or should be described.
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Appendix
In the appendix, we present additional implementation details (Sec. ATD. the dataset gathering
(Sec. [2.1] and captioning (Sec. A2.2 process. the evaluation setup (Sec. A3), more detailed
explanation of our method (Sec. [55 limitations (Sec. (S)) broader impacts (Sec.[A7) and additional
qualitative results (Sec. {.). For information on the input text prompts not shown in the main paper
and further results not included here, please refer to the project page llnk
A1
Additional Implementation Details
A1.1 Detailed Experimental Setup
The experiment was conducted using single RTX A6000 GPU, requiring 12GB of memory per inference. The inference time depends on the number of mesh faces and is calculated as (number of hit levels) x (inference time of the texture synthesis model). For hit level assignment, total of 17 cameras were used: the original camera views from texture generation plus an additional top-view camera. Ray casting for hit level assignment was performed at resolution of 1536x1 1536 using the Open3D [58] library. Since we utilize pretrained Depth ControlNet [54] without additional training, no further training training dataset is required.
A1.2 Implementation Details for Baselines
TEXGen 152 need initial UV map that is completely aligned with input geometry. However, since the ground-truth UV map is unavailable for the mesh, we performed initialization through the three methods illustrated in Fig. (Ag[AAto execute TEXGen. All experiments were conducted using the SyncMVD (27 output UV map which produced the most plausible results-aa as the input UV map for TEXGen.
Black texture init.
Front view generation projection init.
SyneMVD init.
Figure A8: TEXGen input UV map ablation.
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A2
Dataset
A2.1 Dataset Gathering
We curate our dataset from Objaverse [1] and Objaverse-XL [11], selecting 226 high-qualit, meshes with detailed interior geometries evaluate our method. To ensure diversity, we focus on 12 object categories that are likely to contain interior structures; box, bucket, bus, cabinet, car, drawer, house, lamp, room, shelf, tent, and truck.
Our primary selection criterion targets objects exhibiting realistic interior geometry at hit levels above 1. This includes types of objects: (I) those with partially visible interior geometry due occlusions in canonical views but are not fully enclosed (e.g.. tents, igloos, half-open boxes), and (2) topologically closed objects whose interior geometries entirely enclosed and occluded from all external views (c.g., houses, cars, buses).
We filter candidate meshes using metadata, searching for relevant keywords in descriptions and retaining only those with hit levels greater than 4. Each selected mesh is then manually inspected to ensure contains realistic and non-trivially simplified interior geometry.
A2.2 Captioning
To generate rich and diverse captions that capture both the exterior and interior features 3D objects, we follow a two-step process involving key visual element identification and prompt generation.
Key Visual Elements Identification. We begin by rendering each mesh from a fixed three-quarter viewpoint at four different hit levels, progressively revealing surfaces from the outermost to the innermost layers. We then task GPT-4o to analyze these multi-layered renderings to identify salient visual elements and structural features specific to each depth level. See Figure A9 for the detailed system prompt for this task.
Prompt Generation. Using the visual features extracted in the previous step, GPT-4o then generates 10 diverse text prompts for each mesh. These captions are crafted to explicitly describe both the external form and internal structure of the object and are later used as conditioning inputs for text-to-texture generation. For the system prompts used in this step, refer to Figure
21

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Prompt Template for Identifying Key Visual Elements and Structures
[System Instruction]
You are given an object mesh file described through two modalities:
1. A caption summarizing the object.
2. Up four rendering images that sequentially reveal the structure of the mesh.
The first image shows the outermost surface the object.
The following images (image2 to image4) gradually reveal interior details of mesh.
Your task is to identify only the most prominent, large-scale objects or structures within the mesh, using both the caption and the rendering images.
Ignore small minor details.
Focus only on components that are visually dominant or structurally significant.
Include any objects or components mentioned in the caption, but remove descriptive modifiers (c.g., wooden cabin + cabin).
Output Format
Your output should in the following ISON format:
"objects": [/* list of object/component names as strings */]
Examples
Input:
Caption: Jungle tent
Images: <imagel>, <image2>, <image3>, <image4>
Output:
{
"objects": ["tent", "candle", "sofa"]
}
Input:
Caption: Vintage
Images: <image1>, <image2>, <image3>, <image4>
Output:
"objects"; ["car", "seats", "handle", "dashboard"]
}
Input:
Caption: hollow pumpkin head
Images: <imagel>, <image2>, <image3>, <image4>
Output:
"objects":
["pumpkin head"]
[User Prompt]
This is the caption: {caption).
These are the rendering images: (imagel) (image2) (image3) (image4).
Now, please identify the objects components in the mesh.
Figure A9: Prompt template used for identifying key visual elements and structures in a mesh.
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Prompt Template for Caption Generation from Object Mesh Files
[System Instruction]
You are given:
A base caption that describes object mesh.
A list of component names (parts the mesh).
Your task is to generate 10 unique and diverse one-sentence captions that describe both the outer and inner aspects of the mesh, focusing on the material, texture, style, and pattern of the components.
Requirements:
Do NOT describe the overall shape structure of the object any of its components.
Do NOT mention colors directly.
Use ambiguous or stylistic terms that imply visual variety (c.g.. "glossy", "worn", "textured",
"transparent", etc.).
Captions must reflect different styles, materials, vibes avoid repetition.
Each caption should reference the object's components, using the provided component list.
The tone and style of the caption can differ from the original (e.g.. modern, fantasy, sci-fi,
surreal, etc.).
Each caption must be single sentence. Output must be in ISON format with the key "ten_capp" ren_captions".
Example 1: Input:
"caption": "Red retro bus sunny day", "objects": ["bus", "seats", "windows"]
Output:
"ten "ten_captinn _am__captions [
"A modern city bus interior filled with molded plastic chairs
"A vintage tour bus with upholstered velvet chairs arranged in
"A futuristic electric bus with sleek metallic chairs glowing
"A school bus interior lined with simple padded chairs and acratched
"A fantasy woodland bus with carved wooden chairs and vines wrapping
"A luxurious travel bus with reclining leather chairs and soft ambient
"A post-apocalyptic bus interior with mismatched salvaged chairs ....,
"A steampunk bus with brass-framed chairs covered in worn leather..."
"A magical flying bus with floating chairs made of translucent crystal"
"A retro sci-fi bus interior with bubble-shaped plastic chairs and
]
[User Prompt]
This is the original caption: (caption).
These are the list of componetns: (components),
Now, please generate 10 unique and diverse one-sentence captions that describe both the outer and inner aspects of the mesh, focusing on the material, texture, style, and pattern of the components.
Figure A10: Prompt template for generating stylistic captions from object mesh data.
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A3
Evaluation Setup
As described in Sec. of the main paper, we report preference statistics based on responses from user study and GPTs. Below, we provide additional details about the user study setup and protocol. Specifically, following the visualization style used on our project page, we rendered for each method:
one GIF showing the exterior of the object, and GIF showing the interior via cut-away or sliced view.
These side-by-side visualizations were shown to participants or GPTs, providing one exterior and one interior view per method per object. We report results from 16 human raters who passed a vigilance test. The sereen example of the user study is illustrated in Fig. A1l
To enable a more scalable preference analysis, we additionally conduct an automated evaluation using a family of GPTs, including GPT-4o-mini, GPT-4o, GPT-4.1, and GPT-o3. For this study, we sample a total of 400 tasks. To eliminate potential bias due to ordering, each comparison is evaluated in both A/B and B/A presentation orders. Moreover, the two judgments from opposite orders disagree, the evaluation is repeated up 10 times until consistent decision is reached. Tasks that still yield inconsistent results after 10 repetitions are excluded from the final analysis. The detailed system prompt used for this evaluation is shown in Fig. A12
Human Intelligence Task: Visual
Assessment on Mesh Texture Generation Models (20 mins)
This HIT is for the A/B test of the text-to- teettt--eexture methodology, and for this purpose, shows the external and internal textures of the 3D object generated by each method at the same time.
videos [left(outer& inner), right(outer inner)). For each set, videos generated from the same mesh (3D model) and text prompt. Given such videos Left, and Right, choose a video that you think more plausbile.
Participants
When choosing between models,
priority
First, choose the model that successfully textured both the exterior and interior without missing any parts.
2. If both are similar in that regard, choose the model whose textures-bottl inside and out -are better aligned with the prompt.
they are comparable, choose judgment.
visually more appealing your
1. mystic's tent with deep purple silk drapes embroidered in constellations, and black-lacquered desk topped glowing runes.
Left
Right
Figure A11: Screen example of user study. The example depicts evaluation guidelines and question sample used in the user study.
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Instruction for Evaluating Textured Mesh
[System Instruction]
You are given text caption and two sets of rendered images generated by two anonymized text-to-texture models (Model and Model B). Each model attempts to synthesize realistic textures for both the exterior and interior of 3D object based on the given caption.
Each model provides:
external renderings, showing the textured outer surface of the object from different view- points.
internal renderings, showing the textured interior structure, such as cutaways or cross-
sections.
Your task is to:
Carefully read the text caption, which may describe details about the object's outer appearance, internal textures or structure, or both.
Evaluate how well each model's textures match the caption:
Use the external renderings to judge how well the outer textures reflect the description.
Use the internal renderings judge how well the interior textures structures align with the caption.
Compare the two models holistically and determine which model (A or B) better captured the textures described in the caption, for both external and internal parts of the object.
Important Notes:
The model names 'A' and 'B' anonymized. You must avoid any bias based the label
itself.
Your judgment should be based only on the alignment between the caption and the textures shown in the renderings.
Output Format
Return the of the model that performed better; A B.
[User Prompt]
This is the prompt: prrmppt).
Here are rendering images for the Model A. Outer: (imagel) (image2) (image3). Inner: (image4)
{image5) (image6)
Here are rendering images for Model B. Outer:
[imagel] (image2) (image3). Inner: (imago4)
{image5) {imageó]
Now, please choose model A or model B based on the caption and the images provided.
Figure A12: Instruction for evaluating GPT-based A/B test.
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A4 Runtime Analysis
systematically analyzed the runtime our pipeline across meshes with increasing face counts by progressively subdividing mesh faces of five representative assets. For each mesh, we measured the number of faces and superfaces, time spent on superface construction, hit-level assignment, and MVD- based rendering & synthesis. The table A2 shows the average runtime across the assets. Note that hit-level assignment is reusable when generating multiple variants, so we can preprocess the mesh before texturing.
Table A2: Runtime analysis of each module according the number faces.
Superface Construction (s)
Hit-Level
Faces
# Superfaces
Texturing (s)
Assignment (s)
5k
196.0
0.23
259.57
117.93
10k
221.2
0.46
295.38
121.80
20k
247.6
0.86
314.16
130.42
40k
433.0
1.87
394.67
146.45
80k
586.0
3.99
539.68
197.29
160k
2033.2
18.59
942.36
549.83
320k
5418.0
57.76
2563.35
2769.22
A5 More Detailed Method Explanation
0 > 90
0 < 90%
Frontface (Rendered)
Backface (Culled)
Figure A13: Rendering with backface culling.
Mesh & Texture Representation. In standard mesh representations, each face is a single-sided surface that does not distinguish between its front and back; both sides appear visually identical. This means that any texture or color applied to one side of face is also visible from the opposite side. Consequently, mesh face does not have separate visual properties for the inside and outside; regardless of the viewing direction, the appearance remains the same.
To allow for different appearances on the two sides surface, such as when modeling an object with distinct exterior and interior textures, it is common to place two faces very close together, back back. The outer face, slightly offset toward the exterior, is meant to be visible from outside the object, while the inner face is offset inward and intended to be seen from the inside. This setup allows each side to use its own texture and material properties. For example to represent both the painted exterior of bus and its interior wall surface, the mesh includes one face for the outer shell and another for the inner wall. Each face can textured independently to reflect its visual role.
Rendering with Normal Flipping & Backface Culling. During rendering with backface culling, faces whose normal vectors point away from the camera are not rendered. More precisely, as illustrated in Fig A13 when the angle 0 between the viewing ray and the face normal falls within the
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Faoe
Culled (lnvixbbee
Occladed (lavisble)
Vanter
Flippod Normal Vadee
2D View
2D View
Frandan Nendard
Before Normal Flipping
(2) After Nornal Flipping
Figure A14: Progress Normal Flipping & Backface Culling. By flipping the normals and rendering with backface culling, we successfully view the interior geometry while keeping the object boundaries visible, thus preserving the overall object identity and structure.
range- -00 < 8 < 90", the face is considered back face and is culled from the rendering process. As a result, flipping the normal of face that was previously visible causes to become hidden, effectively simulating the removal of the face from view. We use this property to progressively reveal the internal geometries while preserving the object's overall shape and structures during subsequent rendering stages.
We further illustrate this process in Fig, A14 which depicts the current outermost layer face clusters rendering stage for hit level ls (i.e., Fi). divided into two regions colored in orange and green, along with the occluded internal geometry shown blue. For simplicity, we assume a single fixed camera viewpoint. Initially, the normals of F& are oriented outward, making only the orange region-being front-facing relative to the camera camera-visible and rendered, while the internal geometry remains hidden. In the next step, the normals of F& flipped inward. As result, the orange region becomes back-facing with respect to the camera and is culled during rendering, thereby revealing portions of the previously hidden internal geometry. Simultaneously. the green region of FR. which was initially back-facing and thus invisible, now has its normal oriented toward the camera. This allows the green region become visible, unless it remains occluded by inner layers. This progressive rendering approach preserves the continuity of the overall geometry while revealing deeper layers, maintaining the object's global structure, boundary, and identity.
One potential concern arises when some internal mesh faces have normals directed inward, toward the object center, rather than outward. In such cases, if these outward to make the faces visible from the camera, they can occlude deeper surfaces within the object, preventing access to further interior geometry (Fig. A15 (1)). However, this issue does not arise practice due to the way hit levels are assigned. Specifically, hit levels are computed based on weights derived from Eq. [I] which use the negative cosine of the angle between face normals and ray directions (see Fig 16 As result, faces that are originally back-facing receive hit levels in the reverse order of intersection along the ray path, illustrated in Case 2 of Fig. A15 This property ensures that when normal flipping is combined with backface culling, valid hit level always exists that exposes each layer of interior geometry. Consequently, both exterior and interior surfaces be progressively revealed and fully textured (Fig. (2))
normals are flipped
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Rendered Face (Visible)
Normal
Camern Ray
Occluded Face (Invisible)
Flipoed Noemal Vector
Culled (Invisible)
coclusion by the face at leval 2.
2
2
(1) Case 1
3
4
(Interior)
(Interior)
Hit Level
Higher Hit Level
Interior visible.
N-1
N-1
(2) Case 2 (Ours)
N-3
N-3
(Interior)
(Interior) Higher Hit Level
Hit Level
Figure A15: Visibility According to Face Hit Level Assignment. When face normals are complexly oriented and hit levels assigned sequentially from the exterior inward (as in Case 1), applying normal flipping and backface culling during rendering results in occlusion caused by faces at Hit Level 2. preventing visibility into interior regions. Conversely, in Case 2, where faces closer to the camera ray are assigned lower hit levels if they are frontfaces and higher hit levels if they are backfaces, complete visibilitv of interior regions can be achieved.
Level.N,
Hit Level = 0.9
Hit Level N-1, W 0.8
Hit L-v, w
3, 0.8
Hit LeveL W
(Interior)
Figure A16: Hit Level Assignment Based on Normal Direction. For faces close camera rays coming from outside the object (left side), backfaces -wheaeeess--heee whose normals align similarly with ray directions-aae are assigned weight of zero, resulting in hit level assignment. In contrast, rays approaching from the opposite direction (right side) encounter these same faces as frontfaces. thereby receiving hit level assignments. However, due their distance from the camera, these faces assigned higher hit levels.
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A6 Limitations
Our approach has few limitations. While GOATEX is effective at generating high-quality interior textures, its reliance on the SD 1.5 backbone can occasionally result in suboptimal adherence to input text prompts. future work, we plan to integrate more lightweight yet more capable pretrained diffusion models to enhance prompt fidelity and further improve texture quality.
Our current hit-level assignment determined purely by geometric visibility (i.e., ray-intersection depth) and does not explicitly account for semantic coherence. Consequently, in complex geometries such objects with thin openings nested cavities, semantically unified regions may be divided across multiple hit levels, which can in turn cause minor texture discontinuities at their boundaries. In practice, however, our residual face clustering, view-dependent normal flipping, and soft UV- space blending effectively mitigate most of these artifacts, producing semantically plausible and visually coherent textures. Nevertheless, incorporating semantic-awar refinement into the hit-level assignment, e.g., grouping superfaces belonging to the same semantic volume using pretrained part- segmentation models, could further improve cross-region consistency. Future work may also explore integrating soft blending directly into the denoising process enable simultaneous multi-view and multi-hit-level texturing for tighter cross-level coherence.
A7 Broader Impacts
Potential Positive Societal Impacts. It will be possible further accelerate the existing 3D asset creation pipeline and generate a wider variety of assets. With these assets, we can expect to build realistic AR/VR environments. As result, people will be able enjoy a broader range of experiences.
Potential Negative Societal Impacts. Texture generation models present potential risks, such as the synthesis deepfakes, textures resembling copyrighted content, or biased and discriminatory textured meshes. Future work should focus developing robust mechanisms mitigate these risks and enforce safeguards that prevent the generation of harmful or unethical outputs.
29

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A8
Additional Qualitative Results
More qualitative comparisons can be found in Fig. A17 and additional qualitative results are shown in Fig. A18 For more detailed presentation including videos of the results and further outcomes please see this link
Input Mesh
TEXTure
SyncMVD
Paint3D
TEXGen
Ours
folinr
Loorkes desert maalt Booing encaressional wi brees arp sostor Pyn lay pafos with coclives.
refonddered ceptiilatios, Marielaaquarve deçk cloaknn
glma
stedp rar aker forege deariey
pook drood aecrete leeker reeare with tradis and Meds.
Intetiiircd weadee beass sith rasmel calls, arvdlsed sid eg.
Figure A17: Additional Qualitative Comparison. You can view a video of the results and find more qualitative outcomes this link
30

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A coral reef hovse with prismaring wells, bubile tolvtens, and elawtnng seal leiterns.
A Howttosy-themed hemee theater with moral-poirree wells, canop4-covered searts, and carved rimes lining the aisles.
A poliiter's willity chest splattered with Leyoresi sigment stainn and locavored ever for textire..
A bohemian artisan's is toolbok paliened in lwered mondala motifss and distrossed edges.
A marble-top diresser with polished stows drewers and gjided trim along eveny edge.
A gothric wardrolee with muthre black weod, silver islaid fileree, and from clow Vinays.
A larquered oriental wardrolee with palirred crakes and wave Wottif on the lindr baak panels.
A fouitasy laittnn wrapped in silver brandhes, with ai blue olje-ingwing exchanied candle at core.
A clessic car featuring wire-spoko whncols ovilted leatiner intertor, and brass instrmmet dials.
A lowee with statnce glass pamels arravged in floral patterns and out darkc soldored scams.
Figure A18: Additional Qualitative Results. You can view video of the results and find more qualitative outcomes at this link
31


Reviews:
[{"review_id": "y64nzFN2V5", "reviewer": "Reviewer_imB6", "summary": "This paper presents a diffusion-based method for texturing shapes that contain both exterior and interior surfaces. Prior work on diffusion-based shape texturing typically renders a camera view, making use of a depth conditioned multi-view diffusion model to generate uv-textures. This work points out that such a pipeline will generally render (and hence texture) only exterior surfaces, but will fail to texture the interior of a car or building shape. The key architectural innovation is to segment a 3D shape into an \"onion-layered\" collection of surfaces ordered from outside-in. Such segmented shapes can be used to generate segmented depth maps, which can then be combined with depth-conditioned multi-view diffusion models to generate outside-in ordered uv-texture maps (which are then be blended back to obtain a fully inside-out textured model).", "questions": "Please answer the first \"weakness\" above, regarding volumetric texture models.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n\n- The problem formulation is quite creative and the execution seems solid. The rendered visuals are simply fun to look at!\n- This paper includes a link to a github project page, which may or maynot be allowed under the submission guidelines. I personally found the visuals quite helpful.\n\nWeaknesses\n- It would be interesting to compare to NERF-like approaches that learn a full volumetric texture model rather than a uv-based texture model. Such texturing pipelines can naturally model interior surfaces simply by rendering an interior camera, or even a \"cut-away\" view such as those shown in the final visuals. Examples of such volumetric (and not uv-based) texturing approaches include FlashTex and Fantasia3D. Could such methods be more readily adapted to modeling interior surfaces?\n- Minor gripe: I find the term \"occlusion-aware texturing\" to be somewhat ill-defined, since occlusion is associated with a camera viewpoint but shapes are full 3D. One could use it in the sense of view-dependant (specular) materials, but there already exists a large body of work in this vain. I think the more proper term is texturing of models that allow for diverse camera viewpoints, including interior cameras and \"cut-away\" cameras.", "was_revised": true, "final_justification": "This is a nice submission.", "rebuttal": "We sincerely thank the reviewer for their positive and encouraging feedback. We’re glad that the problem formulation and execution were found to be creative and solid, and that the rendered visuals were enjoyable and helpful.\n\n---\n\nQ1: It would be interesting to compare to NERF-like approaches that learn a full volumetric texture model rather than a uv-based texture model. \n\nA1: We appreciate the reviewer’s suggestion to consider NeRF-like or volumetric texturing approaches such as FlashTex and Fantasia3D. As noted, in principle, these methods can support interior or cut-away views and can be combined with diffusion-based supervision (e.g., SDS loss) to optimize radiance fields for interior volumes or regions.\n\nHowever, applying such methods directly without additional geometric and occlusion-aware constraints can be problematic. In practice, when training is performed primarily from a set of fixed external views, NeRF-like models tend to accumulate high density at the outermost surfaces. This effectively blocks camera rays from reaching the interior, preventing supervision signals from propagating to inner geometry. As a result, color radiance in these regions often remains under-constrained or unlearned.\n\nOne might attempt to mitigate this by gradually moving cameras inward or narrowing the frustum to expose internal volumes. However, without access to mesh structure, there is no guidance on how to move the cameras or which regions of the volume to expose. This often leads to indiscriminate optimization over the entire volume, introducing spurious geometry and severely degrading training efficiency. Even worse, NeRF-like models may recursively form nested high-density pseudo-surfaces, i.e. much like a troika structure, resulting in a degenerate, continuous shell-like volume that fails to capture meaningful interior surfaces.\n\nOn the other hand, if mesh structure is available, it becomes possible in principle to expose and supervise surface layers in a controlled, outside-in manner. But doing so effectively still requires an explicit mechanism to identify occluded geometry at each depth and to organize supervision accordingly. This is precisely the functionality that our method introduces through hit-level assignment and visibility-depth analysis.\n\nThus, we would like to emphasize that the core challenge in interior surface texturing is not the choice of representation (e.g., UV-based vs. volumetric), but the ability to perform geometry- and occlusion-aware visibility control. Once such control is established, the decision to optimize a UV texture map or a volumetric radiance field becomes secondary. Our framework provides the foundational mechanism required for either setting, and could be integrated into volumetric pipelines as a principled method for layered, surface-aware supervision, particularly in the presence of occluded or complex internal geometry.\n\n---\n\nQ2: Potential misleading use of \"occlusion-aware texturing\"\n\nA2: We thank the reviewer for pointing out the potential ambiguity in the term “occlusion-aware.” \n\nWhile occlusion is often treated as a view-dependent concept, it can also be view-invariant in the case of structurally enclosed interior regions. For instance, exterior surfaces may or may not be visible depending on the viewpoint, but interior surfaces that are fully enclosed by outer geometry remain occluded from all external views, regardless of camera position.\n\nGOATex is specifically designed to address such occlusions. Our method explicitly identifies these deeply embedded, structurally occluded regions (i.e., faces that are never visible in standard multi-view setups) and progressively reveals them via ray-based visibility analysis. By decomposing the mesh into ordered hit levels and applying layer-wise texturing with blending, GOATex provides a geometric, structure-aware approach to handling occlusion that complements and extends conventional view-aware techniques.\n\nIn that sense, we believe the term “occlusion-aware” is appropriate, as our method does not rely solely on view-based visibility cues, but instead explicitly identifies and handles regions that are consistently hidden due to the mesh’s geometric enclosure. That said, we understand the concern and are open to adopting alternative terminology in future revisions to avoid confusion with view-dependent material modeling."}, {"review_id": "56LxeHRqIQ", "reviewer": "Reviewer_nix7", "summary": "The paper introduces GOATex, a diffusion-based framework for 3D mesh texturing that addresses the challenge of generating realistic textures for both exterior and occluded interior surfaces. Existing methods struggle with interior texturing due to limited access to occluded geometry, often resulting in incomplete textures and visible seams. GOATex leverages ray-based visibility analysis to decompose the mesh into ordered layers, enabling progressive texturing from the outermost to innermost regions. Through extensive experiments, GOATex outperforms existing baselines, generating high-quality and seamless textures for both visible and occluded surfaces of untextured objects.", "questions": "When dealing with an object that is not entirely closed, such as a vase with a small opening at the top, how should we assign the hit level? Specifically, does the inner bottom of this vase belong to Hit Level 1, while other inner surfaces belong to Hit Level 2? In such scenarios, a critical challenge arises: how to ensure that the texture of the inner bottom aligns with the textures of other inner surfaces that belong to Hit Level 2, thereby maintaining a consistent and coherent visual appearance?", "limitations": "As elucidated in the Weaknesses section, GOATex may face significant difficulties when dealing with substantially more complex 3D meshes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n1. The authors pose a thought-provoking question, namely, generating realistic textures for occluded surfaces of 3D meshes, and bring this issue to the forefront of academic scrutiny.\n2. Novel and effective Occlusion-Aware Approach. The proposed Occlusion-Aware Approach, termed GOATex, introduces a novel and effective paradigm for texturing 3D meshes, employing a ray-based and layer-by-layer strategy that progresses from the outermost to innermost surfaces.\n3. The comprehensive evaluation, including a user study, demonstrates the efficacy of this method in generating realistic textures for both visible and occluded surfaces\n\n**Weaknesses**\n1. The approach struggles to effectively handle complex 3D meshes comprising multiple layers.\n2. A notable limitation of the method is that certain occluded areas on interior surfaces are neglected, as exemplified by the back of chairs on the bus shown in Figure 2.\n3. The framework lacks a clear mechanism for adaptively adjusting the corresponding text prompt in response to varying hit levels.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for recognizing the importance and novelty of the proposed task. We appreciate the encouraging feedback on our occlusion-aware approach and the overall evaluation.\n\n---\n\nQ1: The approach struggles to effectively handle complex 3D meshes comprising multiple layers.\n\nA1: We thank the reviewer for raising this point. \n\nScene complexity arises from various factors, including the number of semantic entities, their spatial arrangement, and the underlying geometry. As the number of semantic entities increases, the mesh tends to become more layered and entangled, making it increasingly difficult to disambiguate structures and generate proper textures across them. Furthermore, a high face count also complicates UV mapping, which can negatively affect texture quality and consistency.\n\nIn our current setup, the evaluated meshes span a broad range of semantic and geometric complexity, from simple to highly intricate cases. While it is not straightforward to define or quantify the number of \"layers\" in a scene, each mesh contains on average about 1.35 semantic entities, with some containing up to 7. The geometric complexity also varies significantly, with a median face count of 8.5K and a maximum of 166K.\n\nQualitatively, our method is demonstrated to perform well even on meshes with such multiple internal structures and layers. For instance, in the car example shown in Figure 6 of the main paper, components such as the steering wheel and seats are relatively small yet semantically important. While these parts are challenging to isolate and texture individually, our method (GOATex) is able to produce plausible textures, whereas baseline methods fail to generate meaningful results in such settings. Additionally, as illustrated in row 3, column 1; row 6, column 1; and row 6, column 3 of our project page, our approach generates coherent and prompt-aligned textures even in scenes with multiple internal or layered components. This is further supported by our quantitative evaluation, where our method is strongly preferred over baselines by both human raters and GPT-based assessments.\n\nThat being said, we acknowledge that even more compositionally rich and deeply nested configurations could be imagined. For example, consider a children's bedroom scene containing a closed toy chest filled with plush dolls, a bunk bed with built-in drawers and under-bed compartments, and a ride-on toy car with fully modeled interior components. Such scenes involve numerous interleaved objects with varied spatial and semantic relationships, leading to extreme geometric and semantic complexity.\n\nHowever, due to this practical consideration, we chose not to focus our evaluation on such a setup, as texturing highly nested and composite scenes all at once is rare in typical workflows. In practice, these environments are more effectively and commonly constructed by texturing individual components with moderate levels of hidden internal structures (e.g., a closed toy chest, plush dolls, a bunk bed with built-in drawers, fully enclosed ride-on toy cars) separately, and then combining them into a larger scene graph or hierarchical assembly. Automating this compositional process remains a promising direction for future work.\n\n---\n\nQ2: A notable limitation of the method is that certain occluded areas on interior surfaces are neglected, as exemplified by the back of chairs on the bus shown in Figure 2.\n\nA2: We thank the reviewer for the comment. We believe the concern may stem from the specific view shown in Figure 2 in the main paper, where only the front of the chairs is visible, and the backs are not shown from that particular camera angle. However, we would like to clarify that in our method, each mesh face is assigned a hit level based on multi-view ray casting from a densely sampled hemispherical camera rig (as mentioned in Section 4.1, L195). This setup ensures that all visible and occluded surfaces (including the backs of interior objects) are intersected by at least one ray and are thus included in the progressive texturing process.\n\n---\n\nQ3: The framework lacks a clear mechanism for adaptively adjusting the corresponding text prompt in response to varying hit levels.\n\nA3: Thank you for bringing this up. Upon reviewing our submission, we acknowledge that we did not clearly describe how dual prompting is enabled within our framework and we appreciate the opportunity to clarify it.\n\nAs described in Section 3 (L172–174), we generate a texture for each hit level by rendering multi-view depth maps of the geometry corresponding to that level and synthesizing textures using a text-guided MVD module. Then, as noted in L178, the resulting textures from each hit level are merged into a unified UV texture map via visibility-weighted blending.\n\nThis design naturally enables layer-specific prompting: since the MVD module is conditioned on depth maps and a textual prompt independently per hit level, the prompt used at each level can be chosen independently. In practice, we assign a distinct prompt to the outermost layer (hit-level = 1) to control the exterior appearance, and a different prompt for hit levels ≥ 2 to control interior textures. This mechanism supports what we call dual prompting, as shown qualitatively in Figures 1 and 7.\n\nWe will make this sufficiently explicit in the revised version of the manuscript.\n\n--- \n\nQ4: Potential issues in maintaining texture coherence when faces within a semantically coherent region are assigned to different hit levels\n\nA4: Thank you for the thoughtful question.\n\nAs the reviewer correctly points out, our current hit level assignment is purely geometric, based on ray intersection depth, and therefore agnostic to semantic information. In certain edge cases, such as a vase with a narrow opening, semantically coherent interior regions (e.g., the connected inner surface) may be assigned to different hit levels due to partial visibility from external viewpoints. This could, in principle, result in textural discontinuities across those regions.\n\nHowever, in practice, we empirically observe that our method still produces semantically plausible and coherent textures. We attribute this to several key factors:\n1. Residual face clustering, combined with view-dependent normal flipping and backface culling (Section 3.2), preserves structural continuity across hit levels in the rendered depth maps. As shown in Figure 3 (right), although each hit level targets a distinct subset of faces (particularly those previously occluded), our method consistently maintains the overall geometry, retaining a globally coherent silhouette and shape in the depth maps. This inter-level consistency ensures that the diffusion model receives a stable geometric context at each synthesis stage, even though the specific regions being textured differ. As a result, as shown in the rendered textures in Figure 3 (right), semantic coherence is naturally preserved across hit levels.\n2. In addition, our soft UV-space blending strategy (Section 3.3) further reinforces this continuity by smoothly interpolating textures across hit levels using view-dependent visibility weights. This weighted blending mitigates hard seams and ensures stylistic consistency in regions where semantic surfaces span multiple hit levels, as can be seen in Figure 4.\n\nTogether, these two components work in concert to ensure that our method produces coherent and semantically aligned textures, even in cases where semantically unified regions are geometrically split across hit levels.\n\nThat said, we acknowledge this limitation and view it as a promising direction for future work. Specifically, incorporating semantic-aware refinement of hit levels, for example by further grouping superfaces that belong to the same semantic volume with pre-trained part segmentation modules [*], could further improve consistency in such edge cases.\n\nAdditionally, while not explored in our current implementation due to computational resource constraints, an even more integrated strategy could involve performing soft UV-space blending during each denoising process itself, rather than after hit-level-specific texture synthesis. This would enable simultaneous multi-view and multi-hit-level texturing, potentially allowing for tighter coherence across depth layers.\n\nWe thank the reviewer for highlighting this nuanced and important point, and will clarify this limitation and potential mitigation strategies in the revision.\n\n[*] Wu et al., Point Transformer V3: Simpler, Faster, Stronger, In CVPR, 2024"}, {"review_id": "WJQXGCYHMB", "reviewer": "Reviewer_KoBr", "summary": "Current texture generative models overlook one important problem: generating textures also inside the objects. The proposed method is arguably the first to explicitly tackle this problem by proposing a combination of several clever strategies to decompose complex meshes and texture their interior parts. They introduce the following steps: a superface construction, a hit level assignment performed with ray casting, progressive visibility control and a per-level UV blending. None of the novel components need to be learned and are used in conjunction with an existing pre-trained texturing neural network (MVD). An extensive qualitative evaluation was paired with user and GPT-based studies.", "questions": "Besides mentioning the alternative techniques mentioned in the weakness section, I think the following questions need to be addressed:\n1. The images used for evaluation (reported in A3) depicted 3 images representing the interiors and only one image representing the exterior. Considering that the proposed method is the only one specifically designed to texture interiors, I would consider the test slightly biased. I think that showing 2 images of the interiors and 2 of the exteriors or simply separating them would have been a fairer assessment. \n2. The authors mention that other metrics like FID, KID, etc. are not well suited. I wonder if comparing against real or generated interior images could have been an option. \n3. What is the number of participants recruited in the user study? I don't seem to find it either in the paper or in the supplementary materials.\n4. It would be interesting to know where humans and GPT disagree and see some case where the other methods are better? Were the authors able to identify some patterns?\n5. A potential limitation may be the computational overhead for the full pipeline, but this information is poorly reported. Authors mention in the appendix that the computational time depends on the number of faces, but do not provide estimates. I think that it would be nice to report a plot reporting the number of faces on the x-axis, and the computational time of all the proposed non-learning-based components on the y-axis. Additional information on the computational cost of MVD would also make the paper more self-contained.\n\nAlthough I already lean towards acceptance, I would be extremely eager to further increase my score if these questions and the limitations were properly addressed.", "limitations": "I know that some limitations have been mentioned in the Supplementary materials, but they appear rather superficial. Every method has some limitations, and I would be interesting to hear the author’s opinion on some limitations of their proposed method. In the supplementary materials the main limitations are attributed to MVD, which is not the core contribution of this work. I would suggest being inspired by Section 2 of the paper checklist. Also, refer to Question 5 as a potential point of discussion.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "### Strengths \n- The paper is generally well written and provides enough information to aid reproducibility\n- The proposed method offers a non-learning-based solution that can be potentially leveraged by multiple image-based texture generators without any fine-tuning or training. \n- Authors provided a very well-curated (anonymous) project page providing more results and animations. I am a strong believer that papers should be self-contained, therefore I consider it to be a nice addition which is not part of my assessment but still wanted to thank the authors for the additional effort.\n\n### Weaknesses and Suggestions\n- I think that it would be worth mentioning some methods that are not leveraging pre-trained image models and adopting different representations [i, ii, iii, iv]. These may be intrinsically capable of texturing objects inside, but in most cases given the more challenging generation settings (they do not operate on images), quality improvements would be needed for competing. Therefore, I think comparisons would be unnecessary. Yet mentioning alternative representations would make the related work and/or introduction more comprehensive. \n- I have a few reservations for the Experiment Section, which are later detailed in the Questions section.\n- I think that the limitations are not well analyzed. See Questions section. \n\n### Minor Comments and Suggestions\n- Adding \\\\( F_{k=0, ..., 3}^\\text{init} \\\\) and \\\\( F_{k=0, ..., 3}^\\text{res} \\\\) to Fig. 2 part 3 may aid the reader while reading Sec. 3.2. This addition may be part of the caption as well.\n- Figure 3 should be ideally placed in Sec. 3.2 and Figure 4 in Sec. 3.3.\n- In Figure 5, I believe the former name of the method (probably later renamed GOATex) is reported in all histograms.  \n- line 266: proressive -> progressive\n\n\nPS. It would be a pity if the authors confirmed their decision not to release their code, it would make the paper considerably more impactful. This is obviously not part of the assessment, and it is perfectly understandable if this is not possible. \n\n\n---------------\n\n[i] Yu, X., Dai, P., Li, W., Ma, L., Liu, Z., & Qi, X. (2023). Texture generation on 3d meshes with point-uv diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4206-4216).\n\n[ii] Foti, S., Zafeiriou, S., & Birdal, T. (2024). Uv-free texture generation with denoising and geodesic heat diffusion. Advances in Neural Information Processing Systems, 37, 128053-128081.\n\n[iii] Wei, J., Wang, H., Feng, J., Lin, G., & Yap, K. H. (2023). Taps3d: Text-guided 3d textured shape generation from pseudo supervision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 16805-16815).\n\n[iv] Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., & Geiger, A. (2019). Texture fields: Learning texture representations in function space. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4531-4540).", "was_revised": true, "final_justification": "The authors properly addressed all my questions. I am happy to increase my score.", "rebuttal": "We sincerely thank the reviewer for the detailed and constructive feedback. We believe the combination of precise editorial suggestions and thoughtful technical insights will significantly contribute to improving the clarity, depth, and completeness of the work.\n\n---\n\nQ1: About the code release\n\nA1: Absolutely! We are happy to release the full codebase upon publication to support reproducibility and encourage further research.\n\n---\n\nQ2: About alternative approaches that do not rely on pre-trained image models and use different representations.\n\nA2: Thank you for the suggestion. We agree that mentioning such alternative methods would make our related work more comprehensive. We will revise the manuscript to include a brief discussion of these representations in the Related Work section.\n\n---\n\nQ3: Minor suggestions on clarifying notation in Fig. 2, repositioning Figures 3 and 4, correcting a method name in Fig. 5, and fixing a typo.\n\nA3: We sincerely appreciate your attentive and thoughtful comments. We will reflect suggested edits in the revision.\n\n---\n\nQ4: The number of participants recruited in the user study.\n\nA4: The number of participants is reported in the supplementary material, Section A.3.1, line 58. For clarity, we summarize them here:\n+ vs TEXTure: 21 participants\n+ vs SyncMVD: 33 participants\n+ vs Paint3D: 30 participants\n+ vs TEXGen: 45 participants\n\n---\n\nQ5: Conducting FID/KID-based evaluation by comparing against real or generated interior images.\n\nA5: Thank you for the suggestion.\n\nFollowing the reviewer’s recommendation, we conducted an FID-based evaluation using pseudo-reference images synthesized with depth-conditioned Stable Diffusion (SD 1.5), as the ground-truth meshes lack realistic interior textures. Notably, all evaluated methods are also based on SD 1.5.\n\nTo generate pseudo-references, we rendered depth maps from the ground-truth meshes (per view and hit level) and used depth-conditioned SD 1.5 to synthesize reference images. These were used to compute FID scores against the corresponding outputs of each method, separately for exterior (hit level 1) and interior regions (hit levels ≥ 2). We averaged scores across views and meshes:\n\n| Method | FID (Exterior, ↓) | FID (Interior, ↓) |\n|---|---|---|\n| SyncMVD | **26.07** | **28.36** |\n| TEXTure | 26.23 | 28.65 |\n| Paint3D | 29.05 | 29.86 |\n| TEXGen | 30.74 | 31.21 |\n| Ours | 30.75 | 30.09 |\n\nWhile lower Interior FIDs of TEXTure and SyncMVD may appear favorable, they are misleading: (1) TEXTure leaves interiors blank or smooth, with occasional exterior texture “bleed” through mesh holes; SyncMVD extrapolates exterior colors via Voronoi heuristics. Neither produces semantically meaningful, prompt-aligned interiors. (2) The pseudo-references for hit levels ≥ 2, synthesized from single-view depth, lack multiview consistency and occlusion reasoning, often blending exterior and interior content, unlike MVD-based approaches in SyncMVD and ours.\n\nThese low Interior FIDs largely reflect (1) exterior-like texture continuity in methods without actual interior synthesis, and (2) pseudo-references biased toward such appearances. In contrast, methods that generate semantically meaningful interiors may deviate from these references and be unfairly penalized. We will include this analysis and qualitative examples in the revised manuscript.\n\nRegarding exterior FID: Although our method scores higher, this is not a core limitation. GOATex specifically targets the challenging task of synthesizing textures for occluded interiors (hit-level ≥ 2), which prior work does not address. As GOATex is modular by design, high-fidelity exterior rendering (hit-level 1) can be integrated using existing methods. Our contribution thus complements, rather than replaces, current pipelines.\n\n---\n\nQ6: Concern about potential bias in the qualitative evaluation due to imbalance between interior and exterior images.\n\nA6: Thank you for raising this important point.\n\nTo address the concern regarding evaluation balance, we revised both the user study and GPT-based evaluation setups to ensure fair and unbiased comparisons between interior and exterior texturing. Specifically, following the visualization style used on our project page, we rendered for each method:\n+ one GIF showing the exterior of the object, and\n+ one GIF showing the interior via a cut-away or sliced view.\n\nThese side-by-side visualizations were shown to participants or GPTs, providing one exterior and one interior view per method per object. Due to time constraints, we report results from 16 human raters who passed a vigilance test, with plans to expand this pool further. To compensate for this, we included four GPT variants, GPT-4o-mini, GPT-4o, GPT-4.1, and GPT-o3, and repeated each evaluation four times to reduce variance and improve robustness.\n\nBelow are the aggregated preference rates (%) for GOATex over each baseline:\n| Baseline | Humans | GPT-4o-mini | GPT-4o | GPT-4.1 | GPT-o3 |\n|---|---|---|---|---|---|\n| TEXTure | 86.11 | 51.56 | 50.60 | 43.42 | 55.13 |\n| SyncMVD | 87.15 | 64.79 | 70.51 | 69.62 | 72.84 |\n| Paint3D | 83.33 | 85.51 | 83.13 | 82.72 | 73.75 |\n| TEXGen | 96.53 | 87.50 | 84.71 | 88.31 | 85.19 |\n\nWhile GOATex received strong and consistent preference from human raters across all comparisons, its relative advantage diminished when compared to TEXTure and SyncMVD in evaluations conducted with GPT-4 variants. We elaborate on this observation in A5.\n\n---\n\nQ7: About disagreement patterns between human preference and GPT, and cases where other methods perform better.\n\nA7: Thank you for the interesting question.\n\nTo better understand human-GPT disagreement patterns, we conducted both quantitative and qualitative analyses:\n\n1. Correlation with human ratings (Pearson’s r; r = 1 indicates perfect correlation):\n+ GPT-4o-mini: 0.22\n+ GPT-4o: 0.31\n+ GPT-4.1: 0.43\n+ GPT-o3: 0.34\n\n2. Agreement metrics (Cohen’s κ, averaged over κ > 0; κ = 1 indicates perfect agreement):\n+ GPT-to-GPT: 0.54\n+ User-to-user: 0.31\n+ User-to-GPT: 0.27\n\nThese quantitative results indicate that:\n+ Advanced GPT models (especially GPT-4.1 and GPT-o3) show stronger alignment with human raters.\n+ GPT models exhibit higher internal consistency than human raters.\n+ Notably, in 17 cases, user-to-GPT agreement exceeded κ = 0.5, and in two cases, GPT-o3 achieved perfect agreement (κ = 1.0) with a human rater, demonstrating that GPTs can align closely with human preferences in specific contexts.\n\n3. Qualitative analysis further revealed a consistent pattern of divergence:\n+ Humans weighed both interior and exterior quality and particularly penalized incomplete or semantically incoherent or incomplete interiors.\n+ GPTs tended to favor sharper exterior textures, even when interiors were missing or incorrect — a bias consistent with FID-based evaluation discussed in A5.\n\nThis explains why GPTs often underrated our method: GOATex is designed for occluded interiors, whose quality humans appreciate more than GPTs, which emphasize visible exterior realism. We will include representative examples and full results in the revised manuscript to better contextualize these differences.\n\n---\n\nQ8: About the computational cost of the full pipeline.\n\nA8: Thank you for the insightful suggestion. \n\nWe systematically analyzed the runtime of our pipeline across meshes with increasing face counts by progressively subdividing mesh faces of five representative assets. For each mesh, we measured the number of faces and superfaces, time spent on superface construction, hit-level assignment, and MVD-based rendering & synthesis. The table below shows the average across the assets: \n\n| # Faces | # Superfaces | Superface Construction (s) | Hit-Level Assignment* (s) | Rendering & Texturing (s) |\n|---|---|---|---|---|\n| 5k | 196.0 | 0.23 | 259.57 | 117.93 |\n| 10k | 221.2 | 0.46 | 295.38 | 121.80 |\n| 20k | 247.6 | 0.86 | 314.16 | 130.42 |\n| 40k | 433.0 | 1.87 | 394.67 | 146.45 |\n| 80k | 586.0 | 3.99 | 539.68 | 197.29 |\n| 160k | 2033.2 | 18.59 | 942.36 | 549.83 |\n| 320k | 5418.0 | 57.76 | 2563.35 | 2769.22 |\n\n(*Hit-level assignment is reusable when generating multiple variants.)\n\nAs the reviewer correctly pointed out, the number of mesh faces has a significant impact on runtime. In our Objaverse 1.0-based dataset, however, mesh sizes are typically modest (median: 8.5k faces), keeping total runtime practical (under ~10 minutes in most cases).\n\nWe also find that the number of superfaces grows sublinearly with mesh size. While the pipeline is designed to operate at the superface level, most steps currently rely on face-level processing due to the reliance of existing implementations. Moving to superface-level processing throughout is a promising direction for reducing runtime on high-res meshes.\n\nWe will include this analysis and a corresponding runtime plot in the revision.\n\n---\n\nQ9: Request for a deeper discussion of the limitations of the proposed method beyond what's noted in the submitted version.\n\nA9: Thank you for the insightful question.\n\nOne key limitation of GOATex lies in its semantically inconsistent hit-level assignment. Our current decomposition relies solely on geometric visibility computed via multi-view ray casting, which can result in semantically coherent regions being split across different hit levels (as also noted by Reviewer nix7 in Q4). While this does not lead to noticeable seams or texture artifacts in our experiments, it highlights a structural limitation of relying purely on geometry. Incorporating semantic-aware grouping into the hit-level assignment could enable more fine-grained texturing by allowing the pipeline to operate at the level of semantic entities. This would make it possible to assign different prompts to distinct parts, opening the door to richer, more controllable synthesis beyond the current progressive outside-in approach.\n\nWe will include this discussion in the revised manuscript to clarify the limitation and motivate future extensions."}, {"review_id": "FVbvtZvJR7", "reviewer": "Reviewer_8SvF", "summary": "The paper proposes an approach to simultaneously generate the internal and external textures of 3D objects.The paper utilizes an atlas to segment independent faces into superfaces.It divides the superfaces into different levels based on the order of multiple ray hits on the superfaces.The paper proposes residual face cluster to maintain the complete structure for generating reasonable textures and normal flip to make more occluded areas visible.", "questions": "1.The hit level and its relationship with residual face cluster and normal flip are the core of your paper, which should be demonstrated more clearly rather than being placed in the supplement. This makes it difficult to follow.\n\n2.The author has claimed to be the first work to simultaneously generate internal and external textures of objects, and the texture generation part relies on SyncMVD. Therefore, it is sufficient to compare the baseline SyncMVD and present more ablation studies. There is no need to compare with other texture generation models, as they completely collapse. Alternatively, such content can be moved to the supplement.\n\n3.The ablation study uses complex car cases. Simple examples should also be presented to facilitate understanding of how different modules work.\n\n4.Can a superface be assigned to different hit levels?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\nThe paper is the first to propose the work of simultaneously generating the internal and external textures of 3D objects.\n\nWeaknesses：\nThe hit level is the core of the paper, and its changes under different settings should be described and presented in detail, rather than being included in the supplementary material.", "was_revised": true, "final_justification": "It has addressed most of my concerns, and I have decided to raise the score.", "rebuttal": "We thank the reviewer for recognizing the novelty of our work and for the constructive feedback on improving the clarity of our presentation.\n\n---\n\nQ1: The hit level and its relationship with residual face cluster and normal flip are the core of your paper, which should be demonstrated more clearly rather than being placed in the supplement. This makes it difficult to follow.\n\nA1: We appreciate the feedback and will revise the main paper layout to more clearly present this pipeline in the core sections, rather than relying on the supplementary file.\n\n\n---\n\nQ2.The author has claimed to be the first work to simultaneously generate internal and external textures of objects, and the texture generation part relies on SyncMVD. Therefore, it is sufficient to compare the baseline SyncMVD and present more ablation studies. There is no need to compare with other texture generation models, as they completely collapse. Alternatively, such content can be moved to the supplement.\n\nA2: Thank you for the constructive feedback. We agree that a more detailed comparison with SyncMVD, along with thorough ablation studies, is essential to highlight the core contributions of our occlusion-aware framework.\n\nTo address this, we conducted additional experiments during the rebuttal period by constructing a series of ablated models, where components were cumulatively added on top of SyncMVD. This setup allows both step-by-step ablation and direct comparison with SyncMVD.\n\nFor evaluation, we conducted A/B preference tests using SyncMVD as the reference, and each ablated variant as well as our full model as the comparison targets. To improve robustness, we employed multiple GPT-based evaluators (GPT-4o-mini, GPT-4o, GPT-4.1, and GPT-o3), and ran each configuration four times to account for response variability. In the table below, each score represents the win rate (%) of the corresponding method over SyncMVD.\n\n| Method | GPT-4o-mini | GPT-4o | GPT-4.1 | GPT-o3 | Average |\n|---|---|---|---|---|---|\n| + Hit Level Assignment | 82.50 | 66.67 | 75.00 | 77.50 | 75.68 |\n| + Superface Construction | 84.62 | 70.00 | 75.86 | 89.74 | 80.27 |\n| + Soft UV Merging | 79.49 | 82.05 | **90.00** | 95.00 | 86.49 |\n| + Residual Face Clustering | 77.50 | 72.50 | 88.89 | 84.84 | 81.17 |\n| + Normal Flipping & Backface Culling (**Ours**) | **86.84** | **92.31** | 86.67 | **97.50** | **91.16** |\n\nWhile we observe overall performance gains across stages for the advanced GPT models, a slight drop appears when the residual face clustering is applied without normal flipping and backface culling. This is expected, as residual clustering alone cannot fully resolve occlusion. In some cases, two geometrically adjacent faces may be grouped into the same hit level, even if one fully occludes the other from all external views. In such cases, the occluded face cannot be textured during its designated rendering stage, and once that stage completes, it is excluded along with the rest of the level, resulting in missed interior content.\n\nThis limitation is mitigated in the final step by further applying normal flipping and backface culling, which introduces an indirect peeling-off effect. Instead of removing previously textured faces, we flip their normals and apply view-dependent culling—making them invisible from certain camera viewpoints. This increases the chance that previously occluded faces, which share the same hit level, are no longer blocked and can be exposed and textured in subsequent renderings. As a result, the method produces more complete and semantically coherent textures, especially in tightly occluded interior regions.\n\nIt is also worth noting that while our final textured renderings show clear improvements in the complete pipeline, intermediate ablated methods may not always exhibit visually striking differences in the final outputs. This is because early-stage outputs often produce out-of-distribution (OOD) inputs to the depth-conditioned ControlNet, leading to suboptimal or unstable generations. As a result, the benefits of each component are most clearly observed in intermediate representations, such as depth maps and UV maps, that reveal differences in geometric completeness and structural coherence at each hit level. Therefore, to provide a comprehensive and intuitive understanding of each stage, we will also incorporate side-by-side comparisons of depth maps, UV layouts, and textured renderings in the revised ablation analysis.\n\n\n---\n\nQ3.The ablation study uses complex car cases. Simple examples should also be presented to facilitate understanding of how different modules work.\n\nA3: As discussed in A2, we will incorporate a range of examples, from simple to complex, in the revised manuscript to more clearly illustrate the role of each module in our framework. These examples will be supported by step-by-step visualizations using depth maps, UV maps, and textured renderings, making the effect of each component easier to interpret. We believe this will enhance the clarity of the ablation study and help readers better understand how different modules contribute to the final results.\n\n---\n\nQ4: Can a superface be assigned to different hit levels?\n\nA4: No, a superface is strictly assigned to a single hit level.\n\nAssigning a unique hit level to each superface allows our method to progressively reveal and texture occluded geometry in a controlled, layer-by-layer manner. In contrast, assigning multiple hit levels to a single superface would break this peeling-off progression, making it difficult to expose and texture occluded regions properly."}]

Author rebuttal:

Meta-review: This submission received four "Accept" scores from the reviewers. The reviewers appreciated novelty of the contributions, thoroughness of the evaluation setup, as well as clear presentation and additional results. The remaining questions were addressed in the rebuttal. The final recommendation is therefore to accept.
Decision: Accept (poster)