Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals
Authors: Davide Lobba
Fulvio Sanguigni
Bin Ren
Marcella Cornia
Rita Cucchiara
Nicu Sebe
Venue: ICLR 2026 Poster (2026)
Keywords: Virtual Try-Off
Fashion
Generation
Diffusion Transformer
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a new virtual try-off method for multi-category garments

Abstract: Virtual try-on (VTON) has been widely explored for rendering garments onto person images, while its inverse task, virtual try-off (VTOFF), remains largely overlooked. VTOFF aims to recover standardized product images of garments directly from photos of clothed individuals. This capability is of great practical importance for e-commerce platforms, large-scale dataset curation, and the training of foundation models. Unlike VTON, which must handle diverse poses and styles, VTOFF naturally benefits from a consistent output format in the form of flat garment images. However, existing methods face two major limitations: (i) exclusive reliance on visual cues from a single photo often leads to ambiguity, and (ii) generated images usually suffer from loss of fine details, limiting their real-world applicability.
To address these challenges, we introduce TEMU-VTOFF, a Text-Enhanced MUlti-category framework for VTOFF. Our architecture is built on a dual DiT-based backbone equipped with a multimodal attention mechanism that jointly exploits image, text, and mask information to resolve visual ambiguities and enable robust feature learning across garment categories. To explicitly mitigate detail degradation, we further design an alignment module that refines garment structures and textures, ensuring high-quality outputs. Extensive experiments on VITON-HD and Dress Code show that TEMU-VTOFF achieves new state-of-the-art performance, substantially improving both visual realism and consistency with target garments. Code and models are available at: https://temu-vtoff-page.github.io/.

Published as a conference paper at ICLR 2026
INVERSE VIRTUAL TRY-ON: GENERATING MULTI-CATEGORY PRODUCT-STYLE IMAGES FROM CLOTHED INDIVIDUALS
"Davide Lobba'., "Fulvio Sanguigni 'Bin Ren Marcella Cornia', Rita Cucchiara', Nicu Sebe
University of Trento University of Modena and Reggio Emilia Uniivrrity of Pisa
Visual results produced by proposed text-enhanced tecture, i... TEMU-VTOFF. Given a clothed input person image, the proposed model reconstructs the clean, in-shop version of the worn garment. Our model handles various garment types and preserves both structural fidelity and fine-grained textures, even under occlusions and complex poses, thanks to its multimodal attention and garment-alignment design.
Figure 1:
virtual try-off archi-
ABSTRACT
Virtual try-on (VTON) has been widely explored for rendering garments onto person images, while its inverse task, virtual try-off (VTOFF), remains largely over- looked. VTOFF aims recover standardized product images of garments directly from photos of clothed individuals. This capability is of great practical importance for e-commerce platforms, large-scale dataset curation, and the training of founda- tion models. Unlike VTON, which must handle diverse poses and styles, VTOFF naturally benefits from a consistent output format the form of flat garment images. However, existing methods face major limitations: (i) exclusive reliance visual cues from single photo often leads to ambiguity, and (ii) generated images usually suffer from loss of fine details, limiting their real- world applicability. To address these challenges, we introduce TEMU-YTOFF, a Text-Enhancal MUlti- category framework for VTOFF. Our architecture is built a dual DiT-based backbone equipped with multimodal attention mechanism that jointly exploits image, text, and mask information to resolve visual ambiguities and enable robust feature learning across garment categories. To explicitly mitigate detail degradation, we further design an alignment module that refines garment structures and textures, ensuring high-quality outputs. Extensive experiments Code show that TEMU-VTOFF achieves new state-of-ther thettrtt performance, substan- tially improving both visual realism and consistency with target garments. Code and models are available at: Intpps: petps://temann /tt////euuue///////e/e commu-vton vtoff page. gi github.io!
VITON-HD and Dress
INTRODUCTION
Unlike virtual try-on (VTON), whose goal is to dress given clothing image on target person image, in this paper, we focus exactly on the opposite, virtual try-off (VTOFF), whose purpose is
indicates equal contribution, indicates corresponding author.

---

Published as a conference paper at ICLR 2026
to generate standardized product images from real-world clothed individual photos. Compared to VTON, which often struggles with the ambiguity and diversity of valid outputs, such as stylistic variations in how a garment is worn, VTOFF benefits from clearer output objective: reconstructing a consistent, lay-down-style image of the garment. This reversed formulation facilitates a more objective evaluation of garment reconstruction quality.
The fashion industry, a trillion-dollar global market, is increasingly integrating AI and computer vision optimize product workflows and enhance user experience. VTOFF, in this context, offers substantial value: it enables the automatic generation of tiled product views, which are essential for tasks such as image retrieval, outfit recommendation, and virtual shopping. However, acquiring such lay-down images is expensive and time- consuming for retailers. VTOFF provides a scalable alternative by leveraging images of garments worn by models or customers, transforming them into standardized catalog views through image-to-image translation techniques.
Despite the success of GANs (Goodfellow et al. (0044) and Latent Diffusion Models (LDMs) (Rom- bach et al. 2022) in image translation tasks (Siarohin al. 2019 Ren et al. [023b Isola et al. 2017 Tumanyan an et al. 2023), current VTOFF solutions face notable limitations. Existing models (Velioglu et aal.[2024 Xarchakos & Koukopouloy los 2024) struggle to accurately reconstruct catalog images from dressed human imputs. This limitation arises from fundamental architectural mismatch: these approaches repurpose VTON pipelines by merely reversing the input-output roles, without addressing the unique challenges of the VTOFF task. Moreover, the high visual variability of real-works images category (e-g., upper-body), pose changes, and occlusions makes it difficult for these models to robustly extract garment features while preserving fine-grained patterns. On the opposite side, we design dedicated architecture tailored for the VTOFF task.
due to garment
Recent advances in diffusion models demonstrate that DiT-based architectures (Peebles & Xie 2023), especially when combined with now-matching objectives (Lipman al., 2023), surpass traditional U-Net and DDPM- based approaches (Rombach et al., (0022). Inspired by these findings, we propose TEMU-VTOFF, on a dual-DiT framework. Specifically, we exploit the representational strength of DiT in two distinct ways: (i) the first Transformer component focuses on extracting fine mme-grained garment features from complex, detail-rich person images; and (ii) the second DiT is specialized for generating the clean, in-shop version of the garment. To support this design, we further adapt the base DiT architecture to accommodate the task- sask-specific input modalities. To further enhance alignment, introduce an external garment aligner module and a novel supervision loss that leverages clean garment references guidance, further improving quality of generated images.
Text-Enhancee MUlti-category Virtual Try-OFF architecture based
Our contribution can be summarized as follows:
Multi-Category Try-Off. We present unified framework capable of handling multiple garment types (upper-body, lower-body, and full-body clothes) without requiring category-specifi pipelines.
Multimodal Hybrid Attention. We introduce novel attention mechanism that integrates garment textual descriptions into the generative process by linking them with person-specifi features. This helps the dual-DiT architecture synthesize the garments more accurately.
Garment Aligner Module. We design lightweight aligner that conditions generation on clean garment images, replacing conventional denoising objectives. This leads to better alignment consistency on the overall dataset and preserves more precise visual retention.
Extensive experiments on the Dress Code and VITON-HD datasets demonstrate that TEMU- VTOFF outperforms prior methods in both the quality of generated images and alignment with the target garment, highlighting its strong generalization capabilities.
RELATED WORK
Virtual Try-On. As a core task in the fashion domain, VTON has been extensively studied by
the computer vision and graphics communities due to its practical potential (Bai et al. 2022 Cui
et al. 2021 Fele et al., 2022 Ren et al. 2023a). Existing methods are broadly categorized into
warping- based (Chen et al. 2023 Xie et al. 2023 Yan et al. 2023) and warping-free approaches (Zhu
ct al. 2023 Morelli et al. 2023 Baldrati et al.] [003 Zeng et al. 2024, Chong et al. 2025). with a
growing shift from GAN- (Goodfellow al. 2020) to diffusion- based frameworks (Ho et al.
2

---

Published as a conference paper at ICLR 2026
2020
Song al. 2021 VITON (Han et al. 2018) and its variants (Wang al. 2018 Choi et al.
2021 Kang et al. 2021 improve garment alignment and synthesis quality, but often produce artifacts due to imperfect warping. To mitigate this, warping- free methods leverage diffusion models to bypass explicit deformation (Zhu et al., 2023 Morelli et al. 2023] Xu et al. 2025 Choi et al. 2024), using modified cross- or self-attention condition generation on garment features. However, these pre- trained encoders tend to lose fine-grained texture details, motivating methods like StableVITON (Kim) al. 2024 to add dedicated garment encoders and attention modules, increased computational cost. Lately, DiT-based works (Jiang et al. 2025 Zhu et al., (024)) show the benefits of Transformer- based diffusion models for high-fidelity garment to person transfer. Finally, some models adopt more elaborate conditioning strategies. For instance, LOTS introduces a pair-former module for handling multiple inputs (Girella al. 2025), while LEFFA learns a flow field from averaged cross-attention maps and employs learnable tokens to stabilize attention values (Zhou et al. 2025). While most works focus on generating dressed images from separate person and garment inputs, the inverse problem (i.e., reconstructing clean garment representations from worn images) remains underexplored.
Virtual Try-Off. While VTON has been extensively studied for synthesizing images of a person wearing a target garment, the recently proposed VTOFF task shifts the focus toward garment- centric reconstruction, aiming to extract a clean, standardized image of a garment worn by a person. TryOffDiff (Velioglu et al., (2024) introduces this task by leveraging a diffusion-based model with SigLIP ZZhii et al., (023) conditioning to recover high-fidelity garment images. Building on this direction, TryOffAnyone (Xarchakos & Koukopoulos (024] addresses the generation of tiled garment images from dressed photos for applications like outfit composition and retrieval. By integrating garment-specifie masks and simplifying the Stable Diffusion pipeline through selective Transformer tuning, it balances quality and efficiency. In both cases, these works have been designed for single- category scenarios, limiting scalability to diverse data collections. Recent efforts have started to address these limitations. MGT (Velioglu et al., (0225) extends VTOFF to multi-category scenarios by incorporating class-specific embeddings to handle diverse clothing types within a unified model. More ambitious efforts unify VTON and VTOFF in single framework: Voost (Lee & Kwak (2225) proposes a single DiT to learn both tasks, while One Model For All (Liu et al. [2025) introduces partial diffusion mechanism achieve similar goal. On different line, Any2Any
(Guo let al. 2025) adapts FLUX (L (Lbbs) 2024) using a LoRA-based module (Hu et al. 2022) for this task. While these works reflect shift from person-centric synthesis to garment-centric understanding, they still suffer from structural artifacts (e.g., shape, neckline, waist) and inaccurate colors or textures. We hypothesize that this mismatch is due too generic architectural choice, not tailored for the specific needs of the VTOFF setting. In this work, we focus on existing VTOFF open problems, such as multi-category adaptation, occlusions, and complex human poses, and propose a novel VTOFF-specific architecture enhanced with text and fine -ee-grained mask conditioning and optimized with a garment aligner component that can improve the quality of generated garments.
Conditioning Methods in Diffusion Models. To overcome the limitations of text-only conditioning. many schemes leverage additional visual inputs such as segmentation maps, bounding boxes, poses, and points (Snn et al. 2024: Li et al., 2023 Chen et al.] /2024; Nie et al. 20244 Lin et al. 2024
Wang et al. 2024). Prominent methods like ControlNet (Zhang et al. (023) and A2I-Appteer (Mou
et al. (2024) inject spatial conditions via auxiliary networks, while IP- IP-Adapter (Ye et al. 2023) uses separate attention branches more suited to U-Nets than DiTs. Other works focus on unifying multiple conditions, either through modular controllers like Uni-ControlNet (Zhao et al.) 2023) and dedicated adapters (Lin et al.] 22025, or by concatenating visual embeddings directly into the Transformer input sequence (Tan et 2025 Wang et al. 2025 Xiao et al. 2025). Although these methods are effective for general personalization tasks (i.e., placing an object from one image into another), they lack the fine- ane-grained conditioning mechanism necessary to extract specific garment data from person images, a gap we address to unlock the VTOFF task.
3 METHODOL.OGY
Preliminaries. The latest diffusion models are a family of generative architectures that corrupt a ground-truth image 2g according to a flow-matching schedule (Lipman et al. (023) defined as
2, = (1 t) + te, e~ (~, 1), EE 10. Then, a diffusion model estimates back the injected noise &, through a Diffussinn Transformer (DiT) (Peebles& & Xie, 2023), obtaining a prediction 2.. In Stable Diffusion 3 (SD3) (Esser et al.)
(I)
3

---

Published as a conference paper at ICLR 2026
Grale
Gammer
Faran
Mocatimage
Stale
kays ToykTu
DIT Block N
Bock
MEF
Feature Extractor
Scali
x
Hybrid
Hybrid DXT Block
Hybrid DIT Block N
CaST
Frues
A Aist
Denoising DIT
Iritu hoom
Garment Latent
Pessen Repchidaas
AdxUN
AligentAlgnerr
Figure 2: Overview of our method. The feature extractor FE processes spatial inputs (noise, masked image, binary mask) and global inputs (model image via AdaLN), The intermediate keys and values extractor are injected into the corresponding hybrid blocks of the garment generator FD- The main DiT then produces the final garment using the proposed MHA module. Training combines diffusion loss on noise prediction with an alignment loss with DINOv2 features of the target garment.
K Kxttractor V
2024), the 16-channel latent 2; E P x16 is obtained projecting the original RGB image IR HxXxWXx with a variational autoencoder & (Kingma & Welling 2013), obtaining E(x), with H, W being height and width of the image, and f the spatial compression ratio of the autoencoder. Finally, the model is trained according
MSE loss function Lairt
Laiff = Exoreet [|IE= Ez(zz,
(2)
Overview. An overview of our method is shown in Fig. 2 The objective is generate in- shop version of the garment worn by the person. A critical design choice lies in processing the dressed person image so extract meaningful information for injection into the denoising process. To this end, we adopt a dual-DiT architecture, built upon SD3, with the two models assigned to complementary roles. Firstly, we design the first DiT as feature extractor FF that encodes the model image moddl and outputs its intermediate layer features at timestep / 0 and not from subsequent timesteps, as we are interested in extracting clean features from FE. This block is trained with diffusion loss to generate the person image. Once trained, this model outputs meaningful key and value features of the dressed person. Secondly, the main DiT generates the garment By leveraging the intermediate features from Fg in modified textual-enhancee attention module.
3.1 DIT FEATURE EXTRACTOR
At inference time, the only available input is the clothed person image &model E Hx from which we also extract the mask. To encode this information, we compute the visual projection
epoo CLIP(amodd) model) E 2048
which is then used modulate the latent 2, through the Adst.nest scale y and shift 3:
MLP(t, epoo));
+ (3)))z + (
Existing VTON approaches rely on two visual inputs: the target garment and the person. In our case, however, the model can rely only on person features, from which it is more complex to extract the garment features. This shortcoming makes the CLIP vector efoal the real bottleneck in a unified architecture setting, the CLIP projection alone is too coarse properly encode this information.
4

---

Published as a conference paper at ICLR 2026
To address this, propose introducing a dedicated feature extractor Fg, allowing FD to concentrate exclusively on the garment generation task. The architecture of FE mirrors that of the main SD3 DiT module Fp. with the only difference being its imput layer, which is adapted to handle additional visual inputs in the channel dimension. The inputs are a global input with the person image mmndel E R#x x3 encoded as euss and leveraged by the modulation layers of FE, and local spatial input
as the channel- concatenation x! = [21, M, x E R of the latent 21, the encoded latent of the masked person image JM E (Imadde @ M) E R where is the SID3 VAE encoder, h = H and w = W/S denote the spatial dimensions after downsampling by the factor f 8, and the interpolated binary mask .M Rxxxx1 encoded through the Transformer projector p pxxwx33 +
with S as sequence length and d as embedding dimension.
This design choice is central to our method, as each layer output of the feature extractor FE retains meaningful intermediate representations of both the person and the garment. Leveraging these features offers three key advantages: (i) instead of the collapsed CLIP representation, we obtain expanded features of dimension S x d; (ii) the L layers of FE capture information at multiple granularities, progressing from coarse to fine (Avrahami et al., 2025 Skorokhodov et al., (025). so that each layer 1. conveys different level of detail about the same image; (ii) since FE shares the same architecture as FD. the features extracted at layer from F are naturally better aligned with those of F'D. Motivated by these considerations, we extract the keys K extractor and values V from every layer I of FE-
extractor
3.2 DUAL-DIT TEXT- EEXT-ENIINNCED GARMENT TRY-OFF
Without loss of generality, we will omit the index I when referring to Q' K' and V of Fe and FD. since the conditioning scheme is applied uniformly across all layers. Given the extracted features Kextractor and Vextrattor we propose to modify the SD3 attention scheme to incorporate such information, leading to our Multimodal Hybrid Attention (MHA).
Multimodal Hybrid Attention. Our new module scamlessly mix text information, latent features of the denoising DiT, and intermediate features from Fg. Inspired by the key findings in SD3 (Esser ct al. 2024). we concatenate the text features with the visual inputs along the sequence length dimension, thus obtaining:
== 1Q2z. Qtext]
K K= [K2, K Kextactorr K
[VZ 2E V Vextractor Vieal-
(4)
This module allows the features Quest to attend both the latent projection Kz, and the extractor features K Kextrctor- The resulting attention matrix АmнА captures three key interactions: (i) Alextezza preserving pre-trained alignment between language and latent image tokens, (ii) Ag, evextrattoo fa- cilitating transfer between the input garment and the person representation, and (iii) Alextesextractor grounding the text in the structural features provided by the extractor.
Text embeddings are constructed via the concatenation of CLIP (Radford et al. 2021 and T5 RRaffel et al. 2020) encoders applied to the input caption follows:
Ctext [CLIP(c), TS())), with etext E R 1096
(5)
Now we pose a relevant question: is it possible to disambiguate the garment category from the mask alone? A mask input can improve multi-category handling by acting hard discriminator between two garments, in contrast to text, which acts as soft discriminator since it does not directly indicate the pixels occupied by the target garment. Therefore, the mask can help to visually force the model to retain only upper- or body information but it can not tell much about the appearance of garment, because highly warped together with the person, resulting visual artifacts. Textual information is critical, together with mask information, to extract the category information of the garment. To address this, we decide to use also the global conditioning scheme provided by AdaLN (Huang & Belongie 2017) in SD3. As shown in previous works (Garibi et al. 2025). these layers can be successfully leveraged to adapt "appearance" or "style" information into existing Transformer-batt based architectures. For this reason, we extract pooled textual representation epool E 222048 of CLIP textual features of the caption e and inject them into the model through the modulation layers, following Eq. 3 The pooled vector epool E 2048 encapsulates a coarser
Following SD3, consider the combined embedding from CLIP VIT-L and Open-CI.I IP bigG/14
5

---

Published as a conference paper at ICLR 2026
representation than the full textual embeddings Cix 77774000 thus being suitable for high-level information conditioning.
Training. We employ a two-stage training procedure: we train the module FE alone, detached from the dual DiT Fp, according to the diffusion loss Lai defined as follows:
Lextmatto Exzore.t |lee FE Tmodel t)]
(6)
Then, train the dual DiT FD following a diffusion loss with multiple conditioning signals:
PDTT = Ez Ezg
|| - (z epool, Fr Lmodel, 0), 0
(7)
where =y= = is the latent representation of the target garment encoded by the VAE, and with FE Pmodel, 0) being the list of keys and values extracted from FE at timestep t = 0. We extract this list from F'g key/values from clean data.
and re-use them in FDD for all subsequent timesteps, as want to use
3.3 GARMENT ALIGNER
While model is effective generating realistic and structurally coherent garments, observe occasional failures in preserving high-frequency details such as fine-grained textures and logos. We hypothesize two primary contributing factors: (i) the diffusion loss Ldift. defined in the noise space, optimizes over perturbed latents rather than directly over image-space reconstructions, limiting its sen- sitivity to fine- theegrained patterns; and (ii) the inherent generation dynamics of diffusion models, where errors introduced in early timesteps typically encoding low-frequency content can accumulate and degrade the fidelity of high-frequency details later stages. To mitigate this, we draw inspiration from REPA YYu et al., 2025), and propose to explicitly align the internal feature representation of our DiT with that of a pre-trained vision encoder. Specifically, we encourage patch-wise consistency between the eighth Transformer block of our main DiT model FD and the corresponding features extracted from DINOv2 (Oquab et al. 2023).
Formally, let hOT E R 3072xd denote the token sequence obtained from the eighth Transformer block of the DiT decoder FD. corresponding to 64 x 48 patch grid with embedding dimension Separately, let he E TR. 1021xdd be the 32 x 32 token grid extracted from a frozen DINOv2 encoder, with embedding dimension d' (where d' * d). To bridge this mismatch, we introduce a lightweight garment aligner module composed of convolutional neural network OeNN 164xxxxx 32xxxxx which is used to downsample the spatial token grid while preserving local structure and to project the token embeddings into the DINOv2 feature space. The aligned tokens are defined as hDIT éCNN (ham) E R x024xd'.
We then enforce feature-level consistency via cosine similarity loss:
N
1
Lalign
-I -E
COS
h DIT
h
(8)
N
where iiP and h are the i-th aligned and reference tokens, respectively, is the patch index, N is the total number of tokens, and cos is the cosine similarity. It is important to note that the garment aligner is strictly training-ting time component used to compute Lalign- It is discarded during inference, adding no computational overhead to the generation process.
The garment aligner is applied in the second stage of our training. Our final training objective combines the standard diffusion loss LIST with the garment alignment loss Latign previously introduced. The overall objective is thus defined as:
Overall Loss Function.
Liuil LDi + X falign-
where is a hyperparameter that balances the contribution of the two loss components.
EXPERIMENTS
4.1 COMPARISON WITH THE STATE OF THE ART
We conduct our experiments using two publicly available fashion datasets: VITON-HD (Choi et al. 202T) and Dress Code (Morelli et al. 2022). VITON-HD contains only uoder-body garments and

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative results on the Dress Code dataset, considering both the entire test and the three category-specine speccffcc subsets. 1 indicates higher is better, + lower is better.
All
Bopy
Method
SSIM PSNR
LPIPS 1 DISTS
FID
KID
SSIM
PSNR
LPIPS
DISTS
FID 4 KID
TryOfDiff
76.59
11.54
40.62
29.04
37.97
17.30
Any2AnyTryon
77.56
12.67
35.17
25.17
12.32
3.65
76.61
12.27
38.99
25.78
15.77
3.22
MGT
77.77
11.99
35.37
27.28
13.47
5.28
76.77
11.44
39.70
28.13
19.49
6.87
TEMU-YTOFF
75.95
12.90
31.46
18.66
5.74
0.65
74.54
12.51
35.48
19.75
10.94
0.76
Lower-Body
Dresses
Method
SSIM
PSNR
LPIPS L DISTS 4 FID 4. KID J
SSIM t
PSNR LPIPS
DISTS
FID J KID
Any2AnyTiyon
78.15
12.42
34.72
25.87
30.06
12.01
77.93
13.32
31,80
23.86
19.20
6.27
MGT
77.29
11.64
36.31
28.00
25.98
9.64
79.26
13.09
30.11
25.70
19.09
5.74
TEMU-VTOFF
73.94
12.14
34.60
19.57
13.83
2.04
79.39
14.36
24.32
16.67
11.29
0.59
Input
AnyoAnyTiyon
MST
TEMU-VTOFF (Ours)
Target
Impui
Any2AnyyTyyo
MGT
TEMU-VTOFF (Ours)
Target
Qualitative comparison on the Dress Code dataset between images generated by TEMU- VTOFF and those generated by competitors.
Figure 3:
represents single-category setting, while Dress Code includes multiple categories (i.e., dresses, upper-body, and lower-body garments) enabling evaluation of the generalization capabilities of our methods across diverse garment types. To evaluate the proposed TEMU-VTORE architecture, we use a combination of perceptual, structural, and distributional similarity metrics. Specifically, as VTOFF paired generation setting, we mainly rely on reference- based like SSIM (Wang et al. 2004).
PSNR (Wang et al., 2004), LPIPS (Zhang et al. 2018)) and DISTS (Ding et al. 2020), alongside al. 2018). We compare our approach against recent VTOFF methods, including TryOfiDiff ( /Velioglu al. (024). TryOffAnyone (Xarchakos Koukopoulos 2024), MGT (Velioglu et al. 2025). Voost (Lee & Kwak 2025). One Model For
FID (Parmar al. 2022j and KID (Binkowski
All (Liu et al.) 2025), and Any2Any Tryon (Guo al. 2025). For TryOffAnyone, Voost, and One Model For All, we report the results only on the VITON-HD dataset because they have not been trained on the Dress Code dataset. Additionally, we retrain TryOfIDiff on Dress Code using the official code and hyperparameters provided by the authors. Since TryOffDiff is not designed to handle multi-category garments, we report results only for the upper-body category.
Results on the Dress Code Dataset. Table reports the experimental results on the Dress Code dataset. As observed, our method outperforms existing state-of-the-art approaches across most evaluation metrics and garment categories. These results indicate that our approach is category- agnostic and benefits from the joint use of textual garment descriptions and fine- see-grained masks. Consequently, our model achieves a better perceptual quality and closer alignment with the ground- truth distribution compared to competing methods. Performance on lower-body garments is slightly lower due to the class imbalance in the Dress Code dataset, which contains significantly fewer lower-body samples (~~9k) compared to upper-body (~15k) and full-body dresses (~~29k).
In Fig. 3 provide qualitative results comparing TEMU-VTOFF with competitors. These examples highlight the challenges posed by the diverse set of categories in Dress Code. As shown, MGT and Any2Any'Tyya frequently struggle preserve key visual attributes such as color, texture, and shape. In contrast, our method is able to closely match the target garment across all categories, demonstrating clear improvement in generation quality.
Results on the VITON-HD Dataset. In Table we report the quantitative results on VITON-HD, In this setting, TEMU-VTOFF sets new sate-of-thh--ar across the majority of metrics, achieving the
7

---

Published as a conference paper at ICLR 2026
Table Quantitative results on the VITON-HD dataset. indicates higher is better, lower is better. f denotes results taken directly from the original papers.
Method
SSIM
PSNR
LPIPS.
DISTS
FID 4 KID
TryO
75.53
11.65
39.56
25.53
17.49
5,30
TryOffAnyone
75.90
12.00
35.26
23.47
12.74
2.85
Any2AnyTTryon
75.72
12.00
37.95
24.32
12.88
3.01
MGT
78.10
36.30
24.70
21.90
8.90
Voost
10.06
2.48
One Model for All'
22.50
19.20
9.12
TEMU-VTOFF
77.21
13.38
28.44
18.04
8.71
1.11
Input
TryofDifff
TryOfAryynee
TEMU- -TOFF (Durs)
Target
Input
Tryllibii
TryOfAAnyoee
TEMU-VTOFF
(Ours)
Target
Figure 4: Qualitative comparison the VITON-HD dataset between images generated by TEMU- VTOFF and those generated by competitors.
best scores for DISTS, FID, and KID. This indicates a superior ability to reconstruct structural details and to match the distribution of the ground-truth images. Notably, One Model for All achieves competitive LPIPS score, which we cite from the original paper as public checkpoints are unavailable. Our method, however, achieves more robust performance and DISTS are critical in paired settings, we provide a qualitative analysis (Sec. /Sec.Eoof f the Appendix) showing LPIPS diverge from human judgment, while DISTS aligns more reliably.
FID, KID, and DISTS. Since LPIPS
Overall, our method achieves solid improvements on VITON-HD. although the performance gains less pronounced than Dress Code. This is expected, VITON-HD focuses exclusively upper-body garments and is therefore simpler benchmark. In contrast, the diverse and multi-category nature of Dress Code, with dresses, skirts, and pants, highlights the advantages of our approach, where the joint use of textual descriptions and fine- grained masks proves critical for accurate garment reconstruction. Accordingly, the strengths of our method are most evident complex, matti-category scenarios. A visual comparison on sample VITON-HI HDD images is shown in Fig. M which further demonstrates the improved garment reconstruction quality of our proposed method.
4.2 ABLATION STUDIES
To assess the contribution of each component in our pipeline, we conduct detailed ablation study on the Dress Code dataset reported in Table 3 We first investigate the impact of our dual-stream DiT architecture by removing the feature extractor Fg. In this setting, the garment aligner component is employed. As shown, without the feature extractor, we experience clear performance drop. In contrast, injecting = 0 keys and values from FF into the generator component through the proposed MHA operator enables richer, multi-scale conditioning, leading to better results. Then, we analyze the impact of employing the garment aligner module. As it can be seen, the aligner module helps to improve perceptual fidelity, particularly categorics with complex structures such as dresses, confirming that the designed components plays critical role to the final performance.
Finally, removing garment descriptions or fine sreegrained masks consistently reduces performance, with the largest drop when both absent, confirming that masks act as spatial anchors while text provides complementary semantic and category-level cues. The best results are obtained when both inputs are present, highlighting their complementarity.
8

---

Published as a conference paper at ICLR 2026
Table 3: Ablation study of the proposed components on the Dress Code dataset.
Upper-body
Lower-body DISTS
Dresses
SSIM PSNR LPIPS DISTS FID
KID
DISIS
FID
FID
DISTS
FID
Effect Dual-Strea (wlo Garment Aligner)
w/o feature extractor FR
72.79
11.45
38.61
23.56
1.70
24.97
14.13
23.20
19.54
22.52
16.82
TEMU-VTOFF
76.01
12.85
30.84
20.63
5.91
0.78
21.77
11.26
22.26
14.22
17.86
11.86
Effect Garment Aligner Component
w/o garment aligner
76.01
12.85
30.84
20.63
5.91
0.78
21.77
11.26
22.26
14.22
17.86
11.86
TEMU-VTOFF
75.95
12.90
31.46
18.66
5.74
0.65
19.75
10.94
19.57
13.83
16.67
11.29
Effect of Text and Mask Conditioning
w/o text and masks
71.04
10.92
39.68
25.20
9.63
3.17
23.71
19.75
65.85
49.19
20.12
15.47
w/o text modulation
73.88
12.28
34.63
22.54
7.75
1.52
24.02
13:48
24.33
18.
19.27
13.30
fine-grained masks
74.65
12.30
32.33
20.87
20.85
11:31
22.34
15.74
19: 19:42
13.62
TEMU-VTOFF
75.95
12.90
31.46
18.66
5.74
0.65
19.75
10.94
19.57
13.83
16.67
11.29
Input
w/ Text Only
Text Maak
Target
Input
Garment Aligner
Garment Aligner
Targtt
(a) Evaluation of mask and text joint impact.
(b) Evaluation of garment aligner impact.
Figure 5: Qualitative comparisons demonstrating the contribution of each proposed component. Left: effect of text and mask conditioning as "soft" and "hard" proxies for multi-category modeling. Right: impact the lightweight garment aligner, which guides training with clean garment supervision.
To better understand the strength of each component proposed in our approach, we provide a visual comparison on Dress Code in Fig. When our method relies exclusively on visual features from the person, without any textual guidance, can struggle to resolve ambiguities in the garment design, leading to errors in structural elements such as neckline, sleeve length, overall fit. The introduction of textual description provides essential structural cues, enabling the model capture the intended garment type and style. The fine-grained mask then imposes a precise spatial boundary, enforcing a clean silhouette and sharp edges, which improves the overall shape and contour of the garment. Finally, the garment aligner further improves the visual fidelity by encouraging the reconstruction of high-frequency details. This results improved textures and more accurate patterns, ensuring that the final generated garment is not only structurally correct but also rich in fine-grained detail.
4.3 CROSS-DATASET DATASET GENERALIZATION
To evaluate the robustness of TEMU-VTOFF against domain shifts and its ability generalize unseen garment types and poses, we conduct cross-dataset experiments. Specifically, we train our model on one dataset and evaluate it directly on the test set of the other dataset. We compare against MGT (Velioglu et al. 2025). TryOffDiff (Velioglu et al. 2024), and TryOffAnyone (Xarchakos & Koukopoulos 2024). Note that we exclude Any2Any Tryon (Guo al. 2025) from this specific analysis, it is trained on mixture of datasets including both Dress Code and VITON-HD, making the cross-dataset evaluation unfair. In Table we present cross-dataset generalization results under two transfer settings. When trained on Dress Code and evaluated on VITON-HD, our model consistently surpasses MGT across both perceptual and distributional metrics, achieving a notably lower FID (20.39 vs. 23.11). Conversely, when trained VITON-HD and tested Dress Code (upper-body). our method again shows stronger generalization, obtaining an FID 18.63 and clearly outperforming both TryOfiDiff and TryOffAnyone.

---

Published as a conference paper at ICLR 2026
Table Quantitative comparison where models are trained on one dataset and tested on another to evaluate robustness to domain shift. 1 indicates higher is better, + lower is better.
Method
SSIM
PSNR 1 LPIPS
DISTS 1 FID 1 KID
Troining on Dress Code + Test on VITON-HD
MGT
74.26
10.24
42.57
28.73
23.11
10.81
TEMU-YTOFF
72.80
10.85
40.19
24.20
20.39
7.00
Troining VITON-HD
Test Dress Code (Upper- Body)
TryOfDiiff
75.33
11.50
44.64
32.14
41.91
21.78
TryOffAnyone
71.96
10.52
47.14
27.54
24.45
9.84
TEMU-YTOPY 73.36
11.51
39.74
23.84
18.63
Table VTON results from CatVTON trained on two Dress Code variants: the original dataset and the version augmented with IEMU--TTOF-geneeee images.
All
Upper-Bodh
Training Dataset
SSIM 1
PSNR 1
LPIPS DISTS | EID
KID
SSIM
PSNR 1 LPIPS 1 DISTS FID
KID
Dress Code
90.65
23.03
7.12
9.18
4.56
1.34
92.93
24.32
5.33
7.66
9.58
2.04
Dress Code (Augm..)
90.65
23.36
7,00
9.00
4.15
1.16
90.94
24.39
5.33
7,54
9.26
1.74
Lower-Body
Dresses
Method
SSIM 1 PSNR 1 LPIPS DISTS
FID
KID
SSIM
PSNR
LPIPS
DISTS
FID
KID
Dress Code
91.46
24.44
6.03
7.84
9.60
1.71
87.50
21.18
10.01
12.04
9.58
1.26
Dress Code (Augm..
91.48
24.50
5.95
7.63
9.02
1.38
87.50
21.21
10.00
12,02
9.45
1.12
4.4 DOWNSTREAM UTILITY
To demonstrate the practical utility TEMU- -OOFF, we evaluate its effectiveness as a data aug- mentation tool for the VTON downstream task. High-quality paired data (i.e., person and in-shop garment) is expensive to acquire; our method addresses this by synthetically generating the "in-shop" garment directly from images of clothed people.
Experimental Setup. We use the Dress Code dataset (Morelli et al. 2022) and employ TEMU- VTOFF generate synthetic in-shop garment images for the training samples. Specifically, for each person image in the upper- and lower- body categories, we generate the missing in-shop garment: the lower-body item for upper-body images and the upper-body item for lower-body images. This procedure augments the dataset with additional person-garment pairs generated by TEMU-VTOFF We then employ CatVTON (Chong et al. 2025) (utilizing the SD 3 medium backbone) two distinct settings: trained only on the standard Dress Code training set and trained on the Dress Code training set augmented with the synthetic pairs generated by TEMU-VTOFF.
Results. We evaluate the trained models the official Dress Code test set. As shown in Table 5 the model trained with our augmented data achieves consistent improvements across both perceptual and distributional metrics. Notably, in the upper-body and lower-body categories, the augmented training yields lower FID scores (9.27 vs. 9.58 and 9.02 vs. 9.60, respectively). This confirms that the garments generated by TEMU-VTOFF preserve sufficient structural fidelity and texture details serve as effective training signals, improving the generalization of state-of-the-arr VTON models.
5 CONCLUSION
We presented TEMU- a novel architecture that pushes the boundaries of VTOFF for complex, multi-category scenarios. While existing methods often struggle with detail preservation and accurate reconstruction across diverse garment types, our approach is specifically designed to overcome these limitations. We achieve this through novel dual-DiT framework that leverages multimodal hybrid attention to effectively fuse information from the person, the garment, and textual descriptions. To enhance realism, our proposed garment aligner module refines fine fine-graine grained textures and structural details. The effectiveness of our method is validated by state-of-the-art performance standard VTOFF benchmarks, demonstrating its robustness in generating high-fidelity, catalog-style images.
Acknowledgments. This work was supported by EU Horizon projects ELIAS (No. 101 (01120237) and ELLIOT (No. 101214398), and by the FIS project GUIDANCE (No. FIS2023-03251).
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our method addresses the VTOFF task by generating flat, in-shop garment images from photos of dressed individuals. This enables novel form data augmentation in the fashion domain, allowing clean garment representations to be synthesized without manual segmentation or dedicated photoshoots. By bridging the gap between worn and catalog-like appearances, our approach can improve scalability for fashion datasets and support downstream applications such as retrieval, recommendation, and virtual try-on. However, as with any generative technology. there are important ethical and legal considerations. In particular, our model could be used to reconstruct garments originally designed by third parties, potentially raising issues of copyright and intellectual property infringement. We emphasize that our framework is intended for research and responsible use, and any deployment commercial settings should ensure compliance with applicable copyright laws and respect for designer rights.
REPRODUCIBILIIT STATEMENT
This work uses only public datasets and open-souree models for its training and evaluations. In the Appendix, we include all the implementation and dataset details to reproduce our results. In addition, will publicly release the source code and trained models to further support reproducibility.
REFERENCES
Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov, Kfir Aberman, Dani Lischinski, and Daniel Cohen-Or. Stable flow: Vital layers for training-free image editing. In CVPR, 2025.
Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, and Hongxia Yang. Single Stage Virtual Try-On Via Deformable Attention Flows. In ECCV, 2022.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2.5-VL Technical Report. arXiv preprint 1392333502..3923, 2025.
Alberto Baldrati, Davide Morelli, Giuseppe Cartella, Marcella Cornia, Marco Bertini, and Rita Cucchiara. Multimodal Garment Designer: Human-Centrie Latent Diffusion Models for Fashion Image Editing. In ICCV, 2023.
Mikolaj Binkowski, Danica Sutherland, Michael Arbel, and Arthur Gretton. Demystifingg MMD GANs. arXiv preprint arXiv:1801.014011 2018.
Chich- Yun Chen, Yi-Chung Chen, Hong-Han Shuai, and Men-Huang Cheng. Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network. In ICCV, 2023.
Zhennan Chen, Yajie Li, Haofan Wang, Zhibo Chen, Zhengkai Jiang, Jun Li, Qian Wang, Jian Yang, and Ying Tai. Region-aware text-to-image generation via hard binding and soft refinement In ICCV, 2024.
Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo. VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization. In CVPR, 2021.
Yisol Choi, Sangkyung Kwak, Kyungmin Lee, Hyungwon Choi, and Jinwoo Shin. Improving diffusion models for authentic virtual try-on in the wild. In ECCV, 2024.
Zheng Chong, Xiao Dong, Haoxiang Li, Shiyue Zhang, Wenging Zhang, Xujie Zhang, Hanqing Zhao, Dongmei Jiang, and Xiaodan Liang. CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models. In ICLR, 2025.
Aiyu Cui, Daniel McKee, and Svetlana I Lazebnik. Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-On and Outfit Editing. In ICCV, 2021.
Keyan Ding, Kede Ma, Shiqi Wang, and Ecro P Simoncelli. Image Quality Assessment: Unifying Structure and Texture Similarity. IEEE Trans. PAMI, 2455)22577-2881, 2020.
11

---

Published as a conference paper at ICLR 2026
Patrick Esser. Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling Rectified Flow Transformers for Sigh-Resolution Image Synthesis. In ICML, 2024.
Benjamin Fele, Ajda Lampe, Peter Peer, and Vitomir Strue. C-VTON: Context-Driven Image-Based
Virtual Try-On Network. In WACV, 2022.
Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data. In NeurIPS, 2023.
Daniel Garibi, Shahar Yadin, Roni Paiss, Omer Tov, Shiran Zada, Ariel Ephrat, Tomer Michaeli, Inbar Mosseri, and Tali Dekel. TokenVerse Versatile Multi-concept Personalization in Token Modulation Space. In SIGGRAPH, 2025.
Federico Girella, Davide Talon, Ziyue Liu, Zanxi Ruan, Yiming Wang, and Marco Cristami. LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Tea Text Pairing. In ICCV, 2025.
lan Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Networks. Communications of the ACM, 63(11):139-144, 2020.
Hailong Guo, Bohan Zeng, Yiren Song, Wentao Zhang, Chuang Zhang, and Jiaming Liu. Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks. arXiv preprint arXiv:2501.1589) 2025.
Xintong Han, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S Davis. VITON: An Image-Based Virtual Try-On Network. In CVPR, 2018.
Alec Helbling, Tuna Han Salih Meral, Ben Hoover, Pinar Yanardag, and Duen Homg Chau. Concep- tattention: Diffusion transformers learn highly interpretable features. In /CML, 2025.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurlPS, 2020.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Lu Wang, Weizhu Chen, al. LoRA: Low-Rank Adaptation of Large Language Models. In ICLR, 2022.
Xun Huang and Serge Belongie. Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization. In ICCV, 2017.
Phillip Isola, Jun-Yar Zhu, Tinghui Zhou, and Alexei A Efros. Image-to tnage-to-image translation with conditional adversarial networks. In CVPR, 2017.
Boyuan Jiang, Xiaobin Hu, Donghao Luo, Qingdong He, Chengming Xu, Jinlong Peng. Jiangning Zhang, Chengjie Wang, Yunsheng Wu, and Yanwei Fu. FitDIT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on. CVPR, 2025.
Taewon Kang, Sunghyun Park, Seunghwan Choi, and Jaegul Choo. Data augmentation using random image cropping for high-resolution virtual try-on (VITON-CROP). arXiv preprint arXiv:2111.08270, 2021.
Jeongho Kim, Gyojung Gu, Minho Park, Sunghyun Park, and Jacgul Choo. StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On. In CVPR, 2024.
Diederik P Kingma and Max Welling. Auto-encoding variational bayes.
arXiv preprint
arXivv1312.6114, 2013.
Black Forest Labs.
2024.
Sangyun Lee, Gyojung Gu, Sunghyun Park, Seunghwan Choi, and Jaegul Choo. High-Resolution Virtual Try-On with Misalignment and Hendueedn-Handledd Conditions. In ECCV, 2022.
12

---

Published as a conference paper at ICLR 2026
Seungyong Lee and Jeong-gi Kwak. Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off. arXiv preprint arXiv:2508.0483 04825, 2025.
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. CVPR, 2023.
Han Lin, Jaemin Cho, Abhay Zala, and Mohit Bansal. Ctrl-adapter: An efficient and versatile framework for adapting diverse controls any diffusion model. In ICLR, 2025.
Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, and Bolei Zhou. Ctrl-x: Controlling structure and appearance for text-to-image generation without guidance. In NeurlPS, 2024.
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow Matching for Generative Modeling. In ICLR, 2023.
Jinxi Liu, Zijian He, Guangrun Wang, Guanbin Li, and Liang Lin. One Model For All: Partial Diffusion for Unified Try-On and Try-Off Any Pose. arXiv preprint 250i::200000455533 2025.
Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In ICLR, 2019.
Davide Morelli, Fincato Matteo, Cornia Marcella, Landi Federico, Cesari Fabio, and Cucchiara Rita. Dress Code: High-Resolution Multi-Category Virtual Try-On. In ECCV, 2022.
Davide Morelli, Alberto Baldrati, Giuseppe Cartella, Marcella Cornia, Marco Bertini, and Rita Cucchiara. LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On. In ACM Multimedia, 2023.
Chong Mou et al. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. AAAI, 2024.
Weili Nie, Sifci Liu, Morteza Mardani, Chao Liu, Benjamin Eckart, and Arash Vahdat. Compositional text-to-image generation with dense blob representations. In ICML, 2024.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafranicc, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, al. DINOv2: Learning Robust Visual Features without Supervision. arXiv preprint arXiv:2304.07/9. 2023.
Gaurav Parmar, Richard Zhang, and Jun- Yan Zhu, On Aliased Resizing and Surprising Subtleties in GAN Evaluation. CVPR, 2022.
William Peebles and Saining Xie. Scalable Diffusion Models with Transformers. In ICCV, 2023.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 21404)): -6., 2020.
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He, ZeRO: Memory Optimizations Toward Training Trillion Parameter Models. In SC, 2021.
Bin Ren, Hao Tang, Fanyang Meng, Ding Runwei, Philip HS Torr, and Nicu Sebe. Cloth Interactive Transformer for Virtual Try-On. ACM TOMM, 20(4):1-20, 2023a.
Bin Ren, Hao Tang, Yiming Wang, Xia Li, Wei Wang, and Nicu Sebe. PI-Trans: Parallel-convmlp and implicittrransffommtion based Gan for cross-view image translation. In ICASSP, 2023b.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In CVPR, 2022.
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. In NewrlPS, 2019.
13

---

Published as a conference paper at ICLR 2026
Ivan Skorokhodov, Sharath Girish, Benran Hu, Willi Menapace, Yanyu Li, Rameen Abdal, Sergey Tulyakov, and Aliaksandr Siarohin. Improving the diffusability of autoencoders. In ICML, 2025.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising Diffusion Implicit Models. In ICLR, 2021.
Nick Stracke, Stefan Andreas Baumann, Kolja Bauer, Frank Fundel, and Björn Ommer. Cleandift: Diffusion features without noise. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 117-127, 2025.
Yanan Sun, Yanchen Liu, Yinhao Tang, Wenjie Pei, and Kai Chen. Anycontrol: create your artwork with versatile control on text-to-image generation. In ECCV, 2024.
Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer. ICCV, 2025.
Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdul- mohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. arXiv preprint wrXiv:2502.14786 2025.
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation. In CVPR, 2023.
Riza Velioglu, Petra Bevandic, Robin Chan, and Barbara Hammer. TryOffDiff: -irtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models. In BMVC, 2024.
Riza Velioglu, Petra Bevandic, Robin Chan, and Barbara Hammer, MGT: Extending Virtual Try-Off to Multi-Garment Scenarios. In ICCV Workshops, 2025.
Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang. Toward Characteristic-PPrsseee Image-based Virtual Try-On Network. In ECCV, 2018.
Haoxuan Wang, Jinlong Peng, Qingdong He, Hao Yang. Ying Jin, Jiafu Wu, Xiaobin Hu, Yanjie Pan, Zhenye Gan, Mingmin Chi, et al. Unicombine: Unified multi-conditional combination with diffusion transformer. In ICCV, 2025.
Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediffu- sion: Instance-level control for image generation. In CVPR, 2024.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Processing. 13(4):600-61,, 2004.
Ioannis Xarchakos and Theodoros Koukopoulos. TryOffAnyone: Tiled Cloth Generation from Dressed Person. arXiv preprint arXiv:2412.08577 2024.
Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation. In CVPR, 2025.
Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao, Haoye Dong, Xijin Zhang, Feida Zhu, and Xiaodan Liang. VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Cilobal-Parsing Learning. In CVPR, 2023.
Yuhao Xu, Tao Gu, Weifeng Chen, and Chengeai Chen. OOTDiffusion Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on. In AAAI, 2025.
Keyu Yan, Tingwei Gao, Hui Zhang, and Chengjun Xie. Linking garment with person via semantically associated landmarks for virtual try-on. In CVPR, 2023.
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-lmage Diffusion Models. arXiv preprint arXiv:2308 06721, 2023.
14

---

Published as a conference paper at ICLR 2026
Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation Alignment for Generation: Training Diffusion Transformers Easier Than You Think. In ICLR, 2025.
Jianhao Zeng, Dan Song, Weizhi Nie, Hongshuo Tian, Tongtong Wang, and An-An Liu. CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model. In CVPR, 2024.
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid Loss for Language Image Pre-Training In ICCV, 2023.
Lvmin Zhang, Anyi Rao, and Mancesh Agrawala. Adding Conditional Control to Text-to-Imace Diffusion Models. In ICCV, 2023.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In CVPR, 2018.
Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan- Yee Wong. Uni-controlnet: All-in-one control to text-to-image diffusion models. NeurIPS, 2023.
Zijian Zhou, Shikun Liu, Xiao Han, Haozhe Liu, Kam Woh Ng. Tian Xie, Yuren Cong, Hang Li, Mengmeng Xu, Juan-Manuel Pérez-Rics Rúa, Aditya Patel, Tao Xiang, Miaojing Shi, and Sen He. Learning Flow Fields in Attention for Controllable Person Image Generation. In CVPR, 2025.
Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, and Ira Manelmmache-SShizemmaa TryOnDiffusion: A Tale of Two UNets. In CVPR, 2023.
Luyang Zhu, Yingwei Li, Nan Liu, Hao Peng, Dawei Yang, and Ira Kemelmacher-Shlizeemee M&m vto: Multi-garment virtual try-on and editing. In CVPR, 2024.
15

---

Published as a conference paper at ICLR 2026
A
ADDITIONAL DETAILS
A.1 DATASETS DETAILS
Dress Code. In our experiments, we adopt the Dress Code dataset (Morelli et al. 2022). the largest publicly available benchmark for image-based virtual try-on. Unlike previous datasets limited to upper-body clothing, Dress Code includes three macro-categories: upper-body clothes with 15,363 pairs (e.g., tops, t-shirts, shirts, sweatshirts), lower-body clothing with 8,951 pairs (e.g. trousers, skirts, shorts), and full-body dresses with 29,478 pairs. The total number of paired samples is 53, 792, split into 48, 392 training images and 5, 400 test images at resolution of 1024 X 768.
VITON-HD. Following previous literature, we also adopt VITON-HD (Choi et al. 2021). a publicly available dataset widely used in virtual try-on rescarch. It is composed exclusively of upper-bodi garments and provides high-resolution images at 1024 x 768 pixels. The dataset contains a total of 27, 358 images, structured into 13, 679 garment-mode pairs. These split into 11, 647 training pairs and 2, 032 test pairs, each comprising a front-view image of garment and the corresponding image of model wearing it.
A.2 IMPLEMENTATION DETAILS
For both the feature extractor and the diffusion backbone, adopt Stable Diffusion 3 medium (Esser et al. 2024). All models are trained on a single node equipped with 4 NVIDIA A100 GPUs (64GB each), using DeepSpeed ZeRO-2 (Rajbhandari et al. 2021) for efficient distributed training. We use a total batch size of 32 and train each model for 30k steps, corresponding to approximately 960k images. Optimization is performed with AdamW (Loshchilov & Hutter 2019), using learning rate of x 10-1, a warmup phase of 3k steps, and a cosine annealing schedule. We train separate models per dataset to account for differences in distribution and garment structure. In all experiments, we set the alignment loss weight À equal to 0.5.
We evaluate our method both with distribution-basee metrics and per-sample similarity metrics. For the first group. we adopt FID (Parmar et al.) 2022) and KID (Binkowski et al. 2018) implementa- tions derived from clean- fid PyTorch package Concerning the second group, we adopt both SSIM (Wang et al., 2004), PSNR (Wang et al. 2004), and LPIIS (Zhang et al. 2018 as they are the standard metrics adopted in the field to measure structural and perceptual similarity between pair of images. We reuse the corresponding Python packages provided by TerchMetrie Finally, we adopt DISTS (Ding et al., 2020) as an additional sample-based similarity metric, as it correlates better with human judgment, shown in previous works (Fu et al. 2023). We stick to the corresponding Python package to compute it for our experiments.
A.3 CAPTION EXTRACTION DETAILS
We leverage Qwen2.5-VL VL (Bai al. 2025 to generate textual descriptions of the garments, which serve as semantic conditioning for our Dual-Dit -iT architecture.
To ensure no ground-trute information leaks into the testing process, we employ two different generation pipelines for training and testing:
Training: We generate captions using the ground-truah in-shop garment images. This ensures the model learns precise semantic correlations between visual features and textual attributes during optimization (see Fig Inference: At test time, the ground-trute garment image is strictly unavailable. Instead, the caption is generated directly from the input person image. Qwen2.55VL, is prompted to analyze the person's s clothing and extract the relevant structural attributes. This ensures our method fully applicable to "in-the-wilee wild" scenarios where only the person image is known (see Fig. 7
We define variable category E [dress, upper body, lower body). To avoid leaking color or texture information (which should be handled by the visual feature extractor FF) and to
16

---

Published as a conference paper at ICLR 2026
focus solely structural guidance, utilize strict prompt template. The prompt explicitly forbids the generation of non-structural attributes:
visual attributes
"dresses": I"Cloth Type", "Waist", "Fit", "Hem", "Neckline", "SLeeve Length", "Cloth Length"l, "upper_ hppper_bedy"; ["Cloth Type", "Waist", "Fit", "Hem", "Neckline", Sleeve Length", "Cloth Length"), "lower_body"; ("Cloth Type", "Kaist", "Fit", "Cloth Length")
System: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
User:
Use only visual attributes that are present in the image. Predict values of the following at- tributes: (vi swilsalllse sual_attr butes [category] ). It's forbidden to generate the following visual attributes: colors, background, and textures/patterns. It's forbidden to generate unspecified predictions. It's forbidden to generate newline characters. Generate in this way: a <cloth type> with <attributes description>.
Qwen Caption: denim shirt with straight fit, long sleeves, and button-down neckline. The hem straight and the shirt appears to be of standard length.
Figure 6: Caption extraction pipeline (training stage).
We decide generate structural-only attributes because base model without text already transfer colors and textures correctly from the person image to the generated garment image. The structural attributes slightly different according to the three categories of clothing, specified vi sual l_attri i butes. For example, the neckline can be specified for upper body and dresses (whole body garments), but not for lower body items.
A.4 ALGORITHM
To provide clear understanding of TEMU-VTOFF, we summarize the core components of our method in Algorithm The pseudo-code outlines the sequential steps involved in training dual-DiT architecture, including multimodal conditioning, the hybrid attention module, and the garment aligner component.
17

---

Published as a conference paper at ICLR 2026
System: You are Qwen, created by Alibaba Cloud. You are helpful assistant.
User:
Use only visual attributes that are present in the image. Predict values of the following attributes: (vi Urisual_se butes [category] }- Do it only for the category garment. It's forbidden to generate the following visual attributes: colors, background, and textures/patterns. It's forbidden generate unspecified predictions. It's forbidden generate newline characters. Generate in this way: <cloth type> with <attributes
Qwen Caption: A leather skirt with fitted waist and short length.
Figure Caption extraction pipeline (inference stage).
B ADDITIONAL QUANTITATIVE RESULTS AND ANALYSES
In this section, we report additional quantitative results and analyses on the effectiveness of the proposed components and design choices.
Efffet of Varying À Parameter. We conducted an ablation study on the Dress Code dataset to assess the effect of the À regularization for the alignment of our main diffusion transformer FD and the DINOv2 features. We report the results in Table 6 As shown, À 0.5 is the overall best choice across all metrics.
Effect of Varying the DiT Block i Used for Latign. A critical design choice in our garment aligner module is determining which internal block of the DiT (Fp) should be aligned with the semantic features from DINOv2. To find the optimal depth, we conduct an ablation study on the Dress Code dataset, varying the block index i {6.8, 12, 18) within the 24-block SD 3 medium architecture.
The results are reported in the middle section of Table 6 We observe that aligning the 8th block yields the best overall performance. These results highlight trade-off between structural guidance and generation flexibility, consistent with recent findings in representation alignment (Yu et al. 2025). The early-to early-to-mid layers of the DiT capture the coarse semantic layout and structural essence of the image. Aligning these with DINOv2 ensures the generated garment respects the target structure while leaving subsequent layers free to refine details. As we move to deeper blocks, perceptual quality degrades. While these layers maintain high structural similarity, the distributional metrics worsen. This occurs because the deeper layers of a DiT are increasingly specialized in predicting the high-frequency noise (or flow velocity) required for the diffusion objective. Forcing these "noisy" layers to align with the "clean", invariant features of DINOv2 introduces an optimization conflict, ultimately smoothing out fine-graines textures and degrading realism.
Analysis of Asynchronous Timestep Conditioning. A critical design choice in our architecture is the use of fixed timestep i for the feature extractor FF, while the main denoising DiT
18

---

Published as a conference paper at ICLR 2026
Algorithm 1 TEMU-VTOFT: Dual-DiT and Garment Alignment for VTOFF
Require:
Person image Bmodl garment caption c, binary mask M, target garment image Bg
Ensure:
Generated garment it
1: Latent encoding:
Encode the target garment: 2g Ex3s)
Sample noise: (V,01) 1)
Apply flow-matching: 2; f- (1 1) + t t
2: Prepare masked spatial input:
Encode masked person image: aM + Ex O M)
Concatenate inputs: x [ [M]]
Extract modulation features:
Equul f- CI.IP CI.IP(Tmoda))
4: Extract keys and values using feature extractor:
( (Kaametors Vesiane L
[1=11 < (x Tmodel, f=0)
5: Encode text information:
Get pooled text embedding: Epvoled f- CLIP (c)
Get full sequence text features: Exext < (c). T((e)
6: Noise prediction:
é, + (D Cpoole, Cuxi, { (Kintaetrr )esiaage,,
Compute diffusion loss: LDiT llét -
Align internal representations:
Extract DiT features: ĪDXT <- tokens from 8th block of FD
Extract DINOv2 features: hene <- DINOv2(#,))
Align via projection: PABST < (HDPTT
Compute alignment loss: Lalign & Σ, cos(ht PiT ,1""
8: Final objective:
Combine losses: Leoral <- LIxT + A Latign
9: Decode final garment:
Run reverse process: 2. t D(Eo)
FD operates on noisy latent 2 at timestep t > 0. This raises an important question: could this discrepancy in timesteps lead to a misalignment in the feature space? In this section, we provide the rationale for this design choice, supported by concurrent work and targeted ablation study.
Our primary motivation is to provide the main generator Fp with the cleanest, most semantically rich conditioning signal possible. By extracting features from Fg at = O we ensure the conditioning information is completely free from stochastic noise inherent to the diffusion process. We hypothesize that injecting features from a noisy timestep & >> 0 would introduce an additional, confounding source of noise into the generation process, thereby degrading the quality of the final output. The key to method is that the MHA module is specifically trained to bridge this temporal gap; learns to effectively attend to the clean conditioning features to guide the denoising of the noisy latent 2t.
This design philosophy is strongly supported by recent, concurrent research that analyzes the internal representations of diffusion models:
The work CIeanDIFT (Stracke et al. 2025) directly argues that adding noise to images before feature extraction is a performance bottleneck that harms feature quality. Their entire method is built the same premise our FF: that extracting features from clean images leads to superior performance without needing task-specific timestep tuning.
Furthermore. ConceptAttentton (Helbling al. 2025) demonstrates that the internal representations of DiT's are highly interpretable and correspond to semantic concepts, particularly at early timesteps. This validates our choice to use / = features, as they represent the purest and most semantically meaningful form of this information.
To validate our design choice, we conducted an ablation study comparing our method with the variant where the feature extractor FF and the denoising Fp use the same synchronous timestep t. The results on Dress Code are presented in Table As shown in the table, our proposed method with asynchronous timesteps significantly outperforms the synchronous variant across the majority of the metrics. This result provides strong empirical evidence for the value clean conditioning and confirms the effectiveness of our proposed Multimodal Hybrid Attention.
19

---

Published as a conference paper at ICLR 2026
Table 6: Additional ablation study results on the Dress Code dataset.
All
Upper-body
Lower-body
Dresses
SSIM f PSNR
LPIPS ; DISTS FID
KID
DISTS FID
DISTS FID
DISTS i FID
Effect of Varying À Porameter
À 0.0 (wlo aligner)
76.01
12.85
30.84
20.63
5.91
0.78
21.77
11.26
22.26
14.22
17.86
11.86
À 0.25
74,29
12.48
33.68
19.41
6.42
0.89
19.65
10.77
21.86
16.53
16.73
11.38
À = 0.5 (Ours)
75.95
12,90
31.46
18.66
5.74
0.65
19.75
10.94
19.57
13.83
16.67
11.29
A 0.75
71.93
11.71
37.03
20.35
7.81
1.41
20.11
11.09
23.85
21.12
17.10
11.69
A 1.0
71.76
11.72
37.21
20:45
7.78
1.39
20.07
11.33
24.11
20.59
17.17
11.79
Effect of Varying the DiT Block Used for Catian
1 6
72.11
11.44
38.00
20.62
8.66
1.76
21.21
12.36
23.25
22.15
17.42
12.49
8 (Ours)
75.95
12,90
31.46
18.66
5.74
0.65
19.75
10.94
19.57
13.83
16.67
11.29
1 12
75.30
12.59
32.71
19.13
6.48
0.87
20.41
11.86
20.01
15,40
16.98
11.72
18
76.16
12.57
31,66
19.17
6.87
1.16
20.27
12.37
19.86
15.52
17.38
12.28
Ffffect of Acynchronous Timestep Conditioning w/ same in FR and Fn,
77.70
12.66
32.69
22.41
9.78
2.30
23.98
17.85
21.29
17.83
21.95
17.52
w/t = in Fg. (Ours)
75.95
12,90
31.46
18.66
5.74
0.65
19.75
10.94
19.57
13.83
16.67
11.29
Table 7: Comparison of CLIP vs. SigLIP 2 as vision encoder for person encoding. indicates higher better, J lower is better.
Method
SSIM
PSNR
LPIPS 4
DISTS 4
FID 4
KID
THEU- w/ CLIP
75.95
12.90
31.46
18.66
5.74
0.65
TEMU-VTOFF w/ 2
76.62
14.33
28.10
18.77
5.08
0.53
We replace our CLIP vision encoder with a more powerful SigLIP (Tschannen al., 2025). We adopt the ViT-g 16 model and retrained our architecture with an additional MLP fMLP 12 1536 + 2044 to project SigLIP 2 output dimension into the SD3 input space. The results are presented Table As noted in TryOnDiff (Velioglu et al.) 2024), employing stronger vision encoder improves the final performance. Our experiments further validate this finding. This improvement due to the better capacity of SigLIP 2 at extracting more fine-grained features. As reported in (Tschannen et al., 2025), this model is trained with binary contrastive loss that processes each text-image pair separately, thus preventing information corruption from different image-text pairs. Moreover, fine- -ininnd details are enhanced with self- deff-distillation loss and masked prediction. Finally, this ablation further demonstrates that our core contribution lies in dual-DiT architecture because this design scheme can be improved with plug-and -nd--nddlae modules, unlike the architectural alternatives that underperform in the same setting.
Generalization with Stronger Vision Encoders.
C ADDITIONAL QUALITATIVE RESULTS
We report extended version of the qualitative results presented in main paper. Specifically, additional visual comparisons between TEMU-VTOFF and competitors are shown in Fig. and Fig. [O on sample images from Dress Code (Morelli et al. 2022) and VITON-HD (Choi et al. 202T. respectively. Moreover, Fig. T presents additional ablation results to analyze the impact of textual and mask conditioning. Finally, we include in Fig. 12 the full set of inputs used for generating the target garment, including the model input, the segmentation mask, and the textual caption.
D USER STUDY
To complement our quantitative analysis and address the limitations of automated metrics capturing fine-grained texture details, conduct human perceptual study.
Experimental Setup. We recruited 42 distinct participants to evaluate the visual quality of the generated garments. The study followed a pairwise comparison protocol. For each trial, partici- pants were presented with the input person image and two generated garment results: one from TEMU-VTOFF and one from competitor (randomly selected from MGT (Velioglu et al.) 2025) or Any2AnyTryor (Guo et al.] 2025)). The position of the images (i.e., left/right) was randomized to prevent bias. Participants asked select the image that best represented high-fidelity, in-shop
20

---

Published as a conference paper at ICLR 2026
Table 8: Pairwise comparison showing the percentage of times participants preferred TEMU-VTOFF over the competing method. "Not Sure" indicates cases where participants found the quality of generated images indistinguishabl..
Comparison
Ours Wins (%) Not Sure (%)
Competitor Wins (%)
TEMU-VTOFF vs. MGT
75.77
16.38
TEMU-VTOFF vs. Any2AnyTryon
77.74
5.64
16.62
Target Garment
TEMU-VTOFF
TryOffAnyone
Target Garment
TEMU-VTOFF
TryOffAnyone
DISTS. 1BS (-32 7%) LPIPS (-24)5%)
DISTS 0.245
DNSTS 1 (-2) 8%)
DETS 0.2
03365
LPIP5. UWF5.0.2990 (-3A%)
LPIPS. 0.326
DISTS. 0.150 (8 0%)
DISTS: 0.209
DISTS 0.166 16.9%)
DISS. 0.199 LPIPS. 0.333
LPE5.0. 073.0..32 (-124%)
LPIPS. 0.345
LPIPS. 0.343 (.88))
Figure 8: Additional qualitative results of TEMU-VTOFF and competitors VITON-HD CCoii et al. 2021), with per-sample metrics. DISTS emphasizes structural differences between images better than LPIPS, confirming its higher correlation with human judgments.
version of the garment worn by the model, considering texture preservation, structural integrity, and overall realism.
Results. We collected a total of 1,920 pairwise judgments. The results, summarized in Table g demonstrate strong preference for method. TEMU-YTOFF outperforms MGT with win rate 75.77% and Any2AnyThyy our quantitative analyses (particularly the DISTS and FID scores), confirming that TEMU-VTOFF produces results that are perceptually superior to state-of-the-art methods.
with a win rate 77.74%. These results strongly corroborate
E DISCUSSION AND LIMITATIONS
Our method demonstrates strong performance and generalization, yet inherits some inner problems of foundational models such as Stable Diffusion 3 (Esser et al.) (2024). Although we improve the rendering of large logos and text, the model still struggles with graiigr details, including complex texture patterns, small printed text, and the correct reproduction of small objects such as buttons. Moreover, as mentioned in the main paper, reconstruction is less reliable for lower-body garments than for upper-body items or dresses, likely due to class imbalance in the Dress Code dataset. For completeness, we show set of failure cases Fig, 13 and Fig. [4 on sample images from Dress Code (Cui et al. 2021) and VITON-HD (Lee et al. 2022 respectively.
We further analyze how the adopted perceptual metrics correlate with the presence of visual artifacts in the generated images (Fig-B8 [). While quantitative comparisons are reported averages over the full test set, inspecting per-sample metric values particularly informative in VTOFE, as always lives in paired setting. Edge cases, such missing garment components incorrect structural details, are often critical, and VTOFF naturally provides paired and DISTS play an important role, as both measure image-to-imape distances. It is therefore essential to verify that these metrics respond reliably to detail discrepancies and appropriately penalize weaker baselines.
samples. this context, LPIPS
21

---

Published as a conference paper at ICLR 2026
For each example in Fig. display three images: the ground-truth target, the output of TEMU- VTOFF, and the output of TryOffAnyone (Xarchakos & Koukopoulos 2024). We present four representative comparisons. The first two (columns 1-3) show cases where TEMU-VTOFF preserves fine garment details that are lost by the competitor. The remaining two (columns 4-6) contrast accurate samples from our method with misaligned rotated outputs produced by TryOffAnyone. For each pair, we report the corresponding per-sample DISTS and LPIPS values, along with the percentage improvement of our results over those of TryOffAnyone. In situations where clear qualitative gap exists, DISTS consistently reflects the expected difference, whereas LPIPS often fails to penalize severe distortions and occasionally even assigns worse scores the petter-performing method (e.g.. row 2, column 5). These observations provide empirical evidence supporting our choice to include DISTS as part of the overall evaluation protocol. This observation consistent with findings from DreamSim (Fu et al. 2023). as discussed in Sec. A.2
F LLM USAGE
In this work, we employ LLMs (specifically Qwen2.5-VL.) to extract garment-related textual descrip- tions, which serve as conditioning signals for generation. Beyond this, LLMs were employed solely for minor language refinement. They did not contribute to the design of experiments, the analysis of results, or the generation scientific content.
22

---

Published as a conference paper at ICLR 2026
Model Input
Any2AnyTryon
MGT
TEMU-VTOFF
Target Garment
Figure 9: Additional qualitative results of TEMU-VTOFF and competitors on Dress Code MMorelli
et al. 2022
23

---

Published as a conference paper at ICLR 2026
Input
TryOffDiff
TryOffAnyone
Any2AnyTryon
TEMU-VTOFF
Target Garment
GAP
GAP
GAP
GAFI
GAP
GAP
Figure 10: Additional qualitative results of TEMU-VTOFF and competitors on VITON-HD Choi
et al. 202T
24

---

Published as a conference paper at ICLR 2026
Model Input
w/o Text and Masks
w/o Text
w/o Mask
TEMU-VTOFF
Target Garment
Figure 1I: Additional qualitative results showing the contribution of each component in TEMU- VTOFF on Dress Code (Morelli et al. 2022) images.
25

---

Published as a conference paper at ICLR 2026
Model Input
Binary Mask
Model Masked
Caption
TEMU-VTOFF
Target Garment
long dreae with finted waist, loose fil, and hemn that reaches the floor, The neckline high and the cleeve length is short
cropped lengsh
tatin wide- leg pants with high weist and a straight fit, meahinn the anicken
with decorative neciline tie detail three-quae length, and cloth length.
polo shirt with a fitted waist, a slim straight hem, collar neoding ne. short sleeve length, and short clrth length.
pinstripe cloth with waist, fitted and mid length length
Figure 12: Inputs used to generate the target garment with TEMU-VTOFF, using sample images from Dress Code (Cui et al. 2021)
26

---

Published as a conference paper at ICLR 2026
Model Input
TEMU-VTOFF
Target Garment
Model Input
TEMU-VTOFF
Target Garment
Figure 13: An overview of failure cases on the Dress Code (Morelli et al. 2022 dataset.
Model Input
TEMU-VTOFF
Target Garment
Model Input
TEMU-VTOFF
Target Garment
LEVIS
Figure 14; An overview of failure cases on the VITON-HD
Choi et al. 2021 dataset.
27


Reviews:
[{"review_id": "kKuWgUPmcJ", "reviewer": "Reviewer_mTyd", "summary": "This paper considers multi-garment Virtual Try Off. The proposed approach leverages a dual DiT architecture based on Stable Diffusion 3, where the first network serves as a feature extractor, and the second diffuses the garment itself.\n\nThe feature extractor is trained to diffuse the latents of the model image, and takes as its input the latent $z^t$, the encoded latents of the masked model image and the binary mask of the garment. The diffusion is conditioned on the CLIP embeddings of the original model image scaled and shifted with AdaLN. \n\nThe diffusion network is conditioned on intermediate outputs of the feature extractor and CLIP and T2 textual embeddings of garment captions obtained by Qwen2.5-VL. These are combined in the proposed  Multimodal Hybrid Attention module.\n\nTo further promote detail preservation, the intermediate features of the 8th internal block of the diffusion network is aligned with DINOv2 features.\n\nTraining is done in two stages: First the feature extractor is trained. In the second stage, only the diffusion network is trained, with the values of the feature extractor for timestep 0 serving as the conditioning for all of the timesteps of the diffusion process.", "questions": "[Q1] Which encoder is used to encode the the masked person image in the feature extractor?\n\n[Q2] What is $z_g$ in equation 7? Is the diffusion model in the latent space or the image space?\n\n[Q3] Is garment aligner used during inference?\n \n[Q4] How does this model perform on the samples not from the training dataset? How sensitive is it to the errors in the masking of the model picture?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "[S1] Good quantitative and qualitative results\n\n[S2] A good ablation study justifying most of the design choices.\n\n[S3] Well written and easy to follow.", "weaknesses": "[W1] A3 section of the appendix suggests that the garment captions are based on textual descriptions of the e-commerce garment image. This seems like a fundamental flaw as the original garment caption is not going to exist for samples in the wild where the ground truth is not going to be known. This presents information about test directly seeping into the inference process.\n\n[W2] Some unclear implementation details. See questions.\n\n[W3] A couple of additional ablations would be useful. e.g. Velioglu et al. (BMVC, 2025) report better results with SigLip encoding of the conditioning image. Furthermore, why is it necessary to have two different textual embedders applied to the caption and concatenated?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "m04WiAIjuy", "reviewer": "Reviewer_izfa", "summary": "- This paper introduces TEMU-VTOFF, a diffusion-based virtual try-off model that reconstructs standardized product-style garment images from photos of clothed individuals, addressing a task that is largely unexplored compared to traditional virtual try-on.\n\n- This paper uses a dual DiT architecture where one Transformer extracts garment features from the person image and the other generates the clean in-shop garment image, enhanced with multimodal attention using text and masks.\n\n- This paper employs a garment alignment module and novel supervision loss to preserve structure and fine-grained textures, achieving state-of-the-art results on VITON-HD and Dress Code.", "questions": "Mentioned in the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Purpose-built architecture for try-off instead of reversing VTON pipelines, enabling clean reconstruction across multiple garment categories (upper / lower / full-body).\n\n- Multimodal hybrid attention improves disambiguation and detail preservation by combining visual features with textual descriptions.\n\n- High image fidelity and alignment thanks to the garment aligner module, resulting in superior quality and consistency compared to existing methods.", "weaknesses": "- Your attempt to explore a new direction within the VITON domain is impressive. However, while VITON-HD uses full-body datasets, this paper uses datasets without faces. Is this because including faces would cause errors?\n\n- Would VTOFF also work on more limited imagery such as VITON-CROP [1]? Since this work deals with real-world scenarios, I recommend including [1] in the references.\n\n- It would also be helpful if the ablation study section were organized in a more intuitive manner.\n\n[1] Kang, Taewon, et al. \"Data augmentation using random image cropping for high-resolution virtual try-on (VITON-CROP).\" arXiv preprint arXiv:2111.08270 (2021).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uXOGfw2bIW", "reviewer": "Reviewer_Dtrc", "summary": "The paper studies the inverse of virtual try-on, i.e., try-off. Given a photo of a person wearing clothes, the goal is to generate a standardized product-style image of the garment. This paper proposes TEMU-VTOFF, a dual DiT architecture with a feature extractor for the dressed person and a generator that attends to image, text, and mask tokens through a multimodal hybrid attention. A garment alignment module encourages high-frequency detail fidelity by aligning internal tokens to a frozen vision backbone. Experiments on Dress Code and VITON-HD report state-of-the-art results with ablations on text, mask, extractor features, and the alignment module.", "questions": "- Cross-dataset generalization. Can you report train-on-VITON-HD test-on-Dress-Code and the reverse, for both global metrics and per-category breakdowns?\n\n- Metric justification. Given paired ground truth, why prioritize FID or KID? Please add SSIM and PSNR consistently and analyze correlations with human preference. If possible, add a small human study.\n\n- Tradeoff in garment alignment. Can you quantify the quality–alignment tradeoff across a sweep of alignment strengths and report the setting used in the main results?\n\n- Downstream utility. Try using TEMU-VTOFF to synthesize product tiles from arbitrary web or in-the-wild images to create pseudo-pairs for try-on training. Does this improve a standard VTON model’s accuracy or user preference?\n\n- Caption provenance. How are text captions produced and sanitized to avoid leaking color or pattern attributes beyond the intended structure-only template?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The task definition is with practical value. Inverse try-on is useful for catalog data creation and dataset enhancement. Multi-category handling in a single pipeline is appealing.\n\n- The dual DiT setup and multimodal hybrid attention integrate signals from image, text, and mask in a straightforward and scalable way.\n\n- Solid results on Dress Code. The method shows consistent improvements on distributional and perceptual metrics, with ablations that isolate design choices.", "weaknesses": "(1) Mixed gains on VITON-HD. On VITON-HD the improvements are minor or mixed. For example, LPIPS is **22.50** for One Model for All vs **28.44** for TEMU-VTOFF (LPIPS lower is better, so this favors the baseline), while DISTS is **19.20** vs **18.04** (lower is better, so this favors TEMU-VTOFF). This suggests the gains are not uniform across metrics or categories. A deeper per-category analysis is needed.\n\n(2) Metric suitability and tradeoffs. The ablations imply the garment alignment module can trade off visual quality and alignment. Since paired ground truth exists, full-reference metrics like SSIM and LPIPS are directly meaningful. FID measures distributional similarity and can be less diagnostic in a paired setting. Please justify metric choices, add SSIM and PSNR consistently, and explain which metrics correlate with human preference for this task.\n\n(3) Generalization and robustness. No cross-dataset experiments are reported. A simple but informative test is to train on VITON-HD and test on Dress Code, and vice versa, to evaluate robustness and domain shift.\n\n(4) Qualitative artifacts. Several provided examples contain noticeable errors, which should be acknowledged and analyzed:\n- Row 1, example 1: sleeves unexpectedly longer; skirt shows an unnatural shadow at the waist.\n- Row 1, example 4: garment material looks inconsistent with the source.\n- Row 2, example 2: button count and style differ from the source.\n- Row 2, example 3: top body length should exceed sleeve length, but the result shows a shorter top.\n- Row 2, example 4: trouser color shifts; a bow appears at the waistband that may reflect dataset bias.\n\nA failure-mode analysis with per-category statistics would help.\n\n(5) Broader utility not demonstrated. The paper motivates VTOFF as a tool for data generation, but does not show that the generated product images improve downstream tasks. A small study showing that try-off generated pairs improve try-on training or retrieval would make the case stronger.\n\n(6) Notation and clarity.\nLine 189: 𝑓=8 is used but f is never defined. I guess z_t  should be denoted as  (H/f)×(W/f)×3 if the latent has spatial downsampling by \n𝑓. Please fix the symbol table and all affected equations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QJmL0pWv7m", "reviewer": "Reviewer_nzHz", "summary": "This paper focuses on the virtual try-off task, which generates product images from clothed human images. The authors propose to use both visual and text cues for a richer feature representation from the clothing. In addition, an alignment loss is introduced to refine garment textures by matching DiT features with DINOv2 features. Results on two benmarks show the proposed method ourperforms existing approches.", "questions": "1) How important is the first DiT model that is trained on reconstructing human images? The authors mentioned that the features are better aligned because the two DiTs have similar architecture. However, adding noise to the input human image at early stages in the VTOFF inference seems unnecessary and could be hurtful. Will using coarse-to-fine clean feature layers in DINO with simple adaptors achieve similar effects?\n\n2) In Table 1, lower-body clothing shows significantly worse scores than the other two categories. I would like to see more analysis on the reasons behind it.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1) The motivatio of introducing the alignment loss is clear.\n\n2) The paper is structured well and easy to follow.\n\n3) The authors provide many visualizations of the results.", "weaknesses": "1) The first contribution states that the proposed method does not require category-specific pipelines. However, many similar work including MGT and Any2AnyTryon have achieved this, which makes it less of a valid contribution.\n\n2) In the model design,it is unclear why choosing the eighth Transformer block to match the DINO features. What are the justifications for this design choice? If it's empirical,  can the authors provide ablations since this is a major contribution of the paper? For example, will the generated details benefit from choosing more blocks to match DINO features? Also, since the network predicts noise rather than clean image, will the alignment loss degrades the image quality if the last few transformer blocks are used in the loss function?\n\n3) Evaluation is not sufficient. Common objective metrics like the ones reported in the tables are not very consistent with human perception, especially on evaluating texture details. The authors should provide human study results on the benchmark for a more comprehensive evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)