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arxiv: 2604.08716 · v1 · submitted 2026-04-09 · 💻 cs.CV

Recognition: no theorem link

What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction

Loc-Phat Truong, Meysam Madadi, Sergio Escalera

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:44 UTC · model grok-4.3

classification 💻 cs.CV
keywords virtual try-offgarment reconstructiondiffusion modelsdual-unetvirtual try-onfashion image generationlatent diffusion
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The pith

A Dual-UNet diffusion model reconstructs flat canonical garments from draped try-on images more accurately than prior methods by testing specific backbone, conditioning, and loss choices.

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

Virtual try-off reverses virtual try-on by recovering the original flat garment image from a photo of someone wearing it. The authors adapt diffusion models to this task and center their study on a Dual-UNet architecture, systematically comparing Stable Diffusion backbones, mask-based and semantic conditioning strategies, and combinations of attention-based perceptual losses with multi-stage training. On the VITON-HD and DressCode datasets their best setup lowers the primary DISTS error by 9.5 percent while remaining competitive on LPIPS, FID, KID, and SSIM, showing that these design decisions directly affect how well the model recovers garment shape and texture from folds and occlusions.

Core claim

The Dual-UNet Diffusion Model, when equipped with an effective Stable Diffusion backbone, carefully chosen mask and semantic conditioning, and auxiliary attention-based perceptual losses trained in curriculum stages, achieves state-of-the-art virtual try-off performance by reducing DISTS by 9.5 percent on VITON-HD and DressCode while delivering competitive scores on LPIPS, FID, KID, and SSIM.

What carries the argument

Dual-UNet Diffusion Model that processes masked and unmasked image inputs through parallel U-Net paths while adding high-level semantic features and attention-based losses to guide accurate recovery of garment details.

If this is right

  • Mask design and whether inputs are masked or unmasked during conditioning directly influence boundary accuracy and texture fidelity in the output garment.
  • Inclusion of high-level semantic features improves the model's ability to capture overall garment style and structure beyond low-level pixels.
  • Auxiliary attention-based losses combined with perceptual objectives and curriculum schedules reduce artifacts in reconstructed folds and seams.
  • The resulting configurations supply stronger baselines that future virtual try-off work can build upon or extend to related inverse clothing tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The conditioning ablations may transfer to other clothing image translation problems such as garment editing or virtual fitting preview generation.
  • If the model proves robust outside benchmarks, retailers could automatically extract clean product images from model photos without additional photography.
  • Combining the reconstruction output with 3D garment simulation pipelines could produce more accurate virtual try-on experiences from single 2D images.

Load-bearing premise

The performance gains measured on controlled benchmark datasets will continue to hold for real-world garment images that differ in lighting, pose, and fabric appearance.

What would settle it

Testing the trained model on a new set of real-world photographs of people wearing garments under varied natural lighting and poses, then comparing reconstructed flat images against ground-truth canonical garment photos using the same DISTS metric.

Figures

Figures reproduced from arXiv: 2604.08716 by Loc-Phat Truong, Meysam Madadi, Sergio Escalera.

Figure 1
Figure 1. Figure 1: State-of-the-art single-UNet, TryOffDiff [30] and Try-Off-Anyone [34], vs. our adapted Dual-UNet try-off results. Our Dual-UNet architecture achieves high-quality and realistic garment generation while precisely preserving fine-grained details across diverse datasets. Abstract. Virtual Try-On (VTON) has seen rapid advancements, pro￾viding a strong foundation for generative fashion tasks. However, the inver… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the Dual-UNet Diffusion Model framework for VTOFF [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of Leffa loss impact on guiding attentions toward correct regions and implicitly prevent fine-grained details distortion. (a) with Leffa, (b) without Leffa [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison on VITON-HD dataset between our approach and pre￾vious works. Zoom in for better inspection. More examples are given in Supp. Sec. 6 . Overall, our method achieves the best performance on DISTS, FID, and KID across benchmarks, while SSIM and LPIPS show mixed results. This discrepancy stems from the inherent bias of SSIM and LPIPS toward global structure over local texture; as demonst… view at source ↗
Figure 5
Figure 5. Figure 5: Failure case in VTOFF due to wrong and flat attention similar to VTON [42]. Left: Reference image used in ConditionNet; Center: VTOFF result, which fails to present the olive green stripe; Right: The corresponding attention map of the red region in the result with respect to the reference image. In this section, we provide more details about the dataset, training efficiency and inference hyperparameter: VI… view at source ↗
Figure 6
Figure 6. Figure 6: Disentangling transformations applied to the ground truth images of the VITON-HD test set, showing in each pair of images: the left image is ground truth and the right one is the modified image. From top to bottom: color modifications (background, cloth), cloth shape distortions (rotation & scaling, TPS warping), and mild/drastic texture alterations (random copy-paste, blurring). The reflection on VTOFF me… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of architectural ablations for the proposed Dual-UNet Diffusion Framework on VTOFF. (1) the person wearing the garment, (2) ground￾truth canonical garment, (3) the result of the base architecture described in Sec 4.1.3 (main paper), and (4–8) samples of different variants illustrating for Sec 4.2&4.3 in the main paper: (4)-Inpainting CreationNet in Sec 4.2.1, (5)-SD v1.5 in Sec 4.2.2… view at source ↗
Figure 8
Figure 8. Figure 8: Color fading effect caused by Color Jitter augmentation in later stages of train￾ing [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Quantitative comparison of random vs. fixed prompt training across differ￾ent prompt complexities (ascending from level 0 to 3). Random prompt augmentation yields more consistent performance across prompt levels. Especially, it maintains com￾petitive performance on the shortest prompt - level 0, indicating slightly improved ro￾bustness. However, its best performance remains lower than the case where the mo… view at source ↗
Figure 10
Figure 10. Figure 10: More qualitative comparison with previous works on at 512 × 384 resolution. Qualitative comparison on Unsplash Real-World dataset between our approach and previous works, which are trained on VITON-HD dataset [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: More qualitative comparison with previous works on VITON-HD at 512×384 resolution [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: More qualitative examples on VITON-HD at 1024 × 768 resolution produced by our method [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: More qualitative examples on DressCode at 512 × 384 resolution produced by our method [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Illustration of Failure Mode. Comparison showing the challenge of maintaining accurate generation for small garment types (top row), and intricate and small text (bottom row). G Failure Mode Discussion Despite achieving state-of-the-art performance, we observe two recurring failure modes that suggest directions for future work. First, the model may occasionally misestimate geometry for small garment types… view at source ↗
read the original abstract

Virtual Try-On (VTON) has seen rapid advancements, providing a strong foundation for generative fashion tasks. However, the inverse problem, Virtual Try-Off (VTOFF)-aimed at reconstructing the canonical garment from a draped-on image-remains a less understood domain, distinct from the heavily researched field of VTON. In this work, we seek to establish a robust architectural foundation for VTOFF by studying and adapting various diffusion-based strategies from VTON and general Latent Diffusion Models (LDMs). We focus our investigation on the Dual-UNet Diffusion Model architecture and analyze three axes of design: (i) Generation Backbone: comparing Stable Diffusion variants; (ii) Conditioning: ablating different mask designs, masked/unmasked inputs for image conditioning, and the utility of high-level semantic features; and (iii) Losses and Training Strategies: evaluating the impact of the auxiliary attention-based loss, perceptual objectives and multi-stage curriculum schedules. Extensive experiments reveal trade-offs across various configuration options. Evaluated on VITON-HD and DressCode datasets, our framework achieves state-of-the-art performance with a drop of 9.5\% on the primary metric DISTS and competitive performance on LPIPS, FID, KID, and SSIM, providing both stronger baselines and insights to guide future Virtual Try-Off research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces a Dual-UNet diffusion model for Virtual Try-Off (VTOFF), the inverse task of reconstructing canonical garment images from draped-on person images. It systematically ablates three axes—generation backbone (Stable Diffusion variants), conditioning (mask designs, masked/unmasked inputs, high-level semantic features), and losses/training (auxiliary attention loss, perceptual objectives, multi-stage curriculum)—and reports state-of-the-art results on the VITON-HD and DressCode datasets, including a 9.5% drop in the primary DISTS metric along with competitive LPIPS, FID, KID, and SSIM scores.

Significance. If the central empirical claims hold under matched evaluation protocols, the work supplies both stronger baselines and actionable design insights for the under-explored VTOFF domain. The controlled ablations across backbone, conditioning, and loss axes on two standard datasets constitute a solid empirical contribution that can guide subsequent diffusion-based garment reconstruction research.

major comments (2)
  1. [§4 and Table 1] §4 (Experimental Evaluation) and Table 1 (main quantitative results): the SOTA claim of a 9.5% DISTS reduction is load-bearing for the paper's contribution, yet the manuscript does not explicitly confirm that the exact test splits, pose/lighting distributions, mask generation procedure, and metric implementation match those used by the cited VTOFF baselines. Small protocol differences can produce metric shifts of this magnitude in perceptual reconstruction tasks.
  2. [§4.3] §4.3 (Ablation studies): all reported metric improvements are given as single-point estimates without error bars, standard deviations across random seeds, or statistical significance tests. Because diffusion sampling is stochastic, the absence of these statistics weakens the reliability of the ablation conclusions on conditioning strength and auxiliary loss weights.
minor comments (2)
  1. [Abstract] Abstract: the parenthetical '(VTOFF)-aimed' is missing a space after the closing parenthesis.
  2. [§4] The manuscript would benefit from an explicit statement in the experimental section confirming that all baseline numbers were either re-implemented with the authors' preprocessing pipeline or taken from the original papers under identical conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation and the recommendation for minor revision. We address the two major comments point by point below, providing honest clarifications and indicating the changes we will incorporate.

read point-by-point responses

  1. Referee: [§4 and Table 1] §4 (Experimental Evaluation) and Table 1 (main quantitative results): the SOTA claim of a 9.5% DISTS reduction is load-bearing for the paper's contribution, yet the manuscript does not explicitly confirm that the exact test splits, pose/lighting distributions, mask generation procedure, and metric implementation match those used by the cited VTOFF baselines. Small protocol differences can produce metric shifts of this magnitude in perceptual reconstruction tasks.

    Authors: We appreciate this concern regarding protocol consistency. Our experiments strictly follow the official test splits, preprocessing pipelines, mask generation procedures, and metric implementations from the VITON-HD and DressCode papers as well as the released code of the cited baselines. To make this explicit and eliminate any ambiguity, we will revise §4.1 to include a dedicated paragraph on evaluation protocols and add a clarifying statement to the caption of Table 1 confirming matched protocols. This will directly support the validity of the reported 9.5% DISTS improvement. revision: yes

  2. Referee: [§4.3] §4.3 (Ablation studies): all reported metric improvements are given as single-point estimates without error bars, standard deviations across random seeds, or statistical significance tests. Because diffusion sampling is stochastic, the absence of these statistics weakens the reliability of the ablation conclusions on conditioning strength and auxiliary loss weights.

    Authors: We agree that the stochastic nature of diffusion sampling makes single-point estimates less reliable and that error bars or multi-seed statistics would strengthen the ablation analysis. However, the computational cost of running multiple random seeds for every ablation configuration is substantial. In the revised manuscript we will add an explicit discussion in §4.3 acknowledging this limitation, noting that the observed trends remained consistent in our internal multi-seed spot-checks on key configurations, and we will report standard deviations for the main results in Table 1 where space allows. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical ablation on external benchmarks

full rationale

The paper is an empirical study that proposes a Dual-UNet diffusion architecture for virtual try-off, performs ablations across backbones, conditioning masks, and loss terms, and reports quantitative results on the standard VITON-HD and DressCode test sets. No derivation, uniqueness theorem, or predictive equation is presented whose output is forced by construction from its own fitted inputs or self-citations. All performance numbers (DISTS, LPIPS, etc.) are obtained by running the trained model on held-out external data and comparing against independently published baselines, satisfying the criterion of an independent, falsifiable evaluation.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard diffusion-model assumptions and empirical validation rather than new theoretical constructs.

free parameters (2)
  • Mask design variants and conditioning strength
    Chosen and ablated during experiments; values not fixed a priori.
  • Auxiliary loss weights and curriculum schedule stages
    Tuned as part of the multi-stage training strategy.
axioms (1)
  • domain assumption Diffusion models trained on VTON data can be directly repurposed for the inverse VTOFF task with architectural modifications.
    Invoked when adapting Stable Diffusion variants and conditioning mechanisms from try-on literature.

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