TexTailor: Customized Text-aligned Texturing via Effective Resampling

Dae-shik Kim; Suin Lee

REVIEW 5 major objections 5 minor 39 references

TexTailor: Customized Text-aligned Texturing via Effective Resampling

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read TexTailor claims view-consistent 3D textures come from resampling during denoising, few-shot model fine-tuning, and adaptive camera placement.

desk verdict Plausible method, honest limitations, but the main metric is admitted not to measure view consistency and the tables disagree by 4x; worth reviewing, not worth citing for the numbers. read the letter →

arxiv 2506.10612 v1 pith:UZCGEKND submitted 2025-06-12 cs.CV cs.AI

Suin Lee , Dae-Shik Kim This is my paper · ORCID

classification cs.CVcs.AI

keywords text-to-texturesynthesisviewconsistencydiffusionmodelsDDIMresamplingfine-tuningwithpreservationlossadaptiveviewpointrefinement3Dmeshtexturingcatastrophicforgetting

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

TexTailor is a text-to-texture method for meshes that tries to stop the gradual drift in color and pattern that appears as a texture is painted view by view. Its central claim is that the drift comes from under-integrating previously painted texture during denoising and from an autoregressive synthesis order, and that both can be fixed with three moves: a resampling loop inside DDIM that repeatedly merges known and unknown regions, per-object fine-tuning of a depth-conditioned diffusion model on five resampled images with a preservation loss, and adaptive addition of camera positions when too little previously painted texture is visible. On a subset of Objaverse and on ShapeNet cars, the paper reports lower LPIPS and FID than existing text-driven texture methods, meaning textures that look more alike across views. A sympathetic reader would take the contribution to be a practical recipe for adapting a pretrained 2D diffusion model to a single object's texture while keeping the model's original generation ability.

What carries the argument

The engine of the method is resampling in a DDIM non-Markovian process. At each denoising step, instead of merging the known (already painted) and unknown (to-be-painted) regions once, TexTailor adds noise to the merged latent, denoises it, and merges again, repeating $R$ times; this integrates previously synthesized texture into the new region with only 30 steps per view. On top of that, a depth-conditioned diffusion model (ControlNet) is fine-tuned on five resampled images near the first viewpoint, with a preservation loss $\mathcal{L}_{\mathrm{pre}}$ that penalizes deviation from the frozen pretrained noise predictor to prevent catastrophic forgetting. The third mechanism is an adaptive viewpoint rule: when the ratio $p$ of "keep" pixels to (keep + new) pixels falls below a threshold $\beta$, an interpolated camera position is inserted, so the next view always has enough previously painted texture to condition on.

What would settle it

Render a mesh with strong front/back asymmetry using a deliberately corrupted first-view training image, then compare the texture at the opposite viewpoint to the uncorrupted run: if the opposite view does not visibly drift or degrade, the fine-tuning step is more robust than the paper's stated five-image dependence.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the gradual texture shift in multi-view texturing is not inevitable: it can be suppressed by repeating the known/unknown merge several times per diffusion timestep inside a non-Markovian sampler, then teaching the depth-aware model the object's appearance from a handful of resampled views. The fine-tuning step is the load-bearing addition because resampling alone still lets properties drift at viewpoints far from the first camera. To keep that fine-tuning from destroying the pretrained model's fidelity, the paper adds a preservation loss that pulls the fine-tuned noise prediction toward the original pretrained noise prediction. The final claim is that the measured result, lower average LPIPS and FID than the listed baselines on an Objaverse subset plus qualitative gains on ShapeNet cars, supports the whole combination.

Load-bearing premise

The load-bearing premise is that five images rendered near the first viewpoint teach the model the object's appearance at every other angle, even though the paper's limitation section concedes that texture quality "heavily relies" on those five images and that bad training images can degrade output.

Editorial extensions

If this is right

With $R$-fold resampling, each view needs roughly 30 denoising steps instead of the 250 steps of the underlying inpainting sampler, so the consistency gain does not come from brute-force compute per view.
Fine-tuning on five self-generated resampled images removes the need for an external dataset of 3D meshes, textures, or text descriptions; the object's own first views supply the training data.
Because the preservation loss keeps the fine-tuned model close to the pretrained predictor, the method avoids the catastrophic forgetting that naive few-image fine-tuning produces, as shown in the paper's ablation study.
Adaptive camera insertion means texture synthesis no longer requires manually tuned viewpoint sets; the method places an intermediate view whenever the visible painted fraction is too small.
On the reported Objaverse subset, the full method lowers LPIPS from 38.89 to 37.89 and FID from 30.924 to 29.998 compared to the no-training baseline, and beats the listed baselines on both metrics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Not claimed by the paper: the same preservation-loss term could serve as a general regularization for any few-shot fine-tuning of conditioned diffusion models, protecting pretrained fidelity whenever the training set is tiny.
Not claimed by the paper: the dependence on five training images implies a concrete stress test, namely that corrupting or occluding one first-view image should worsen opposite-view consistency, and the paper's limitation section predicts exactly this failure mode.
Not claimed by the paper: the resample-and-merge loop could transfer to other autoregressive multi-view generation tasks, such as video inpainting where the "known" region is a previous frame rather than a painted mesh surface.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things you should know. The method is a sensible combination of known tools, and the qualitative story is believable. But the numbers in the main table cannot carry the claim: the paper's own limitation section says LPIPS doesn't measure view consistency, and the same metric appears to produce values around 38–54 in Table 1 and around 9 in Table 3 with no explanation. That's a 4x gap on the headline metric, and FID moves the other way. The 'outperforms SOTA' sentence is not supported by the evidence as written.

What's genuinely new: applying RePaint-style resampling inside DDIM for texturing, per-object ControlNet fine-tuning with a preservation loss to fight forgetting, and adaptive camera placement based on keep-region ratio. The ablations show each component helps, and the qualitative figures, especially the muffin and basket, make a plausible case that the method reduces gradual drift. The authors also earn credit for an honest limitations section; they flag the five-image fragility and the LPIPS issue themselves.

Soft spots, in order. First, the metric problem is real and load-bearing. If the main metric is admitted not to measure the claimed property, Table 1 cannot establish the result. The Table 1 vs Table 3 discrepancy also suggests some unstated protocol change, which undermines reproducibility. Second, Eq. 7 uses z-tilde-r-0 without defining it. You can guess it is the predicted clean latent from a previous resampling step, but guessing is not what a reader should have to do for the central equation. Third, fine-tuning on five self-generated images is a genuine robustness risk; the paper acknowledges it can degrade quality. For some objects it clearly works, but on a harder mesh it could be a coin flip. Fourth, the comparison is not controlled: per-object fine-tuned model against frozen baselines, no error bars, and only one prompt template.

Who is this for? People working on text-to-texture who want a concrete recipe to reduce drift. The recipe is worth trying, and the adaptive viewpoint idea could be reused independently. But the paper should not be cited for the quantitative superiority claim until the metric issue is resolved.

If this crossed my desk, I would send it to reviewers. The core approach is sound enough to warrant a serious look, and the fixes are specific: report a geometry-aware consistency metric, reconcile the tables, and define the resampling notation.

Referee Report

5 major / 5 minor

Summary. The paper presents TexTailor, a method for text-to-texture synthesis on 3D meshes. It proposes three contributions: (1) a resampling scheme applied within the DDIM non-Markovian process to repeatedly integrate previously synthesized textures during inpainting-based texturing; (2) per-object fine-tuning of a ControlNet depth-aware diffusion model on a small set of resampled images, regularized by a 'performance preservation loss' to mitigate catastrophic forgetting; and (3) adaptive viewpoint refinement that inserts intermediate camera positions based on the ratio of kept to new texture regions. The method is evaluated on a subset of Objaverse and qualitatively on ShapeNet cars, reporting lower LPIPS and FID than prior methods, with ablations showing that each component contributes to the final result.

Significance. If the quantitative claims are substantiated, TexTailor offers a practical and interesting recipe for improving view consistency in text-to-texture generation without requiring external 3D datasets: the model is fine-tuned on its own resampled outputs while a distillation-style loss keeps it close to the pretrained model. The promise of released source code, the per-component ablations, and the adaptive viewpoint mechanism are clear strengths. However, the current evidence is weakened by an underspecified core equation, internally inconsistent quantitative tables, and a metric that the authors themselves acknowledge does not measure the claimed property. With a valid consistency metric and a cleaned-up presentation, the contribution could be solid and useful to the community.

major comments (5)

[Sec. 3.1, Eq. (7)] Eq. (7) uses the quantity \tilde z^r_0, which is never defined. The preceding text defines \tilde z^0_{t-1} (as the result of Eq. 5) and \tilde z^r_t (via Eq. 6), but the DDIM denoising formula requires an estimate of the clean latent, presumably \tilde z^r_0 = (\tilde z^r_t - \sqrt{1-\bar\alpha_t}\,\epsilon_\phi(\tilde z^r_t, t, c))/\sqrt{\bar\alpha_t}. Without this definition, the resampling recursion is not reproducible. Please define \tilde z^r_0 explicitly and verify the subscripts throughout Eqs. (6)-(8).
[Sec. 4.1 and A.6, Tables 1 and 3] The quantitative results are internally inconsistent: Table 1 reports LPIPS values around 38-54 and FID around 30-45, while Table 3, using the same metric names, reports LPIPS around 8.99 and FID around 57-59 for the same method. No difference in protocol (e.g., different mesh subset, render resolution, camera sampling, or LPIPS implementation) is described to reconcile these numbers. As written, the discrepancy undermines the credibility of the comparisons in Table 1. Please clarify the protocols and, if the numbers come from different settings, explain precisely why the scales differ by a factor of roughly four.
[Sec. 4.1, Table 1 and A.4] The central claim is that TexTailor improves view consistency, but the only quantitative metric used to support this claim, LPIPS between rendered views, is explicitly stated in A.4 to 'not adequately capture consistency across multiple viewpoints due to spatial misalignments in overlapping sections between adjacent views.' Thus Table 1 does not measure the property it is used to assert. Please add a directly consistency-aware metric, such as warped-view agreement with depth, texture-map alignment error, or a user study, and report it for TexTailor and all baselines. Without such a metric, the headline outperformance claim is unverified.
[Sec. 4.1, evaluation metric aggregation] The description of the LPIPS aggregation says: 'compute the average LPIPS values for all pairs of images in the 3D scene and sum the averages across all evaluated categories.' Summing rather than averaging over categories makes the score dependent on the number of categories and objects, and it is unclear whether the numbers in Tables 1 and 2 are sums or means. This also prevents comparison across datasets of different sizes. Please clarify the aggregation and, ideally, report the mean and standard error over objects.
[Sec. 4.1, Sec. 4.2, and A.4] The method's success depends heavily on the five fine-tuning images, and A.4 concedes that 'suboptimal training images can sometimes degrade texture quality rather than enhance it.' However, no quantitative sensitivity analysis is provided: there is no report of variance across different random seeds, different choices of the five viewpoints, or different objects where the training images are poor. Given that this fine-tuning step is a core component of the pipeline, please add such an analysis or explicitly characterize the conditions under which the method is expected to fail.

minor comments (5)

[Sec. 2.2, Eq. (3)] In Eq. (3), z^{known}_{t-1} is defined using \bar\alpha_t, but the standard forward-process posterior for timestep t-1 should use \bar\alpha_{t-1}; please check the subscript.
[Sec. 1, first paragraph] The sentence 'geometry optimization process (Shen et al., 2021; ?; Nash et al., 2020)' contains a stray '?' placeholder; please remove or replace it with the intended citation.
[Throughout] The phrase 'an performance preservation loss' appears in the abstract, Sec. 1, Sec. 3.2, and elsewhere; it should be 'a performance preservation loss.' Similarly, 'we finetunes' in Sec. 1 should be 'we fine-tune.'
[Appendix A.6] The text says 'randomly select 100 meshes from the Objaverse dataset, ensuring no overlapping categories among the 400 meshes,' but Sec. 4.1 states the Objaverse subset contains 410 meshes. Please correct this number or explain the discrepancy.
[Appendix A.5] The sentence 'visual differences are harder to discern in the qualitatve comparison sections (Sec. 5 and Sec. 8)' contains a typo ('qualitatve') and references sections numbered 5 and 8, which do not exist in the paper's numbering; please correct the cross-references.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reduction found: the self-referential fine-tuning loop and metric-based beta selection are not definitional circularity.

full rationale

The derivation chain is self-contained against external benchmarks. The resampling step (Eqs. 6-8) is an explicit adaptation of RePaint's resampling to the DDIM sampler, building on external cited work; fine-tuning (Eqs. 9-11) trains ControlNet on five self-generated resampled images with an added preservation regularizer, and adaptive viewpoint refinement (Eq. 12) is a geometric heuristic. None of these steps defines the reported outcome (LPIPS/FID or view-consistent textures) in terms of its own inputs. The per-object fine-tuning is self-referential in that its training images come from the same model's resampling, but the evaluation uses external artist-designed textures as the real distribution for FID and renders novel viewpoints for LPIPS, so the measured result is not equivalent to the training loss by construction. Appendix A.4's admission that 'LPIPS does not adequately capture consistency across multiple viewpoints' and the unexplained Table 1 vs Table 3 numeric gap (LPIPS 37.889 vs 8.99176; FID 29.998 vs 57.799) are correctness and reproducibility concerns, not circular steps. The choice of beta=0.5 from Table 3 is hyperparameter selection on the evaluation metric, not a fitted parameter renamed as a prediction; it does not force the Table 1 comparison by construction. There are no load-bearing self-citations or author-imported uniqueness theorems in the manuscript.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The method introduces no new physical entities or latent factors. Its free parameters are hand-chosen hyperparameters (R, lambda, beta, gamma, fine-tuning image count, sampling steps). The central assumptions are the validity of DDIM resampling, the sufficiency of five self-generated images for per-object fine-tuning, and the usefulness of the keep-region ratio as a viewpoint-quality criterion.

free parameters (6)

resampling steps R = 3
Used in Sec 3.1 and fixed for all experiments; no ablation of R is shown.
performance preservation loss weight lambda = 2.5
Set in Sec 4.1; no ablation reported.
keep-region ratio threshold beta = 0.5
Ablated in Appendix A.6 on a 100-mesh subset; value chosen as balanced, then used everywhere.
viewpoint interpolation factor gamma = 0.5
Set in Sec 4.1; no ablation reported.
fine-tuning image count and viewpoints v1..v5 = 5 images; v1=(0,15,1), v2=(0,35,1), v3=(0,-5,1), v4=(20,15,1), v5=(340,15,1)
Chosen by hand in Sec 4.1; the paper's limitation A.4 says final texture quality depends on these.
DDIM sampling steps = 30
Fixed in Sec 3.1; claimed to be far fewer than RePaint's 250, but no study of step count is given.

assumptions (6)

standard math DDIM's non-Markovian sampling equations are correct and remain valid at 30 steps.
Sec 3.1 Eqs. 6-8 rely on DDIM's deterministic reverse step; if misapplied, the resampling scheme is invalid.
domain assumption ControlNet fine-tuned on five images generalizes to unseen viewpoints of the same object.
Sec 3.2 Eq. 9 assumes five resampled images near the first viewpoint teach the model the object's texture for all other angles; A.4 admits this can fail.
domain assumption The ratio p = keep/(keep+new) is a reliable indicator of when an intermediate viewpoint is needed.
Sec 3.3 Eq. 12 uses this ratio to trigger viewpoint interpolation; no independent validation of this criterion is given.
domain assumption Self-generated resampled images are a valid training signal even if they contain artifacts.
Fine-tuning uses the model's own outputs; the paper's A.4 says suboptimal training images can degrade quality rather than improve it.
standard math Latent inpainting composition (Eq. 5) correctly combines known and unknown regions in latent space.
Inherited from RePaint (Lugmayr et al. 2022) and used without modification in Eq. 8.
domain assumption The evaluation images and ground-truth textures are aligned for FID and LPIPS computation.
Sec 4.1 renders both synthesized and original meshes from the same camera set; if mesh alignment differs, the metrics are biased.

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Cite this review

Pith. "Pith review of TexTailor: Customized Text-aligned Texturing via Effective Resampling." pith.science (2026). https://pith.science/paper/UZCGEKND

@misc{pith2026250610612,
  author       = {Pith},
  title        = {Pith review of: TexTailor: Customized Text-aligned Texturing via Effective Resampling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UZCGEKND}},
  note         = {Machine review of arXiv:2506.10612}
}

read the original abstract

We present TexTailor, a novel method for generating consistent object textures from textual descriptions. Existing text-to-texture synthesis approaches utilize depth-aware diffusion models to progressively generate images and synthesize textures across predefined multiple viewpoints. However, these approaches lead to a gradual shift in texture properties across viewpoints due to (1) insufficient integration of previously synthesized textures at each viewpoint during the diffusion process and (2) the autoregressive nature of the texture synthesis process. Moreover, the predefined selection of camera positions, which does not account for the object's geometry, limits the effective use of texture information synthesized from different viewpoints, ultimately degrading overall texture consistency. In TexTailor, we address these issues by (1) applying a resampling scheme that repeatedly integrates information from previously synthesized textures within the diffusion process, and (2) fine-tuning a depth-aware diffusion model on these resampled textures. During this process, we observed that using only a few training images restricts the model's original ability to generate high-fidelity images aligned with the conditioning, and therefore propose an performance preservation loss to mitigate this issue. Additionally, we improve the synthesis of view-consistent textures by adaptively adjusting camera positions based on the object's geometry. Experiments on a subset of the Objaverse dataset and the ShapeNet car dataset demonstrate that TexTailor outperforms state-of-the-art methods in synthesizing view-consistent textures. The source code for TexTailor is available at https://github.com/Adios42/Textailor

Figures

Figures reproduced from arXiv: 2506.10612 by the authors.

**Figure 1.** (a): The illustration of definition of viewpoint. Following the red arrow, the pencil case mesh is painted from left to right in each row in (b). (b): This visualization illustrates the gradual shift in texture properties that becomes more pronounced as the viewpoint changes. Compared to Text2Tex (Chen et al., 2023a) and TEXTure (Richardson et al., 2023), TexTailor exhibits significantly less of this gradual shift.… view at source ↗

**Figure 2.** Left: The image x0 rendered from the current viewpoint before being processed by the depth-aware diffusion model. Right: An illustration of the four regions on the partial mesh surface. Texture synthesis procedure. In recent works (Richardson et al., 2023; Chen et al., 2023a; Tang et al., 2024), the partial mesh surface viewed from a single viewpoint are segmented into several regions and undergo incremental text… view at source ↗

**Figure 3.** Overview of TexTailor. TexTailor synthesizes textures for a given 3D mesh without textures, based on a textual description, such as “a hippopotamus”. We add additional camera positions to the predefined set to properly condition the previously synthesized textures from each viewpoint, eliminating the need for manually coordinating camera locations. Based on these viewpoints, we progressively generate textures using… view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Illustration of the loss of original generative ability when fine-tuning ControlNet without the performance preservation loss. Using the resampling method (Sec. 3.1) within the depth-aware diffusion model, the partial mesh surface from the current viewpoint are coher…

**Figure 5.** Figure 5: Qualitative comparisons on Objaverse. We compare TexTailor with state-of-the-art baselines (Metzer et al., 2023; Chen et al., 2023a; Richardson et al., 2023; Youwang et al., 2024) on Objaverse meshes. Compared to other methods, TexTailor produces textures that are more…

**Figure 6.** Figure 6: Qualitative comparisons on ShapeNet car. Our approach synthesizes more viewconsistent and higher-quality textures for fine-grained categories compared to the baseline. w/ Resampling w/ Training w/Perf.Loss w/ View Refine LPIPS ↓ FID ↓ ✓ x x x 38.89 30.924 ✓ ✓ x x 39.8…

**Figure 7.** Figure 7: Ablation studies. The illustration demonstrates the effectiveness of the key components of TexTailor. As each component is applied, the muffin’s texture becomes more consistent across multiple viewpoints, and the overall texture quality improves. point between these vi…

**Figure 8.** Figure 8: Additional qualitative comparisons on Objaverse. We compare TexTailor with stateof-the-art baselines (Metzer et al., 2023; Chen et al., 2023a; Richardson et al., 2023; Youwang et al., 2024) on Objaverse meshes. Compared to other methods, TexTailor produces textures th…

**Figure 9.** Figure 9: Additional qualitative comparisons on RenderPeople (ren, 2023). Qualitative comparison of generated textures for clothed human meshes using various text prompts. Each column corresponds to a different text prompt: (1st col) ‘A woman wearing a white blouse with a ribbo…

**Figure 10.** Figure 10: Additional qualitative comparisons on non-diffuse objects from the Objaverse dataset. We present the texture synthesis results categorized into three types: (a) Glossy objects, (b) Metallic objects, and (c) Translucent objects. We further demonstrate the texture quali…

**Figure 11.** Figure 11: Ablation studies. The illustration highlights the effectiveness of the key components of TexTailor. As each component is applied, the textures of various objects become increasingly consistent across multiple viewpoints, demonstrating improved texture quality regardl…

**Figure 12.** Figure 12: Zoomed-in qualitative comparisons between TexTailor and the baseline Text2Tex The red boxes highlight regions where visual differences in texture consistency and quality are more apparent, helping to illustrate the effectiveness of TexTailor in preserving texture prop…

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write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...
[38]

@esa (Ref

\@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...
[39]

\@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...
[40]

region, while the part that lacks texture is called the ``new

@open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

2021 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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URL https://renderpeople.com/

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work page 2022