Mitigating Occlusions in Virtual Try-On via A Simple-Yet-Effective Mask-Free Framework
Authors: Chenghu Du
Shengwu Xiong
Junyin Wang
Yi Rong
Shili Xiong
Venue: NeurIPS 2025 poster ()
Keywords: virtual try-on
de-occlusion
diffusion models
Primary area: deep_learning
TL;DR: This paper investigates occlusion issues in virtual try-on (VTON) tasks and proposes a novel mask-free framework that effectively addresses inherent and acquired occlusions through background pre-replacement and covering-and-eliminating operations.

Abstract: This paper investigates the occlusion problems in virtual try-on (VTON) tasks. According to how they affect the try-on results, the occlusion issues of existing VTON methods can be grouped into two categories: (1) Inherent Occlusions, which are the ghosts of the clothing from reference input images that exist in the try-on results. (2) Acquired Occlusions, where the spatial structures of the generated human body parts are disrupted and appear unreasonable. To this end, we analyze the causes of these two types of occlusions, and propose a novel mask-free VTON framework based on our analysis to  deal with these occlusions effectively. In this framework, we develop two simple-yet-powerful operations: (1) The background pre-replacement operation prevents the model from confusing the target clothing information with the human body or image background, thereby mitigating inherent occlusions. (2) The covering-and-eliminating operation enhances the model's ability of understanding and modeling human semantic structures, leading to more realistic human body generation and thus reducing acquired occlusions. Moreover, our method is highly generalizable, which can be applied in in-the-wild scenarios, and our proposed operations can also be easily integrated into different generative network architectures (e.g., GANs and diffusion models) in a plug-and-play manner. Extensive experiments on three VTON datasets validate the effectiveness and generalization ability of our method. Both qualitative and quantitative results demonstrate that our method outperforms recently proposed VTON benchmarks.

Mitigating Occlusions in Virtual Try-On via A Simple-
Simple-Yet-Effecth Yet-Effective Mask-Free Framework
Chenghu Du
Shengwu Xiong² Junyin Wang'
Yi Rong 1M
Shili Xiong 1.3+
Schooo of Computer Science and Artificial Intelligence, Wuhan University of Technology
2 Interdisciplinnry Artificial Intelligence Research Institute, Wuhan College SShanghai Artificial Intelligence Laboratory
{duch, xiongew, wjy199708, yrong) edu. https://du- chenghu github. io/Dcc/ree- VetccFrreeVVO////
Abstract
This paper investigates the occlusion problems in virtual try-on (VTON) tasks. According to how they affect the try-on results, the occlusion issues of existing VTON methods can be grouped into two categories: (1) Inherent Occlusions, which are the ghosts of the clothing from reference input images that exist in the try-on results. (2) Acquired Occlusions, where the spatial structures of the generated human body parts disrupted and appear unreasonable. To this end, analyze the causes of these two types of occlusions, and propose a novel mask-free VTON framework based our analysis deal with these occlusions effectively. In this framework, we develop two simple-yet-powert prerreplacement operation prevents the model from confusing the target clothing information with the human body or image background, thereby mitigating inherent occlusions. (2) The covering covering-and-chlee and-climinating operation enhances the model's s ability of understanding and modeling human semantic structures, leading to more realistic human body generation and thus reducing acquired occlusions. Moreover, our method is highly generalizable, which can be applied in in-the- scenarios, and our proposed operations can also be easily integrated into different generative network architectures (e.g., GANs and diffusion models) in plug-and-play manner. Extensive experiments on three VTON datasets validate the effectiveness and generalization ability of our method. Both qualitative and quantitative results demonstrate that our method outperforms recently proposed VTON benchmarks.
operations: (1) The background
Introduction
Virtual Try-On (VTON) technology [1, 2, 4, 6, 7, 8, 9] aims to synthesize the user's desired clothing from a garment image onto human models real-person images. It provides customers with convenient and efficient try-on experience that greatly reduces the time and effort required in traditional offline shopping modes. Therefore, VTON has gained substantial interest in recent years, particularly in the fields of fashion, e-commerce, and entertainment.
One of the main problems in building VTON model lies in the unpaired nature of the training data. While the target clothing image and the corresponding ground-truth image person wearing this clothing are provided, the input reference image that contains the same person but wearing a different clothing is typically unavailable. According to the strategies adopted to address this issue, current VTON methods can be roughly divided into two categories: (1) Mask-based methods [10, 11, 5, 12, 7, 8, 13] consider VTON as self-supervised image inpainting problem. They first
*Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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DCI-VTON
IDN-VTON
OOTDiffusion AAAI25
LDE-VTON
CatVTON ICLA'25
Mask
Represent.
MMM23
CVPR'24
arXiv'24
ECCV24
25A/25
Iahurent Occlusion
Acquired
Occhusion
Figure 1: Visualization of the occlusion issues in VTON and the effectiveness of the proposed method. It illustrates the inherent occlusion (Cyan regions) caused by imprecise inpainting masks and acquired occlusion (Red regions) resulting from erroneous human structural representations.
mask the clothing-relevan regions in the ground-truth image, and then attempt to recover these regions based on the target clothing image. In contrast, (2) Mask-free methods [1, 2, 3, 14, 15, 16] directly synthesize pseudo reference images (e.g., via generative model) as the input for VTON model training, thus eliminating the requirement of generating inpainting masks in inference phase.
However, during the training stage, these two types of methods both rely on the inpainting masks to accurately remove the clothing regions or generate appropriate pseudo reference inputs, respectively. As a result, the imprecise masks that cannot fully cover the clothing regions will incorrectly remain a portion of the target clothing areas in the masked (or pseudo) input images. Such remained regions can be misidentified as the human body or background and be mistakenly preserved. Therefore, training with these samples will establish incorrect associations between the target clothing information and the human body or background pixels. This will finally lead to a sub-optimal model that outputs the try-on results containing ghosts of the clothing from reference input images, which are recognized as Inherent Occlusions [17] (see the top row in Fig. 1). In addition, existing VTON approaches may also suffer from another type of Acquired Occlusions, which is typically caused by erroneous human structural representations (HSRs) that misguide the learning and inference processes of the generator. Consequently, the spatial structures of human body parts in the try-on results will be disrupted and appear unreasonable, as shown in the bottom row of Fig. 1.
In this study, we propose simple-yet-fffective approach that is able to deal with both inherent and acquired occlusions in a unified mask-free VTON framework. On the one hand, to tackle inherent occlusions, we design a background pre-replacement operation that replaces the image regions outside the combination of the inpainting mask and the person identity mask in each training sample with either a pure background or a random scene image. In this way, the remained target clothing areas will be filled with background pixels, thus allowing the learned model to differentiate the clothing information from the background more effectively. On the other hand, since HSRs mainly affect the generative quality of human body parts, the acquired occlusions caused by erroneous HSRs mostly occur in situations where the human body generation required, i.c., when trying on clothes with smaller body coverage to the reference image with a larger clothing area. To this end, we develop seeeiig--nd-llmmiinie
operation to mimic these situations so that the related robustness can be improved. Specifically, it first produces pseudo reference images with a different clothing that completely covers the original target clothing. Then, our model is trained eliminate these coverings and reconstruct the underlying human body areas. During this process, the model's 's understanding of human semantic structures will be enhanced under the accurate supervision of real ground ground-truths, thereby mitigating the negative effects of incorrect HSRs. Extensive experiments on three VTTN datasets validate the effectiveness of our framework, surpassing recently proposed benchmarks both qualitatively and quantitatively. The main contributions of this paper are summarized as follows:
We analyze the causes of both inherent and acquired occlusions, and propose a novel mask-free VTON framework that can effectively handle these two types of occlusions.
We design a background pre-replacement operation to prevent the model from mistaking the target clothing information as the human body or image background during the training process, thus mitigating inherent occlusions.
We design a covering-ande saiiing--nndll-inatiin operation to generate pseudo reference images that mimic the situations where acquired occlusions mostly occur. Under the supervision of real

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ground-truths, eliminating the larger clothing in these reference images and reconstructing the human body will improve the model's ability of modeling human semantic structures.
We validate the effectiveness of our method through extensive experiments and demonstrate its scalability to be compatible with different generative network architectures.
2 Related Work
Virtual Try-Ons. Recent work has achieved impressive results virtual try-ons. For instance. Xie et al. [4] then proposed GP-VTON, which warps garments based on the characteristics of each area of garments, advancing garment alignment to the current peak of fully generalizable performance. However, it heavily relies on human parsing, and mask errors lead to a series of failed results. To address this, Du et al. [15] designed cyclic architecture USC-PFN that successfully eliminates the negative impact of masks during inference. Recently, methods based on diffusion models have achieved notable results. For instance, DCI-VTON [5], based on the РьE architecture [1I]. pioneered the possibility of achieving high-quality virtual try-ons. Subsequently, methods like LaDI-VTON [10], StableVITON [7], and AnyDoor [13] have focused on the diffusion model structure to enhance generation quality. However, it has been demonstrated that even elementary Latent Diffusion Models are capable of producing high-quality results. Therefore, optimizing the final output results seems to be more effective than improving the network structure. Recently, Chong et al. [9] proposed CON. They found that training only the self-attention layers of the diffusion model can achieve model convergence at extremely low computational cost. This undoubtedly provides a more efficient research path for subsequent studies. Since then, it seems that the design of model architecture is a laborious and unprofitable task. Based on this, we propose framework that focuses on the needs of the task itself rather than the design of model structure. It can easily solve the occlusion problem that all current methods have failed to address, and provides new shorteut for future research on more robust and practical virtual try-on.
Occlusion. This is long-standing challenge in virtual try-on, the unpaired nature of training data and the imprecise segmentation masks often lead to two types of occlusions (inherent occlusion and acquired occlusion). Early works used 3D models [18] or dense pose [19] infer missing regions, while recent methods adopt inpainting networks conditioned on segmentation masks [17]. Despite these advances, most existing methods treat occlusion as a passive artifact to be inpainted, rather than actively modeling the interaction between visible and occluded regions. We propose a simple-yete yeffffeeeeeeffectvve mask-free framework that eliminates both types of occlusions via two key operations: background pre-replacement and covering-and and-elimimniiinng which enhances the model's understanding of human semantic structures.
Preliminary
Given VTON dataset (g)) E D, where gi E B3xHXx represents source clothing images
and p, E R xW denotes reference person images depicting individuals wearing the corresponding clothing from gi, Due to the unpaired nature of the training data, i.e. the input p; that contains the same person but wearing a different clothing is typically unavailable, prior methods [4, 5, 7, 8] typically leverage mask- based inpainting model M& parameterized by 6, which formulate VTON as an optimization problem by minimizing the following training objective:
6' = arg min Ldist Pi, M.
( (Magn, Pago, 8:)
(I)
where Magn, E {0, H is a inpainting mask, which represents the area of the clothing to be changed on P; as well as the adjacent skin regions. Pagn, P; O (1 Magn,) represents an inpainting person. Both are shown in Fig. 1, column 2. (-ise denotes a suitable loss function, e.g., l and MSE losses. To compensate for the missing structural information (e.g., arm shape) in Magn, region, additional human structural representations r; (c.g., densepose map [19]) of p: are typically introduced as supplementary conditions to guide the synthesis of limbs and clothing. However, the low quality of magn; and usually hinders inpainting methods from obtaining the ideal semantic information to accurately translate the desired human body regions [1, 2], resulting in defective appearance occlusion, as depicted in Fig. 1.
3

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Table 1:
Comparison of occlusion handling capabilities between mask-based and mask-free VTONs. Ma and Ma correspond to mask-based and mask-free models. Dyn and D real denote synthetic (by pre-trained M2) and real datasets. D is generated from well-trained Ma.
syn
Model
Training Data
Mask-Free
Inherent Occlusion
Acquired Occlusion
M
Dreal Daya: Draal
X
X
X X
M
X
Mo
D Syn Dreat
V
partial
To mitigate the negative impacts of Magn, and r,, a mask-free architecture is proposed [1, 2, 3]. It uses the pre-trained well-performed, mask-based inpainting model M to generate pseudo reference image lun; E R (D; wearing random clothing gun;), thus replacing the functions of magn, and rs, to form data triplets (tun, ge P.) for the full-supervised training of mask-free My parameterized by t. It can also be formulated as minimizing the following training objective:
10- = arg min Ldist Mp (Ma (Magn; Pagn; Sun,y r:), 83)
Psaudo Refarence
Image tun;
Since Magn and r; are no longer required as input conditions at Ma. it seems that this architecture can thoroughly block the negative impact of magn, and r, on the results. However, as can be seen from Eq. (2), the pseudo reference image tun, related to defective magn, and r, that is not responsible in My can still be transferred to Ma during training, which can still cause occlusion in the results.
4 Occlusion Analysis
As can be seen from Fig. 1, occlusion manifests in different forms, but it is mainly caused by Magn and r (subscript i is omitted for brevity). We refer to the occlusion caused by Magn as "inherent occlusion," while the occlusion caused by r is called "acquired occlusion."
Inherent Occlusion. The inherent occlusion stems from the erroneous parsing of the clothing-
imprecise inpainting mask magn that missegments the human body, background, and clothing regions of p. For mask-based methods, Maga serves as the guiding condition for the inpainting model Ma (in Eq. (1)) determine the area Pagn = p O (1 magn) that is to be preserved before and after trying on. Therefore, the residual source clothing areas in Pagn were treated as immutable and preserved unconditionally by Ma- While ensuring accurate inpainting masks for both training data and inference samples might seem like a solution, the diversity of people, clothing, and scenes worldwide makes it impossible to guarantee consistently accurate masks during large-scale data training and inference. Therefore, this also directly sows the seeds of trouble for mask-free methods. In mask-free methods, the mask-basee inpainting model MM must provide a pseudo-refcrence (person) image tun for model Ma (in Eq. (2)) to form a data triplet ((tun. g).p). enabling fully supervised training for MM. This reconstructs p without needing the inpainting mask Maga. However, tun contains residual areas from p. which are mistakenly treated as background during reconstruction and mapped unchanged into the results. This creates incorrect associations between the target clothing and the human body or background pixels, ultimately leading to sub-optimal models that output try-on results with ghosts the clothing from reference images. Consequently, during inference, when dealing with complex postures or rare clothing styles in person image P. the mask-free My still misrecognizes boundaries between the human body and background, resulting in inherent occlusion.
relevant region in p. leading
The acquired occlusion stems from erroneous parsing of the human structure, resulting in an inaccurate human structural representations (HSRs) that misguide the learning and inference processes of the generator. For example, if human parsing (semantic segmentation) map with missing arm is used to translate into a human image, it is evident that the arm in the resulting image will also be missing. Therefore, as one of the conditional inputs, the quality of HSRs determines the quality of the generated human regions. In mask-based and mask-free methods, the manner in which acquired occlusion is produced is the same as that of inherent occlusion. The only difference is that acquired occlusion directly affects the model's 's understanding of human semantic
Acquired Occlusion.

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Covering
Background Pre- Replacement Operation
Process
Eq.
Eq. (9)
Eq (10)
M
Background
Eq. (4)
Replacement
UNet
aggo Mide B
B
Target Clothing
Augmented
Person
Result
Person
Optional
PRI
Person Pa
Eliminating
Psoudo Image
Me
Process
Supervise
Latent
UNet
Eq. (11)
Training
Eq. (12)
Clothing
Result
Person
Missmatch & Match Clothing Candidates
Figure 2: Overview of our proposed framework. includes two operations: the covering and eliminating processes to integrate clothing with a person image, and the background pre-replacement operation create a pseudo reference image by substituting the original background.
structures, thereby translating into disruptions and rendering the spatial structure of clothing and body parts unreasonable.
Based on the above analysis, we summarize the occlusion handling capabilities of mask-based and mask-free VTON methods in Tab. 1. It be seen that they are all directly indirectly affected by maga and r. Motivated by the desire to break through the limitations faced by the aforementioned VTON methods, our goal is to design a new framework with robust VTON model Mg to fundamentally mitigate the two types of occlusion issues.
5 Proposed Framework
To address the two types of occlusion problems, we propose a novel VTON framework (see Fig. 2) that aims to achieve high-quality occlusion- ffree VTON through simple-yet-effeettve method.
5.1 How to Eliminate Inherent Occlusion
To eliminate inherent occlusion, potential and feasible solution is to simultancously remove the residual regions from both the inputs and the supervision signals during training. This prevents the model from mistakenly identifying these residual regions as part of the background or human body. However, detecting and segmenting the residual regions still involves the unreliability of segmentation errors. Therefore, we attempt to replace the entire background that contains the residual regions.
Background Pre- Peeeaeeplacment. To this end, we propose background pre-replacement operation to intercept the residual regions that may propagate during the training process from the source. Given data group (p, magn; mide) E Dreat, where made E {0, 1] H is person identity mask from human parsing, e.g., the head, hands, and feet. As shown in Figs. 2, we segment out the residual background mask MRB E {0, 1) Hf xW from Magn via Eq. (3):
MRB = (Magn + mide C (1 Magn)),
where @ represents the element-wise (Hadamard) product. In this case, (1 MRR) @ contains only the background but also residual clothing regions. Then, B E xx to replace (1 MRB) @ with completely pure background, thereby completely severing the connection between the background and the human body region, expressed Eq. (4):
a background map
p' MAB p MRB) ② B,
where p* is the background-replaced version of p. Thus, ensures that there are residuals in the supervision signal. For the input side, mask-based methods, which require the input of magas are
"The implementation method is shown in the Technical Appendices (Sec.

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unable to effectively eliminate occlusions. Therefore, we use MRB to process its result tun. thereby removing the residuals in the imput of the mask-free model, as shown in Eq. (5):
MRB O tun + (1 MRB) + B.
(5)
For specifie improvements to the mask-free methods, please refer Eq. (22). In summary, our design aims to enable the model to accurately distinguish between the human body and hackground from
In-the-Wild Scenes. Currently, the datasets in use often feature meaningless and low-diversity scenarios. For instance, the backgrounds in the VITON [20] and VITON-HD [21] datasets are irregularly grayiss-white, and most backgrounds in the DressCode dataset [22] are just monotonous wall colors. To enable the model to focus on changes in the inpainting region without being distracted by the diverse backgrounds found in real-world scenarios, the background map B can be designed to be random in-the-wild scene images generated by pre-trained well- well-performed T2I (Text-to-Imape model [23, 24]: B (2I where e the prompt and is random Gaussian noise, Alternatively, B can be designed as random single value matrix: B random(O, 1) E 3 HxW for the pure background scenes.
5.2 How to Eliminate Acquired Occlusion
To address acquired occlusion as much as possible, one potential solution is to significantly enhance the model's 's ability to understand human semantic structures. To this end, a straightforward idea to first produce pseudo reference images with different clothing that completely covers the original target clothing in p. Then, our model is trained to eliminate these coverings and reconstruct the underlying human body areas. During this process, the model's understanding of human semantic structures will be enhanced under the accurate supervision of real ground-trume thereby mitigating the acquired occlusions of incorrect HSRs.
Covering and Eliminating Processes. We introduce an ideal architecture that can achieve the desired processes with de-occlusion model Ma, parameterized by 6, by minimizing the following training objectives:
0- = arg min Ldist p", Mo Covering
Ma ,un) )))
(6)
0
Elininating Procesa
It can be observed that the architecture completely discards HSRs and instead acquires human structural information by learning to parse the human body within p' itself. However, due to the absence of corresponding ground truth for (p', gun), this architecture fails to converge. Moreover, since the mask-free model does not have the interference of magn and r present in the mask-based model, we positively believe that the cover results of mask-free model M can serve the more beneficial pseudo reference images Pun. To leverage this, we directly use the results of M b to optimize Mg. Therefore, Eq. (6) is rewritten as Eq. (7):
8* = arg min Laist (p", MMo (o g)), where Pun = BP (p. gun)),
(7)
0
where BPR is background pre-replacement operations. However, the given gun is random, whereas the gun we expect here should have an area larger than that of the clothing g worn by p'
Correct Covering Process. For simplicity and readability, we first analyze the upper clothing (g. gmn) here, while other cases, such as dresses, are discussed in the Technical Appendices (Sec. C.2). To obtain the desired gun, inspired by [6], we design a functional function A(.) to determine the size of the clothing g on pd and the size of the clothing gan- Although directly comparing sizes is intuitive and effective, for upper clothing, there may be cases where the sleeves of gun are longer than those g, but the neckline is lower than that of g. This makes it impossible to accurately judge the size and achieve complete coverage. Therefore, conduct by separately comparing the arm length and neckline size of p* and Pun- To this end, we trained a
accurate indirect comparison
Thhe implementation method is shown in the Technical Appendices (Sec. C)

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Person Image
Clothing Image
DCI-VTON MMM25
SL.VITON CVPR'24
St.Garment arXiv'24
LDE-VTON AAAI'2
IDM-VTON ECCV24
OOTDiffusion
AAAI'5
CatVTON ICLR'25
Oura
Figure 3: Qualitative results on the VITON-HD dataset. The baseline methods consist of seven SOTA Iittusioon-bssed methods. Red dashed boxes highlight the limitations of each method.
parser S, which generate human parsing (semantic segmentation) maps m E | for 18 human semantic categories, thereby segmenting the human body region with specified semantics, m (pmm) where S = arg min ||m* - Sp(p*)
here, m E Dreal is the human parsing map of p". Therefore, A(-) is formulated as Eq. (9):
W
H o m
A(m m)) = Σ (m
m
m))w.,
where j denotes the specified semantics, e.g., neck, arms. To ensure that only gan with size absolutely larger than participates in the training of the stage represented by Eq. (7), we introduce a gating coefficient yy Following [6], when the and neck sizes of Pun both smaller than those of p", we set 7 = (represents that backpropagation can be performed). In this case, gun can fully cover the original clothing of p", expressed Eq. (10):
7 { 1, if A(m m m 2 0 and A(m mleck) 0,
(10)
0, otherwise.
where Marm E {0, 1] H.W and maeck E 13 represent the semantic layers of the arms and the neck, respectively. By performing the occlusion elimination task represented in Eq. (7), the model can effectively learn to eliminate the acquired occlusions and achieve favorable convergence. However, there exists distribution shift between the pseudo reference image Pun and the real data p. which will bound the performance of Ma-
Correct Distribution Shift. To correct the distribution shift, we perform the reconstruction task with probability of 1 10% during training, thereby expanding the model's perception of the real distribution. This process is represented as Eq. (11):
0"
arg min Laist (p. Mo(p.g)).
(11)
5.3 Plug-and-Play De-occlusion for Generative Models
There are now many types of generative networks, such as the classic GANs [25] and diffusion models [23]. The de-occlusion approach discussed above be fully applied to both. However, for diffusion models, due to their unique Markov process, the implementation process differs slightly from that of GANs. We implemented Ma using the diffusion model in this work, and the overall loss corresponding to Eqs. (7) and (11) is expressed as Eq. (12):
min ( LLDM (Mo g), p"), if 7)
(12)
(LLD (p.g).)) p),
otherwise.
where random(O, 1) denotes the probability value generated randomly. Limited by space, specific implementation details are illustrated in Technical Appendices (Sec. B).

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Table 2: Quantitative comparisons on the VITON-HD and DressCode datasets. For LPIPS, FID, and KID, the lower the better. For SSIM, the higher the better." "Mask-Free -Free" denotes whether the inpainting mask magn and human structural representation (HSR) are used during inference. Bold
denotes the best result. Underline represents second best.
Train Test
VITON-HI
Drossodee Upper
Publication
Backhone Mack-Pree
Methods
SSIM
LPIPS,
FID.,
KID.,
SSIM,1 1
LPPPS,
FID.p
KID.. 4
VITON-HI (21)
CVPR 21 ECCV-22
RecCaet ResCant
X
0.862
0.117
12.117
3.23
n/a
.i/a
nia
r/a
HR- HR-VITON (26)
X
0.578
0.105
11,265
2.73
0.926
0.065
13.82
2.71
GP-YTON [4]
CV
ResUnct
0.881
0.081
9.701
1.26
0,769
0.270
20.11
8.17
LaDI-VTON [10)
MM'23
$D1.5
9,180
3.33
Pbe (11)
CVPR'25
0.143
11,939
3.85
0.8977
0.078
4.64
DCI-VTON [5]
MM'23
SD1.5
X
0.880
0,080
8.998
1.19
0.937
0.042
11.92
1.39
StableVITON [7]
CV
SD1.5
X
0.561
0.084
9.465
1.40
D/a
n/a
StabieGarmmnn [27]
24
SD1.5
0,803
0.104
17.115
K.85
m/a
n/a
Anydoor [13]
C4
10.
IDM-VTON [28]
2CCV'24
0.060
9.842
1.12
0.880
0.056
LDE-VTON [16]
AAAI22
SD1.5
0.884
0.081
9.640
1,21
m/a
n/a
m/a
p/a
CaIYTON [9]
ICLR'25
SD1.5
0.570
0.061
0.287
1.17
0,902
0.045
7.40
2.62
BooW-VTON [14)
25
SDXI
0.862
0.108
8.809
0.82
0.919
0,062
11.03
0.86
Ours
This Work
SD1.5
0.889
0,057
3,854
0.96
0,923
0.042
6.58
1.22
nia: official code or data is inaccessible.
6 Experiments and Analysis
Implementation. Our model is built on the Diffusers framework' with Stable Diffusion v1.5 as the backbone and initialized from the official CatVTON checkpoint [9]. It is fine-tuned for 100 epochs on six NVIDIA RTX 4090 GPUs under Ubuntu 22.04 LTS. Training employs T = 1,000 denoising steps with a linear noise schedule, the AdamW [29] optimizer (8) = 0.5, 82 = 0.999) in fp32 precision, a batch size of 8, and a learning rate of 1 x 10-5
Datasets and Metrics. We conduct exper- iments on three challenging datasets: VI-
TON [20]. VITON-HD [21], and Dress-
Table 3: Quantitative comparisons on the VITON
Code [22]. VITON dataset contains 16,253 image groups, each with a resolution of 256 x 192. It is divided into a training set of 14,221 groups and a testing set of 2,032 groups. VITON-HD dataset, with reso- lution of 512 x 384, comprises 13,679 im- age groups and is split into training set of 11,647 groups and a testing set of 2,032 groups. Dress dataset, also with res- olution of 512 x 384, includes 15,363 im- age groups and is divided into a training set of 12,863 groups and a testing set of 2,500
dataset.
Methods
Publication
Mask-Free
SSIM,
FID.
PF-AFN [2]
CVPR'21
0.89
10.21
RT-VTON [30]
CVPR'22
X
n/a
11.66
DAFlow [31]
ECCV'22
0.88
12.05
DressCode [22]
CVPR'22
X
0.89
13.71
POYNet [32]
TPAMI'25
0.89
13.37
USC-PEN [15]
NeurlPS'23
0.91
10.47
PbE
CVPR'23
0.83
12.56
TPD [12]
CVPR'24
X
0.89
9.58
LDE-VTON [16]
AAAI'25
0.91
9.86
Ours
This Work
0.91
9.23
-We:: official code or data is inaccessible.
groups. All evaluations and visualizations are performed using the test set. For metries, under paired setting (P. g), we employ the SSIM (Structural Similarity Index Measure) [33] to assess the pixel-level similarity between the generated and ground-truth images. Meanwhile, we utilize LPIPS (Learned Perceptual Image Patch Similarity) [34] to measure the perceptual similarity, which captures the semantic and high-level features of the images. For evaluating the overall distribution of generated images compared to real images, under unpaired setting (P. gun), we calculate the FID (Fréchet Inception Distance) [35] and KID (Kernel Inception Distance) [36], which provide insights into how well our method can produce images that match the real-world data distribution.
Baselines.
We utilize state-of-the-art (SOTA) methods for comprehensive evaluation.
GAN-based Methods: PF-AFN [2], RT-VTON [30], DAFlow [31], DressCode [22), POVNet [32), USC-PFN [15], VITON-HD [21], HR-VITON [26], and GP-VTON [4].
8

---

St.Garment Imput arXtv224
IDM-VTON
OOTDifusion AAA/25
CatVTON
Ours
Input
St. Garment
arXiv/24
IDMAVTON ECCV'24
OOTDiflusonn
AAAFSS
CatVTON
ICLR'25
Ours
Upper
Lower Body
Figure 4: Qualitative results the DressCode dataset. The baseline methods consist of four SOTA diffusion baadd methods. Red dashed boxes highlight the limitations of each method.
Diffusion-based Methods: LaDI-VTON [10], PЫЕ [11], DCI-VTON [5], TPD [12], StableVITON [7]. OOTDiffusion [8]. AnyDoor [13], StableGarment [27], IDM- VTON [28]. LDE-VTOR [16]. CatVTON [9], and BooW-VTON [14].
6.1 Comparison with Baseline Methods
We conducted quantitative comparison our method against 21 baseline methods, as shown in Tab. 3. On the low-resolution dataset VITON [20]. our method outperforms existing tech- nologies terms of FID, while achieving comparable results to the SOTA methods in terms of SSIM. This is mainly because the residual parts of the source person favorable for the SSIM metric (for reconstruction). As shown in Tab. 2, on the two high-resolution datasets VITON-HD [21] and DressCode [22]. our method slightly underperforms the SOTA BooW- metrics. One significant
Input
GANs
GANs Ours
Figure 5: Applying our training
framework to GANs.
for this is that Boo W-VTON utilizes a more powerful SDXL backbone [24). Apart from this, method outperforms existing technologies in most cases and also secures the highest overall ranking. This demonstrates that our method can effectively address occlusion issues while generalizing well try-on scenarios with various clothing styles.
VTON in
For a visual assessment', we present qualitative comparison on VITON-HD [21] and DressCode [22]. As shown in Figs. and 4. It can be seen that regardless of the style of the dataset, framework handle both types of occlusions with ease, especially the significant inherent occlusions. Our method almost completely eliminate this issue, which demonstrates the generalization and universality of the proposed approach. Furthermore, as shown in Fig. 5, we applied our training framework to GANs. The occlusion-free results obtained confirm that our framework be generalized any generative network type (GANs or diffusion models, elc.).
6.2 Ablation Studies
We study the effectiveness of each design choice in framework and draw the following conclusions: (#1) Correct Covering Process. We replaced gin in Eq. (7) with both randomly selected gan and that had been filtered through our strategy, in order verify the effectiveness of our filtering approach. As shown in Fig. 6 and Tah. 4, using a randomly selected gun still resulted in significant acquired occlusion. However, after applying our filtering strategy the baseline (CatVTON [9]), the phenomenon of acquired occlusion was largely climinated. This indicates that enhancing the model's 's ability eliminate occlusions in covered samples can improve the model's capability to parse human structures. (#2) Background Pre- Pre-Replacement, Without background pre-replacement,
More visualization results shown in the Technical Appendices (Sec. D) and Supplementary Material

---

Imput Image
Baseline
Baseline
Preceding
Ieput Image
Preceding + #2 Preceding +
Input Imaage
Figure 6: Visual ablation studies of different components in our approach. Zooming in for more details. Red dashed boxes highlight the limitations of each configuration.
the try-on results of configuration #2 in Fig. 6 exhibited significant inherent occlusion. However, after introducing mRn to replace the background of p, the inherent occlusion was eliminated, thereby demonstrating the effectiveness of the background pre-replacement. (#3) Correct Distribution Shift. We verified its effectiveness by separately adding and removing the correct distribution shift process. As can be seen from Fig. 6 and Tab. 4, when the correct distribution shift is removed, some samples exhibit synthetic bias, which is caused by the erroncous distribution of some defective Pun misleading the model training process. Therefore, when it is added, this part of the defect is significantly improved. (#4) In-the- Wild Scenes. Firstly, Tab. 4 presents our background replacement verified the generalization of replacing
method for datasets with pure backgrounds. Additionally, the background of p with B in real-world scenarios, i.c., testing the model trained on the pure background VITON-HI HD dataset with the DressCode dataset. The results in Fig. 6 show that our method can better handle person images with diverse background styles, indicating that using the synthesized B can significantly enhance the model's 's ability to distinguish between the human body and background.
7 Conclusion
In this work, we present a novel mask-free VTON framework to address the inherent and ac- quired occlusion problems that plague existing VTON methods. Our framework introduces two key operations: the background pre-replacement operation and the covering- covering-and-eliminttee and-eliminating op-
Table 4: Ablation studies on VITON-HD.
Configuration
SSIM,1
LPIPS
FID.; | KID., J
Baseline
0.870
0.061
9.287
1.17
#1
0.883
0.059
8.902
1.11
#2
0.882
8.863
eration. The background pre-replacement oper-
#3 0.887
0.058
8.859
0.98
ation effectively mitigates inherent occlusions
#4
8.854
by preventing the model from confusing target
clothing information with the human body image background. The covering-anddellminn and-eiiminaiing operation enhances the model's ability to understand and model human semantic structures, thereby reducing acquired occlusions. Our method is highly generalizable and can be casily integrated into various generative network architectures, such as GANs and diffusion models, plug-and-play manner. Extensive experiments three VTON datasets validate the effectiveness and generalization ability of our proposed framework.
Acknowledgments
This work was in part supported by the National Key Research and Development Program of China (Grant No. 2022ZD01606D4), the National Natural Science Foundation of China (NSFC, Grant No. 62176194), the Key Research and Development Program of Hubei Province (Grant No. 2023BAB083), the Project of Sanya Yazhou Bay Science and Technology City (Grant Nos. SCKJ- the Project of Sanya Science and Education Innovation Park of Wuhan University of Technology (Grant No. 2021 KF0031), and the Huawei Sunpeng-Ascent Innovation Incentive Programme. This work was also supported in part by both computing resources from the Wuhan Supercomputing Center and the Wuhan Artificial Intelligence Computing Center.
JYRC-
SLJC-2022PPTDX-A42
10

---

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13

---

Appendix
For thorough understanding and visualization of our proposed framework, we compile compre- hensive appendix.
Section A: Preliminary: Diffusion Models.
Section B: Algorithmic Representation of Proposed Method During Training and Inference.
Section C: Comprehensive Technical Appendices.
Section D: More Visualization Results Comparing with Baseline Methods.
Section E: Limitations, Failure Cases, and Future Work Discussion.
Section F: Societal Impact Discussion.
A Preliminary: Diffusion Models
Diffusion models [37, 38, 39], as probabilistic generative models, encompass a two-step process:
diffusion and its reverse. The diffusion phase adheres to Markov chain defined by g(zz|z,-1) (zzi VT -,2,1, 3,11,, spanning T iterations with a noise schedule {(+]==1- This schedule incrementally corrupts the initial data, 2o ~ q( ((zo), with Gaussian noise. Each noisy latent state 2, at any timestep can be sampled directly through closed-form sampling function:
21 == 20 + v - OgC, 6~ N~ (0, I),
(13)
The noise level is determined by a₁ = = 3, and ār = II Oa. The reverse process starts with a noisy data zT - (0, I) at step T and gradually denoises it using known real distributions 9(z,-1 (x)) for each step:
where is uniformly sampled from {L...., T).
po (zt-1 k.) N (z1-13 140 (21, t). Eo (ze, t))
(14)
To achieve this, a denoising autoencoder E- is trained to remove noise from 2; to reconstruct zo by optimizing the following objective:
min Ezg,e,t || (zr, - E
(15)
B Training and Inference Procedures
The training and inference procedures of our proposed VTON framework are designed address the inherent and acquired occlusion issues while ensuring high-quality occlusion- free virtual try-on results. The detailed procedures are outlined in Algorithm 1. The training process iterates until convergence, optimizing the model parameters 0 to achieve high-quality occlusion- free virtual try-on. Additionally, for diffusion models, our training objective for Eq. (7) formulated as follows:
||Ex - eg (Zr, Catss(p.... 2x),
min Fx0. 6
(16)
where
2
Vorex V1 - C,Cats((p Zg), Ex (0,1) 11
(17)
Apm x (Pan) 2g x Evae(g), 2pp R (p
(-t denotes the concatenation operation along the spatial dimension. & is the encoder of KL- regularized autoencoder with its default latent-space downsampling factor 8. In addition, our training objective for Eq. (11) is formulated as follows:
|cx co (zi, Catss 2g),t) 1|2
min Ex (Ni.xx~N( 8
(18)
where
2i vārex vI a.Cats(Zp: 2g), ex ~ N (R. 2p E x Ev.e(p).
(19)
During the inference phase, the pre-trained well perl-performed try-on model Mÿ is used to generate virtual try-on results.
14

---

Algorithm 1: Training and Inference Procedures
Input: Dataset D reals pre-trained T21 model, pre-trained parser S, pre-trained model M Output: Trained model Mg
Training Procedure:
2 repeat
Obtain the input sample:
(g. gun p) ~ Dreat // Target clothing image, random clothing image, and reference person image from the dataset
Obtain the segmentation masks:
Magn Dreal // inpainting mask from the dataset
Mide ~ Dreal // Identity layers (e.g., head, hands, feet) from the
dataset
Generate the background-replaced person image:
MRB + 1-1 (magn mide 3 magg)) )): Compute the residual background mask.
10
B T2I(c, c); // Generate random background image using
pre-trained T2I model.
+ mRn @ p (1-mRR) @ B;// Replace background.
12
Check the size of the clothing:
13
m, +- S(p);; Generate
human parsing map of the person image using pre-trained parser 5.
7 [1, A(m m menck) and A(m. m(") , Check if the (0, otherwise.
clothing can fully cover the original one.
Compute the pseudo reference image:
16
Pun + MM gnn)) Generate the pseudo reference image using the teacher model.
Perform the de-occlusion task:
18
if 1 then
19
TeLDx(MM(P.o p"); Minimize loss between the generated image and the pseudo reference image.
end 20
Correct distribution shift:
22
p Uniform // Generate random probability value.
p < y then
24
V (M g). // Minimize the loss between the generated image and the real image.
end
until converged;
Inference Procedure:
Input: Target clothing image gm. reference person image
Output: Virtual try-on result Ptr
Generate the virtual try-on result:
29
Ptry < MM // Generate the virtual try-on result.
return Ptry
C Technical Appendices
C.1 Improve Traditional Mask-Free Method
For traditional mask-free methods, such as WUTON [1], PF-AFN [2], and LDE-VTON [16], solving inherent occlusions can be achieved with just a few simple steps. First, we generate teacher knowledge through a tre-trained well-performed teacher (inpainting) model M
t tun MA (Magns Paga: gun: r).
(20)
15

---

Then, we use MAB to perform background pre-replacement on tun?
tun MRB ④ B MRB) ④ tun-
(21)
Finally, tyn is paired with p', where p' can be used as the input, while top serves as the pseudo reference image. The distillation process is then carried out by training student network Me:
arg min
Caist (p("
(22)
Alternatieely, tim can be used as the input, while p* is used as the ground truth to train the student network Mp:
arg min Caist (D', (
(23)
C.2 Size Comparison for Other Types of Clothing
To ensure that only gun with a size absolutely larger than participates the training of the stage represented by Eq. (7), in addition the execution process for upper clothing described in the main text, the following are the execution processes for the remaining two types of clothing.
Lower Clothing. For lower clothing, when the leg size of Pun is smaller than those of p', we 1. In this case, gun can fully cover the original lower clothing of p', expressed as Eq. (24):
- { 0, otherwise,
1, A mieg) 20,
(24)
where mieg E {0, H HxW represents the semantic layer of the legs. For the person identity mask Mide, we obtained the layers "hair." "shoes." "hat," "sunglasses," "scarf," "bag," "head," "upper "upper_clothess clothes," and "arms" from human parsing as the sub-layers that compose mide. The code is represented as:
iddee sparse_array
label_ Jabel__app"hair") astype (np. float32)
(parse_array label_map
(parse_array -- ("ai__map""ett__shhoe""" astype (np float32)
("tp_appprrhhhhhhheee"ll astype (np. float32) +
(parse_array == label_map ast=""))
(parse_array label_mas ("annaaasssss...aaaaoe... (1ooaa333)
(parse_array label_map ("ccart"").eeeeyoocc..ooaaata))" float32)
6
7
(parse_array == label_map
(parse_array == label_may ("headd)).asssppoodpffff.e
(parse_array label_map (""gpercclcttee") (fp.att33))))) float32)
(parse_array label_man ["left_arm"]] astype www.pe
11
(parse_array label_man ["right_arm (nt.
For dresses, when the arm, neck, and leg sizes of Pun are smaller than those of p", we set 7 In this case, gun can fully cover the original dresses of p*, expressed as Eq. (25):
Dresses.
{ 1, if A(m m 'ockk) and A(m m and A(m ming) 2 0,
(25)
0, otherwise.
" "hat," " "sunglasses, "scarf," " "bag," and "head," from human parsing the sub-layers that compose mide. The code represented as:
For dide- we obtained the layers "hair," "shoes,"
m_ ide (parse_arte _arra
label. map ["aair") astype (np.ffaat12)) (parse_array == label_map ("left_shoe"]) astype (np float32)
astype (parse_array label_map ["hat"]) alat"")).astyppepnn..loo.
(parse_array = label_map ["right_shoe"" (p float32) +
(parse_array label_map ["sunglases" astype (np.foat33)) float32)
(parse_array label_map ["taart"").atyyyyooo..oot
+
(parse_array -- label_map ["bag"]) astype
(parse_array -lbellmap [ ["head"]) astype
16

---

Upper Clothing, For Mdde. we obtained the layers "hair," "shoes," "hat," "sunglasses. "scarf," "bag." "head," " "skirt." "pants, "dress" "belt" and "legs" from human parsing as the sub-layers that compose mide. The code is represented as:
idide m_ide + mdide of dresses
(parse_array
label_map ["skirt"]) astype float32)
(parse_array label_may map ["pants"]] .a)pastypeeeeee inp.floate
(parse_array = label_map ''dres"]) astype .sttyyp (np.
+
(parse_array = label_mas ["belt"]] "bbeltee).asttppeCC astype(np) (ng.faattp. float32)
(parse_array label_map ["left_leg"])...... astype (wr. float32)
(parse_array label_map ["right_leg""] astype (np float22)
C.3 How to obtain B in Eq. (4)?
The background map B be designed to be random images generated by pre-trained well-performed T2I (Text-to-lmage) model [23, 24]: B = (2I c), where с is the prompt and e random Gaussian noise. In implementation, T2I utilizes the pre-trained SD1.5 implemented by the diffusers library in Huggingface". By simply inputting prompt с related to any scene, the desired scene image B can be obtained.
Alternatively, B can be designed as random single value matrix: B = random(0, 1) E 3 This approach is simpler and more practical because only require the model to focus the human body in the image, without needing the model to understand what the background in the image is. Its code is implemented as follows:
mRB
m_ide (1-agnostic_mask) torch.. rand (1) A random floating -point number between 0 1 image (1 mRB) * image + mRE B
(agnostic_mase
The reason for choosing a random floating-point number between and 1 is that the normalized human image's original background color values generally range from 0 to 1, which allows B to better adapt to real sample distributions.
D More Results
We first supplement some quantitative results of classic methods on the VITON dataset [20] in Tab. 3, as shown in Table 5. In addition, we qualitatively compare our proposed method with several state-of-the-art (SOTA) methods.
Table 5: Supplementary quantitative results of early virtual try-on methods on the VITON dataset in Tab. 3. "Mask-Free denotes whether the mask and structural representation are used during inference. Bold denotes the best result.
Underline represents second best.
D.1 Results of VITON-HI HD
Methods
Publication
Mask-Free
SSIM,
FID.,,
CP-YTON [40] VTNEP
BOUV'18
X 0.72
24.45
We conducted qualitative comparisons between our method and baseline methods on the VITON-HD dataset [21], with resolution of 512 x 384. Figs. and illustrate the vi- sual comparison of try-on results between our method, DCI-VTON [5], StableVITON [7], IDM-VTON [28], StableGarment [27), LDE- VTTNN [16], and CatVTON [9]. It is evident that our method can handle occlusion problem
ICCV19
X 0.80
n/a
[22]
CP-YTON+ [43]
CVPRW'20
X
0.75
21.04
SleveNet [44]
WACV
0.77
n/n
ACGPN
CVPR'20
0.84
IM-YTON [46]
AAAU21
DCTON [47]
CVPR'21
0.83
14,82
ZFlow (48)
ICCV"21
0.88
15.17
Ours
This Work
0.91
9.23
official data insosesible.
of higher-resolution images in different methods, resulting in more realistic try-on results.
D.2 Results DressCode
Furthermore, we have also included additional visual comparisons between our method and baseline methods (IO-VTON [28], StableGarment [27], and CatVTON [9]) on DressCode dataset [22] to
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Person
Clothing
Ours
Person
Clothing
Ours
DCI-VTON
st.VITON
St.Garment
DCI-VTON
St.VITON
St.Garment
LDE-VTON
IDM-VTON
CatVTON
LDE-VTON
IDM-VTON
CatVTON
Person
Clothing
Ours
Person
Clothing
Ours
DCI-VTON
St.VITON
St.Garment
DCI-VTON
St.VITON
St.Garment
Willfoo
LDE-VTON
IDM-VTON
CatVTON
LDE-VTON
IDM-VTON
CatVTON
Figure 7: Qualitative results on the VITON-HD dataset. The baseline methods consist of six SOTA iefuuiio-bbsed methods. Red dashed boxes highlight the limitations of each method.
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Person
Clothing
Mask
Person
Clothing
Mask
St.VITON
St.mentt
LDE-VTON
St.VITON
St. Strment
LDE-VTON
IDM-VTON
CatVTON
Ours
IDM-VTON
CatVTON
Ours
Person
Clothing
Mask
Person
Clothing
Mask
St.VITON
St.Garment
LDE-VTON
St.VITON
St.Garment
LDE-VTON
IDM-VTON
CatVTON
Ours
IDM-VTON
CatVTON
Ours
Figure 8: Qualitative results on the VITON-HD dataset. The baseline methods consist of five SOTA diffusion- based methods. Red dashed boxes highlight the limitations of each method.
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Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Figure 9: Qualitative results on the DressCode dataset (Dresses). The baseline methods consist of three SOTA diffusion-basee methods. Red dashed boxes highlight the limitations of each method.
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Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Gartent
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Figure 10: Qualitative results on the DressCode dataset (Upper). The baseline methods consist of three SOTA diffusion-basse methods. Red dashed boxes highlight the limitations of each method.
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Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.Garment
IDM-VTON
CatVTON
Ours
Person
Clothing
St.mmntent
IDM-VTON
CatVTON
Ours
Figure 11: Qualitative results the DressCode dataset (Lower). The bascline methods consist of three SOTA diffusion-base methods. Red dashed boxes highlight the limitations of each method.
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Figure 12: Qualitative results in in-the-wild scenarios.
demonstrate its superionity, as shown in Figs. (Dresses), (Upper Clothing), and 11 (Lower Clothing).
D.3 In-the-Wild Results
To validate the robustness and generalization the model in real-world, open-domain scenarios, we tested a set of in-the-wild samples. In the actual implementation, we migrated our de-occlusion architecture to [8] to leverage the powerful garment-image understanding capability of ReferenceNet. After fine-tuning, the visualized results are shown in Fig. 12.
E
Limitation and Future Work
Despite significant advancements in addressing occlusion is- for virtual try-on, this work still faces limitations, par- ticularly regarding dataset quality. Current datasets, whether synthetic real, may fail to capture the full range of human appearances, clothing styles, and occlusion scenarios, thus limiting model performance. Furthermore, our method may yield failed results when encountering clothing shapes that are objectively unlearnable, as shown in Fig. 13. In the currently available datasets, the lower body area indicated by the red dashed box in the swimsuit covered by pants after trying However, in real life, people generally do not wear other pants or skirts when wearing this kind of swimsuit. Due the inconsistency between this ground truth and real-life situations, biases have been introduced in the try-on process for different clothing samples. The model cannot learn synthesize this area, resulting in failed try-on of the lower body area. However, this limitation is due the lack of diversity in the dataset, rather than a flaw the framework we designed. We believe that this issue can be resolved in the future by using much larger number of training samples. Future work should focus on enriching dataset diversity and mitigating biases to enhance model robustness and applicability.
Person
Clothing
Result
Figure 13: Example of a failed vir- tual try-on of our method.
Discussion of Societal Impacts
The societal impacts of virtual try-on technology multifaceted, offering significant benefits in terms of enhanced shopping experiences, fashion innovation, and environmental sustainability. However, these advancements also bring challenges related to privacy, digital divide, ethical considerations, and economic disruption. Addressing these challenges requires collaborative effort from technologists, policymakers, and stakeholders across the retail and fashion industries ensure that the benefits of virtual try-on technology are realized equitably and responsibly.
By engaging in ongoing discussions and implementing appropriate measures, harness the potential of virtual try-on technology to create a more inclusive, convenient, and sustainable future for fashion and retail.
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Reviews:
[{"review_id": "MnJupoqPAn", "reviewer": "Reviewer_yELi", "summary": "This is a virtual try-on paper focusing on scenarios where occlusion appears in images. More specifically, the authors group the occlusion-related problems into two main categories: (1) inherent, and (2) acquired. Main contributions are: (1) the analysis and formulation of the occlusion-related problems, (2) novel pre-replacement operation to prevent inherent occlusions, (3) novel covering-and-eliminating procedure to prevent acquired occlusions, (4) comparisons against many other methods (useful as a benchmark).", "questions": "I think that showing and discussing limitations even further in this work would be useful for the community. The paper already identifies and suggests potential solutions to two interesting limitations. Can the authors include more qualitative results that depict the failures of the proposed method (showing a larger variety of clothing, body shapes, and poses)?\n\nIt seems that in Figure 5 (GANs), there are some artifacts in the results. Can the authors explain why? Is there a significant difference in the results for GANs as compared to diffusion models?\n\nCan the authors introduce some qualitative results with more complex backgrounds?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe paper is intuitive and provides insights into where the current methods are failing and provides potential solutions to those problems.\n\nThe method produces most detailed results (according to the perceptual error (LPIPS)) in Table 2 and maintains high ranking on other metrics in comparison with many other methods (useful as a benchmark).\n\n\nWeaknesses:\n\nQuality of the results: In Figure 5 there are still seams/mismatches in the “GANs + Ours” column.\n\nData: only standard fashion datasets were used, limiting the variety of clothing, body shapes, and poses.\n\nThere are no examples of results with more complicated backgrounds even though the model technically should be able to handle such examples due to the background augmentations that are used. Also see Line 305: “The results in Fig. 6 show that our method can better handle person images with diverse background styles” -> Fig. 6 contains examples with mostly white backgrounds only.\n\nThe paper does not contain many examples of failure.\n\nThere are some typos, for example: Line 165 aim -> aims.", "was_revised": true, "final_justification": "After reading the other reviews and the rebuttal, I decided to keep my Accept score. My score could have been higher if the paper had provided a more systematic overview of the limitations of existing methods, including its own (and comparisons on such extreme cases). I believe papers that clearly expose such limitations can have a greater impact, and this work moves in that direction, which I found both interesting and useful.", "rebuttal": "We appreciate all your valuable suggestions and constructive comments.\n\n> **W1**: Quality of the results: In Figure 5 there are still seams/mismatches in the \"GANs + Ours\" column.  **And Q2**: It seems that in Figure 5 (GANs), there are some artifacts in the results. Can the authors explain why? Is there a significant difference in the results for GANs as compared to diffusion models?\n>> **R1**: As shown in Figure 5,  there are some artifacts observed in the experimental results of both \"GANs\" and \"GANs+Ours\". But when we apply our method to a diffusion-based backbone (SD 1.5 used in this work), we can find that these artifacts are eliminated (see our results in Figure 3 and 4). So we believe that such problem lies in the relatively weaker generative capabilities of the GANs backbone compared to diffusion models. The adversarial training process of GANs is usually unstable and may introduce those undesirable artifacts into the final results. In addition, the quantitative comparisons in Table 2 and 3 also demontrate that the diffusion-based methods consistently achieve the best and second-best results across all evaluation metrics, outperforming the GANs-based benchmarks. Therefore, there is a significant difference in results between diffusion models and GANs.\n\n> **W2**: Data: only standard fashion datasets were used, limiting the variety of clothing, body shapes, and poses.\n>> **R2**: To ensure a fair comparison with existing methods, we employ the same datasets as them for our performance evaluation. The DressCode dataset, in particular, comprises 53,792 garment images spanning three categories—upper body, lower body, and dresses—and an equal number of human images featuring diverse skin tones, body shapes, and poses. Therefore, achieving state-of-the-art results in both quantitative and qualitative experiments on this dataset, especially in handling occlusions, is strong evidence of our method’s effectiveness and its advantages over existing approaches.\n\n> **W3**: There are no examples of results with more complicated backgrounds even though the model technically should be able to handle such examples due to the background augmentations that are used. Also see Line 305: “The results in Fig. 6 show that our method can better handle person images with diverse background styles” -> Fig. 6 contains examples with mostly white backgrounds only. **And Q3**: Can the authors introduce some qualitative results with more complex backgrounds?\n>> **R3**: In the first and last two rows of Figure 10 and the last two rows in Figure 11, we present some qualitative results with more complex backgrounds rather than plain colors. We can see that our method can handle these background styles well. Moreover as suggested, in the revised version of this paper, we will show more results on these backgrounds and introduce other results under in-the-wild scenario in Figure 6 to support our claim.\n\n> **W4**: The paper does not contain many examples of failure. **And Q1**: I think that showing and discussing limitations even further in this work would be useful for the community. The paper already identifies and suggests potential solutions to two interesting limitations. Can the authors include more qualitative results that depict the failures of the proposed method (showing a larger variety of clothing, body shapes, and poses)?\n>> **R4**:  We have already discussed the limitations and future work in Section F of our Appendix and have displayed a failure case in Figure 12. Nevertheless, as suggested, we will add more examples of failure in our revised version to better illustrate the current limitations of the proposed method and guide our future work.\n\n> **W5**: There are some typos, for example: Line 165 aim -> aims?\n>> **R5**:  Thanks for pointing out this. We have carefully checked our paper and will correct all such typos (including L165: aim -> aims) in the revised version."}, {"review_id": "Vr8ikIvAL8", "reviewer": "Reviewer_1L7U", "summary": "This paper introduces a mask-free VTON framework, which includes background pre-replacement operation and covering-and-eliminating operation, to address the inherent and acquired occlusion problems in existing VTON methods. The background pre-replacement operation mitigates inherent occlusions by preventing the model from confusing target clothing information with the human body or image background. The covering-and-eliminating operation enhances the model’s ability to understand and model human semantic structures, thereby reducing acquired occlusions.", "questions": "1. From the visualizations in Fig.3 and in supplementary materials, I noticed that there exists common distortion in human models' face area, especifically around the eyes. In addition, the generated image resolution of the proposed method seems to be lower than comparison baselines. Do authors notice these issues, and what do you think might be the cause?\n\n2. From the illustration of Background Pre-Replacement process in Figure 13, I notice that there still exists inherent occlusion issue, where the mask for right arm is not accurate. This leads to another question regarding the background pre-replacement process. It essentially depends on the quality of the segmentation mask, so how can it eliminate the inherent occlusion issue brought out by inaccurate masking?", "limitations": "Please refer to previous sections.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths of this paper include:\n1. The motivation of the proposed method is pretty reasonable, as it deals with the major concerns of current VTON frameworks, which are inaccurate masking that missegments the human body, background, and clothing regions, as well as inaccurate representations that leads to erroneous human structure parsing.\n2. The writing of this paper is well organized. It first introduces the two types of occlusions in detail, then proposes the method that addresses these two types of occlusions respectively.\n3. Extensive quantitaive and qualitative experiments validate the effectiveness and generalization ability of the proposed method.\n\nPlease refer to next section for questions.", "was_revised": true, "final_justification": "Thanks the authors for the response. Some of my concerns have been addressed. Although I'm not so familiar with VTON area, this work provides an interesting and effective framework to resolve two critical occlusions, and provides promising results. I'll tend to keep my scores.", "rebuttal": "We appreciate all your valuable suggestions and constructive comments.\n\n> **Q1:**  From the visualizations in Fig.3 and in supplementary materials, I noticed that there exists common distortion in human models' face area, especifically around the eyes. In addition, the generated image resolution of the proposed method seems to be lower than comparison baselines. Do authors notice these issues, and what do you think might be the cause?\n>> **R1:** Yes, we have noticed these issues. We discuss the causes of them as follows:\n>>\n>> **(1)** The reason of human face distortion is because that encoding and decoding human images with a pre-trained KL-regularized autoencoder (e.g., VQGAN) in latent diffusion models will inevitably discard a certain amount of face details (especially around the eyes) [1]. Most existing virtual try-on methods often mitigate such information loss by performing the following post-processing step, which adopts an inpainting mask to paste the human identity area from the original image onto the same region of the generated result.\n>>$$p_{result} = m_{agn} \\odot p + (1 - m_{agn}) \\odot p_{un}$$\n>>However, since the inpainting mask used above may also be imprecise and introduce some new occlusions, we abandon this post-processing step in our proposed framework. Prompted by your comment, we will incorporate optimization of the latent diffusion model generation quality [1] in our future work to achieve further improvements.\n>>\n>> **(2)** The lower image resolution is mainly caused by the fewer inference steps we adopt. In order to speed up the visualization process of the qualitative results of our method, we only run 20 denoising steps during inference while other comparison methods employ 50 denoising steps. If more inference steps are used, our method can generate better results with higher resolution.\n>>\n>> &nbsp;\n>>\n>>[1] Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models, CVPR 2025.\n\n> **Q2:**  From the illustration of Background Pre-Replacement process in Figure 13, I notice that there still exists inherent occlusion issue, where the mask for right arm is not accurate. This leads to another question regarding the background pre-replacement process. It essentially depends on the quality of the segmentation mask, so how can it eliminate the inherent occlusion issue brought out by inaccurate masking?\n>> **R2:** This may be a misunderstanding. Our background pre-replacement operation is designed to address the problem of inaccurate segmentation masks. Take Figure 13 as an example, if without the background pre-replacement operation, the clothing in the unmasked areas of the right arm will be incorrectly preserved in the pseudo reference image. In the correct situation, such clothing information should be generated by the model, but now they can be simply maintained like the human body or background because they appear in both pseudo reference image and ground-truth image. Therefore, training with these samples can establish incorrect associations between the target clothing information and the human body or background pixels. In contrast, by using our background pre-replacement operation, we replace the inaccurate unmasked clothing areas in both pseudo reference image and ground-truth image with the same pure background or random scene image. By doing so, those areas will only contain the background information that should be maintained during the generation process, instead of target clothing pixels (as illustrated in Figure 2). Therefore, the clothing information in other accurately masked areas that need to be generated can be more effectively differentiated from the background, thus leading to better generation results."}, {"review_id": "mwiZbFcgtk", "reviewer": "Reviewer_5r1a", "summary": "This paper presents a new training pipeline for virtual try-on (VTON) tasks. The pipeline involves multiple complicated stages that trying to find the most appropriate supervision signals for training a generative model while addressing Inherent-Occlusion and Acquired-Occlusion. In experiments, the proposed method was evaluated on 3 datasets against a total of 21 baselines.", "questions": "1. It seems that the mask-free approach serves as an additional image refinement step built upon the inpainted results from mask-based methods. Similarly, the proposed approach appears to be an extra refinement step based on a pre-trained mask-free model. Is this understanding correct? If so, direct comparisons with mask-based methods may not be strictly fair, given that those methods involve little to no refinement.\n2. Why did the authors use SD1.5 instead of SDXL as the base generative model? BooW-VTON has shown that SDXL produces better results, so it is unclear why SDXL was not used.\n3. In Section 4.2, Equation 10, the gating parameter $\\gamma$ is determined solely by the sizes of the neck and arms. What is the rationale behind using only these two body parts?\n4. The authors claim that their method generalizes to in-the-wild scenes. However, according to Section 5.2, the model was trained on the VITON-HD dataset and tested on the DressCode dataset, which is not strictly in-the-wild evaluation. I suggest including additional experiments on real-world images of humans and clothing sourced from the internet.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The presentation of the paper is pretty good. The problems are clearly defined, and the figures and tables are mostly clear and straightforward.\n2. The experiments are comprehensive and solid. The model outperforms other methods across multiple benchmarks.\n3. The appendix is thorough, with Algorithm 1 being particularly clear and helpful.\n\nWeaknesses:\n1. The technical contribution is incremental. While the proposed approach for identifying a more reliable training objective is valuable, it appears to be more of an engineering effort, as the optimization target remains training or fine-tuning a generative model. Moreover, the problems of Inherent Occlusion and Acquired Occlusion have already been defined in [16], which further diminishes the novelty of this work.\n2. Although cloth/body occlusions are greatly improved based on the figures and supplementary materials, the generation quality of human faces and limbs is significantly worse than that of the baseline methods. The generated faces are often distorted and blurred. Is there a specific explanation for this?\n\nSee \"questions\" below for further doubts and minor weakness.", "was_revised": true, "final_justification": "Thanks authors for the rebuttal. Some of my concerns have been addressed. While I am still concerned about the novelty&complexity of the method and the moderate visual results demonstrated, I think the paper with modifications based on rebuttals is ready to be present at NeurIPS.", "rebuttal": "We appreciate all your valuable suggestions and constructive comments.\n\n>**W1**: The technical contribution is incremental. While the proposed approach for identifying a more reliable training objective is valuable, it appears to be more of an engineering effort, as the optimization target remains training or fine-tuning a generative model. Moreover, the problems of Inherent Occlusion and Acquired Occlusion have already been defined in [16], which further diminishes the novelty of this work.\n>>**RW1**: We cannot agree with your comment that “The technical contribution is incremental” based on the reasoning that “training or fine-tuning a generative model appears to be more of an engineering effort”. As we know, “pre-training and fine-tuning” has already been a widely used paradigm to generalize the powerful foundation model that per-trained on large-scale datasets to the downstream tasks, such as virtual try-on. Since most downstream tasks have their own characteristics, designing a novel and effective fine-tuning strategy to address the problems arising in the generalization process is definitely a strong technical contribution. Moreover, although the problems of Inherent Occlusion and Acquired Occlusion have already been defined in [16], our work provides a more insightful analysis on the causes of these two types of occlusions (see Section 3). And after that, we designed a targeted solution based on the analysis, which is simple-yet-effective for addressing the occlusion problems in (mask-free) virtual try-on. Therefore, we believe that our proposed method has sufficient novelty.\n\n>**W2**: Although cloth/body occlusions are greatly improved based on the figures and supplementary materials, the generation quality of human faces and limbs is significantly worse than that of the baseline methods. The generated faces are often distorted and blurred. Is there a specific explanation for this?\n>>**RW2**: This is mainly due to the limitations of latent diffusion models. Encoding and decoding human images with a pre-trained KL-regularized autoencoder (e.g., VQGAN) in latent diffusion models will inevitably discard a certain amount of face details (especially around the eyes) [1]. Most existing virtual try-on methods often mitigate such information loss by performing the following post-processing step, which adopts an inpainting mask to paste the human identity area from the original image onto the same region of the generated result.\n>>\n>>$$p_{result} = m_{agn} \\odot p + (1 - m_{agn}) \\odot p_{un}$$\n>>However, since the inpainting mask used above may also be imprecise and introduce some new occlusions, we abandon this post-processing step in our proposed framework. Prompted by your comment, we will incorporate optimization of the latent diffusion model generation quality [1] in our future work to achieve further improvements.\n>>\n>> &nbsp;\n>>\n>>[1] Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models, CVPR 2025.\n\n>**Q1**: It seems that the mask-free approach serves as an additional image refinement step built upon the inpainted results from mask-based methods. Similarly, the proposed approach appears to be an extra refinement step based on a pre-trained mask-free model. Is this understanding correct? If so, direct comparisons with mask-based methods may not be strictly fair, given that those methods involve little to no refinement.\n>>**RQ1**:  This is a misunderstanding. The mask-free and mask-based strategies are two different paradigms to address the unpaired data problem, and the greatest difference between them is that the mask-free methods **DO NOT** require to generate inpainting masks in the inference phase. To eliminating such requirement, the mask-free methods can synthesize pseudo reference images from a **pre-trained generative model (SD1.5 used in our implementation) or a mask-based method**.  So the mask-free approach is not an additional image refinement step for mask-based methods. In addition, our proposed method is built by **fine-tuning the SD1.5 diffusion model from scratch not on another pre-trained mask-free model**, so it does not serve as an extra refinement step. As shown in Figure 2, similar to other mask-free methods, our approach first synthesizes and selects the pseudo reference images and their corresponding occlusion-augmented person images. During this process, our designed background pre-replacement and covering-and-eliminating operations are employed to deal with inherent and acquired occlusions, respectively. After that, we fine-tune the SD1.5 diffusion model, which is a widely-used foundation model for virtual try-on tasks, by using the selected paired samples. According to the above explanation, since both mask-free and mask-based methods start from the same pre-trained diffusion model, and mask-free methods (including our approach) even do not utilize the inpainting mask information during the inference phase, we believe that the comparisons between our method and mask-based methods are sufficiently fair. \n\n>**Q2**: Why did the authors use SD1.5 instead of SDXL as the base generative model? BooW-VTON has shown that SDXL produces better results, so it is unclear why SDXL was not used.\n>>**RQ2**: To make fair comparison with existing virtual try-on benchmarks, we adopt their commonly used SD1.5 backbone. And moreover, due to the limited computational resources of our laboratory, it is infeasible to conduct experiments using SDXL.  Based on the above reasons, we use SD1.5 instead of SDXL as our base generative model. Nevertheless, the qualitative results in Figures 3 and 4 and the quantitative results in Table 2 demonstrate that, even with the relatively weaker SD1.5 backbone, our method outperforms SDXL-based counterparts such as BooW-VTON and IDM-VTON on several metrics. This provides sufficient evidence for the effectiveness of our approach.\n\n>**Q3**: In Section 4.2, Equation 10, the gating parameter $\\gamma$ is determined solely by the sizes of the neck and arms. What is the rationale behind using only these two body parts?\n>>**RQ3**: We have explained the reason for using these two body parts in Lines 198–202 of our paper. The gating parameter $\\gamma$ is used to indicate whether the garment $g_{un}$ fully covers $g$ or not. For the upper-body clothing, the body areas that are most likely to expose skin are the arms and neck. Therefore, we use these two body parts to generate $\\gamma$ so as to determine the coverage of different garments.\n\n>**Q4**: The authors claim that their method generalizes to in-the-wild scenes. However, according to Section 5.2, the model was trained on the VITON-HD dataset and tested on the DressCode dataset, which is not strictly in-the-wild evaluation. I suggest including additional experiments on real-world images of humans and clothing sourced from the internet.\n>>**RQ4**: Considering the background augmentations used in our background pre-replacement operation, the proposed method should technically be able to generalize to in-the-wild images. In the first and last two rows of Figure 10 and the last two rows in Figure 11, we present some qualitative results with more practical backgrounds rather than plain colors. And moreover, the right block of Figure 8 also displays some results of heavier persons with sideways poses. Nevertheless, as suggested, we will add more results under real-world in-the-wild scenarios in our revised version to further validate the generalization ability of our method."}, {"review_id": "OBkIcUHfKB", "reviewer": "Reviewer_ciwU", "summary": "This work introduces a mask-free VTON framework that tackles occlusions through two key operations: background pre-replacement to reduce inherent confusion between clothing and background, and covering-and-eliminating to address acquired occlusions by enhancing semantic understanding. The method is generalizable and compatible with generative models like GANs and diffusion models.", "questions": "1) Do g and g_un both refer to upper cloth？\n2) It appears that the identity of the subject changes noticeably after applying the target clothing. What might be the underlying cause of this issue? Have the authors considered any strategies to preserve identity more effectively during the try-on process?\n3) Does the proposed method generalize well to in-the-wild images, particularly for subjects with greater variation in body shape, such as individuals with heavier or more muscular physiques?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1) The paper provides an effective solution to the primary occlusion issues commonly encountered in virtual try-on approaches, addressing both inherent and acquired occlusions\n2) The method is extensively compared with state-of-the-art methods, clearly demonstrating its effectiveness and generalizability.\n\n\nweaknesses:\n1) The methodological descriptions and accompanying notations in the paper are a bit hard to follow. In particular, the explanation of the background pre-replacement strategy could be improved by supplementing the mathematical formulation with a clear, step-by-step visual illustration. Such a figure would enhance clarity and accessibility, as the current presentation may hinder comprehension.\n\n2) The qualitative results are primarily demonstrated using images from fashion datasets or under controlled studio photography settings. It would be more meaningful to evaluate the proposed method on in-the-wild data to better assess its robustness and generalization to real-world scenarios.", "was_revised": true, "final_justification": "Overall this is a nice work which advanced the virtual try on framework. The author has referred to existing figure for virtual try on results which are closed to in-the-wild cases. Although the effectiveness can be demonstrated to some extend, there is still a domain gap between fashion image taken in studio with set camera and daily image taken with random lenses, thus a more comprehensive evaluation on in the wild cases would be better. Although the author has explain why they did not include the inpainting method to address the identity changing issue, leaving this problem unsolved is still considered a limitation for the proposed work. I will keep my score unchanged.", "rebuttal": "We appreciate all your valuable suggestions and constructive comments.\n\n> **W1**: The methodological descriptions and accompanying notations in the paper are a bit hard to follow. In particular, the explanation of the background pre-replacement strategy could be improved by supplementing the mathematical formulation with a clear, step-by-step visual illustration. Such a figure would enhance clarity and accessibility, as the current presentation may hinder comprehension.\n>> **R1**: We appreciate your valuable suggestions, they are very helpful in improving our paper. In the revised version of this paper, we will follow your comments to add the visual illustrations to supplement the mathematical formulation of each step in our designed background pre-replacement strategy, so as to enhance the clarity and accessibility of such operation.\n\n> **W2**: The qualitative results are primarily demonstrated using images from fashion datasets or under controlled studio photography settings. It would be more meaningful to evaluate the proposed method on in-the-wild data to better assess its robustness and generalization to real-world scenarios. **And Q3**: Does the proposed method generalize well to in-the-wild images, particularly for subjects with greater variation in body shape, such as individuals with heavier or more muscular physiques?\n>> **R2**: Due to the background augmentations used in the background pre-replacement operation, our method should technically be able to generalize to real-world in-the-wild images. In the first and last two rows of Figure 10 and the last two rows in Figure 11, we present some qualitative results with more practical backgrounds rather than plain colors. And moreover, the right block of Figure 8 also displays some results of heavier persons with sideways poses. Nevertheless, as suggested, we will add more results under in-the-wild scenarios in our revised version to further validate the generalization ability of our method.\n\n> **Q1**: Do g and g_un both refer to upper cloth？\n>> **RQ1**: Actually, $g$ and $g_{un}$ indicate different garments from the same cloth category (e.g., lower body, upper body, or dresses). Specifically, $g$ denotes the garment that matches the one worn by the person in image $p$, forming a paired sample, whereas $g_{un}$ is a garment randomly sampled from the dataset.\n\n> **Q2**: It appears that the identity of the subject changes noticeably after applying the target clothing. What might be the underlying cause of this issue? Have the authors considered any strategies to preserve identity more effectively during the try-on process?\n>> **RQ2**:  This is mainly due to the limitations of latent diffusion models. Encoding and decoding human images with a pre-trained KL-regularized autoencoder (e.g., VQGAN) in latent diffusion models will inevitably discard a certain amount of face details (especially around the eyes) [1]. Most existing virtual try-on methods often mitigate such information loss by performing the following post-processing step, which adopts an inpainting mask to paste the human identity area from the original image onto the same region of the generated result.\n>>\n>>$$p_{result} = m_{agn} \\odot p + (1 - m_{agn}) \\odot p_{un}$$\n>>However, since the inpainting mask used above may also be imprecise and introduce some new occlusions, we abandon this post-processing step in our proposed framework. Prompted by your comment, we will incorporate optimization of the latent diffusion model generation quality [1] in our future work to achieve further improvements.\n>>\n>> &nbsp;\n>>\n>>[1] Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models, CVPR 2025."}]

Author rebuttal:

Meta-review: This paper presents a valuable contribution to the virtual try-on field by explicitly addressing the underexplored but critical challenge of occlusions. The work is commendable for its systematic approach to the problem, clearly categorizing occlusion types into "inherent" and "acquired," which provides a useful conceptual framework for future research.

The proposed technical solutions are well-motivated by the identified limitations of current methods. The novel pre-replacement operation for inherent occlusions and the covering-and-eliminating procedure for acquired occlusions represent a logical and intuitive approach to mitigating these distinct issues. The experimental results are persuasive, demonstrating that the method achieves state-of-the-art performance on key perceptual metrics like LPIPS while maintaining strong performance on other standard benchmarks. The extensive comparisons against numerous other methods add significant value to the paper as a useful benchmark for the community.

While the paper has weaknesses—such as residual artifacts in some results, the use of standard datasets limiting variety, and a lack of highly complex background examples—these do not outweigh its core contributions. The identified limitations are common in the field and are often addressed in subsequent work. The paper's foundation is solid, and its novel focus on occlusion provides a clear pathway for advancement. The issues mentioned, such as dataset scope and minor typos, can be readily addressed in a final camera-ready version.

In conclusion, this paper introduces a timely and insightful approach to a key problem in virtual try-on. The proposed method is effective, the experiments are thorough, and the conceptual framing of occlusions is itself a contribution. The strengths significantly outweigh the weaknesses, making this paper a strong candidate for acceptance.
Decision: Accept (poster)