Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On
Authors: Siqi Wan
Jingwen Chen
Yingwei Pan
Ting Yao
Tao Mei
Venue: ICLR 2025 Poster (2025)
Keywords: Virtual Try-On
Image Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Diffusion models have shown preliminary success in virtual try-on (VTON) task. The typical dual-branch architecture comprises two UNets for implicit garment deformation and synthesized image generation respectively, and has emerged as the recipe for VTON task. Nevertheless, the problem remains challenging to preserve the shape and every detail of the given garment due to the intrinsic stochasticity of diffusion model. To alleviate this issue, we novelly propose to explicitly capitalize on visual correspondence as the prior to tame diffusion process instead of simply feeding the whole garment into UNet as the appearance reference. Specifically, we interpret the fine-grained appearance and texture details as a set of structured semantic points, and match the semantic points rooted in garment to the ones over target person through local flow warping. Such 2D points are then augmented into 3D-aware cues with depth/normal map of target person. The correspondence mimics the way of putting clothing on human body and the 3D-aware cues act as semantic point matching to supervise diffusion model training. A point-focused diffusion loss is further devised to fully take the advantage of semantic point matching. Extensive experiments demonstrate strong garment detail preservation of our approach, evidenced by state-of-the-art VTON performances on both VITON-HD and DressCode datasets. Code is publicly available at: https://github.com/HiDream-ai/SPM-Diff.

Published as a conference paper at ICLR 2025
INCORPORATING VISUAL CORRESPONDENCE INTO DIFFUSION MODEL FOR VIRTUAL TRY-ON
Siqi Wan JJingwwen Chen', Yingwei Pan?. Ting Yao?, Tao Mei?
1 University of Science and Technology of China, HiDream.ai wansiqi4789 @ mddu.ustt..du..n,
(ch.luussccuuuucc.ccccennnnnne {chenjingwen, pandy, tiyao, tmei @hidream.ai
ABSTRACT
Diffusion models have shown preliminary success in virtual try-on (VTON) task. The typical dual-branch architecture comprises two UNets for implicit garment deformation and synthesized image generation respectively, and has emerged as the recipe for VTON task. Nevertheless, the problem remains challenging to preserve the shape and every detail of the given garment due to the intrinsic stochasticity diffusion model. To alleviate this issue, we novelly propose to explicitly capitalize on visual correspondence as the prior to tame diffusion process instead of simply feeding the whole garment into UNet the appearance reference. Specifically, we interpret the fine-grained appearance and texture details as a set of structured semantic points, and match the semantic points rooted in garment to the over target person through local flow warping. Such 2D points are then augmented into 3D-aware cues with depth/normal map of larget person. The correspondence mimics the way of putting clothing on human body and the 3DD-awar cues act as semantic point matching to supervise diffusion model training. point-focused diffusion loss is further devised to fully take the advantage of semantic point match- ing. Extensive experiments demonstrate strong garment detail preservation our approach, evidenced by state-of-the-ae VTON performances both VITON-HD and DressCode datasets. Code is publicly available at: https::/gihuub..o//HHlle ai/SPM-Difi
INTRODUCTION
Virtual Try-ON (VTON) is an increasingly appealing direction in computer vision field, that aims to virtually drape the provided garment items onto target human models. The task empowers the end users to experience the visual affects of wearing various clothings without the need of physical store try-ons. That has great potential impact for revolutionizing the shopping experience within E-commerce industry. VTON can be regarded as one kind of conditional image synthesis [Song al. (2023); Yu al. (2023); Chen & Kae (2019): Cong et al. (2020): : [i et al. (2023a); ; [Chen et al.
(2024a bJ 2023a) with two constrains (i.e., the given in-shop garment and the target person image). However, the typical conditional image synthesis commonly tackles spatially aligned conditions like human pose sketch/edges Zhang et al.) (2023b); Mou et al.) (2024). In contrast, VTON signifies flexible change in shape of in-shop garment in real world, thereby being more challenging due to the complexities associated with the preservation of intrinsic clothing geometry and appearance (i.e., global garment shape and local texture details).
When generative networks become immensely popular, the early VTON techniques Choi et al. (2021 Lee et al. (002); Xie et al. (2023); Chopra et al. (202T): Dong et al. (2019b); Ge et (2021) consider capitalizing explicit warping deform in-shop garment according the pose of the target person, leading to spatially- condition of warped garment. These aligned conditions are further fed
into Generative Adversarial Networks (GAN) Goodfellow al. (2014): Karras et al. (2021 2019) 2020b a for person image generation. Nevertheless, it is found that such GAN-based methods (c.g.. GP- Xie al. (2023) )) show unrealistic artifacts especially when the garment texture is complex and the human pose is challenging as in Figure (b). This can be attributed to the warping errors of complex textures in garment and the limited generative capacity of GAN for synthesizing real- world
"This work performed HiDream.ai.

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Published as a conference paper at ICLR 2025
Target Person
In-shop Garment
GP-VTON
Stable-VTON
OOTDiffusion
SPM-Diff
(a)
(c)
(d)
(e)
Figure I: Illustration of given target person and (a) in-shape garment with semantic points. Existing GAN-based methods (e.g., (b) GP-NTON) and diffusion- based approaches (e.g., (c) Stable-VTON and (d) OOTDiffusion) often struggle with complex garment texture details and challenging human poses, resulting in a range of artifacts and the lack of necessary texture details. contrast, (e) our SPM-Diff effectively alleviates these limitations and leads to higher-quality results with better-aligned semantic points, leading to strong visual correspondence and thereby preserving garment detail/shape.
person images. To tackle this issue, recent advances Gou et al. (2023): Morelli et al. (2023); Xu et al.
(2024a); Kim et al. (2024) take the inspiration from Diffusion models Avrahami et al. 2022 Hertz
et al.. (2023): Kawar et al. (2023): Ruiz et al. (2023) Rombach et al. (2022) with enhanced training stability and scalability in content creation, and present new diffuston-based direction for VTON
task. In general, the whole garment image as appearance reference is encoded via VAE encoder and UNet|Kingmaa & Welling (0144; Ronneberger al. (013)), which is further integrated into diffusion model for conditional image generation. Despite improving the quality of synthesized person image with few artifacts, such VTON results still fail to preserve sufficient garment details (Figure I (c-d) due to the stochastic denoising process in diffusion model.
Unlike previous efforts that rely on the whole garment to trigger diffusion process, we view VTON problem from a new perspective of visual correspondence in the paradigm of diffusion model. Intuitively, each in-shop garment image contains interest points, i.e., 2D locations in an image which are stable and repeatable from different viewpoints. In analogy to the traditional interest points in geometric computer vision field Sun et al. (02T); ; Hedlin et al. (2023); TTng et al. (2023); Wang et al. (2023), we name such kind of interest point in VTON task as "semantic point." As shown in Figure [a), when viewed individually, each semantic point refers to the unique regional fine fine-graine grained texture detail; when viewed whole, all semantic points reflect the holistic garment shape. Both regional fine-grained texture detail and holistic garment shape derived from semantic points are supposed to be nicely preserved for VTON tasks. In other words, these semantic points should be aligned with the visually corresponding ones in the synthesized person image. That motivates us to introduce the explicit correspondences of semantic points between in-shop garment and output synthetic person image to diffusion model. With this semantic point matching, we can temper the stochasticity of diffusion model, leading to qughityquality VTON results with better-aligned garment shape and texture details (Figure (e)).
By consolidating the idea of capitalizing on visual correspondence prior for virtual try-on, we present novel diffusion model with Semantic Point Matching (SPM-Diff). Specifically, SPM-Diff first samples a set of semantic points rooted in the given garment image, and matches them to points on target human body according to garment-to-person correspondence via local flow warping. Furthermore, these 2D appearance cues of semantic points are augmented into 3D-aware cues with depth/normal map of target person, mimicing the way of putting clothing human body. Such 3D-aware cues are later injected into a dual- dual-branch diffusion framework to facilitate virtual try-on. In an effort amplify the semantic point matching along the whole diffusion process, exquisitely
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devise point-focused diffusion loss that puts more focus on the reconstruction of semantic points over target persons. Empirical results on two VTON benchmarks demonstrate the evident superiority of our SPM-Diff on garment detail and shape preservation against the state-of-the-an of-the-art methods.
RELATED WORK
GAN-based Virtual Try-on. To tackle virtual try-on (VTON) task, prior works Wang al. (2018)
Li et al. )2 Fele ct al. (2022), Morelli et al. (2022): Dong et al. (201b)); He et al. (2022) Choi
et al. 2022 y Lee al. (2022) Xie et al. (2023) typically adopt a two-stage strategy, first deforming the garment to fit the target body shape and then integrating the transformed garment onto the human
model using GAN-based image generator to synthesize the final person image capitalizing on conditions like the warped garment and human pose. One key point for these works is to accurately warp the garment to the clothing region. As one of the pioneer works, VITON Han et al. (2018) estimates thin thin-plate spline transformation (TPS) Bookstein (1989) based on hand-craft shape context to achieve realistic garment deformation. Later, CP-N Wang et al. (2118) introduces an upgraded learnable TPS transformation to boost the performances. However, the results remain far from satisfactory due to artifacts in the misaligned areas between the suboptimally deformed garment and the desired clothing region, particularly as image resolution increases for online shopping. To tackle this problem. VITON-HD Choi et al. (2021) predicts a segmentation map of the desired clothing regions guide VTON synthesis. which utilized to alleviate the impact from the misaligned regions. Recently. HR- RR-VVTON Lee et al. (2022) novelly introduces try-on condition generator that combines garment warping and segmentation modules to resolve visual misalignment and occlusion. Moreover, an innovative Local-Flow Global-Pane Parbilg warping module is proposed in GP-VTON ( jet al.) (202)), which warps garments parts individually and assembles locally warped results based on the global garment parsing. Despite the promising results, these methods fail to handle complex poses and maintain fine-grained garment details due to the limited generative capability of GANs.
Diffusion-based Virtual Try-on. Diffusion models Ho et al. (2020); Rombach et al. (2022); x Chen et al. (2023b); Zhang et al.) (004)); Zhu et al. (002)); Qian et al. (224)); Yang et al. (2024) have drawn widespread attention due their superior capability in visual generation compared to GANs Goodfellow et al. (2014); Pan et al. (2017). Therefore, some works Gou et al. (2023): : Morelli et al.
(2024); Xu et al. (0024)) have attempted to incorporate diffusion models into the pipeline for V'TO tasks, striving to generate a photo-realistic image that preserves appearance patterns. For example, DCI-VTON Gou et al. (2023) directly overlays the deformed garment over the target person with the desired clothing regions masked out to steer the diffusion models. Furthermore, LaDI-VTON Morelli et al. 2023) additionally learns token embeddings of the given garment and refines the garment detials with an upgraded decoder. GarDiff Wan et al. (2024) simultaneously employs CL.P Radford et al. 202T and Variational Auto-encoder (VAE) Kingma & Welling (2014) to encode the reference garment into appearance priors, which are regarded as additional conditions to guide the diffusion process. However, solely capitalizing on the deformed garment could introduce notable artifacts due to the suboptimal warping results. Therefore, Stable- VTON et al. (2024) novelly improves controlnet (Zhang et al. (202b)) with zero cross-attention blocks implicitly deform the input garment and further injects the intermediate features extracted by the proposed module into the UNet of diffusion models to boost VTON, Most recently, a dual-branch framework with two UNets is presented Xu et al.) (2024a); Choi al. (0024) fully leverage the pre-trained image prior in the diffusion models for garment feature learning. In this framework, a Eierence-UNee initialized from the pre-trained one (dubbed Main- -Net) and employed to learn multi-scale features of the input garment, while the Main-UNe refers to these features for high- fidelity image generation. Although appealing results are achieved by these methods, it remains challenging to preserve every detail of the garment due to weak visual correspondence between the garment and synthesized person using implicit warping mechanism.
(2023) ; /Zhu al. 2023); Kim et al.
METHOD
In this section, we first briefly review the fundamental concepts of Latent Diffusson Model in Sec. 3.1 Next, we elaborate technical details of the overall framework of our proposed SPM-Diff Diff and the novel semantic point matching (SPM) in Sec. aad (.2.2,, respectively. Finally, an upgraded training objective to facilitate semantic point matching is demonstrated in Sec. 3.3
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3.1 PRELIMINARY
The prevalent Latent Diffusion Model (LDM) Rombach et al. (2022) is adopted as our foundational model in this work. Specifically, LDM first exploits pre-trained Variational Autoencoder Kingma & Welling 2014) E() to map an input image lo from the high-dimensional pixel space to the latent space: X {lo). the forward diffusion process at timestep (( T), noises are added the latent code Xo according to a pre-defined variance schedule {3. as follows:
X Vāyxo + V1 11-xze Ōzξ,
where (O 1), ā -I B.), and 3, {3.0T. In the reverse denoising process. LDM learns to predict the added noise and removes it. The typical training objective of LDM (generally implemented UNet Ronneherger et al. (2055) parameterized by 0 can be simply formulated as
[(0, Xo. c) E.,
c, -e1
(2)
where e denotes the conditioning text prompt. During inference, LDM starts from a random noisy latent code and iteratively denoises it given the text prompt step by step for image generation.
3.2 OUR SPM-DIFF
In virtual try-on (VTON) task, given a target person image 1p E R x3 and an in-shop garment Ig E "R' 4''x'x' xW"x3 the model is required to synthesize high-quality image I E R ff W where the person Ip wears the in in-shop garment Ig. The main challenge lies in preserving the intricate details and shape of the in-shop garment, due to the high diversity of synthetic contents (e.g., varied human body) within the intricate sampling space of diffusion models.
3.2.1 OVERALL FRAMEWORK
Recently, the utilization of Reference-Net has demonstrated remarkable efficacy in retaining the fine-grained details of reference images across diverse fields [Co et al. (2023); Hu et al.) (2024);
Nam et al.] (2024); Xu al. (2024b), shedding new light VTON, Drawing inspirations from these studies, we frame our SPM-Diff within a basic dual-branch architecture comprising two UNets: Main-UNS and Garm-UNet where the Main-UNet refers to the features of the implicitly deformed garment from the Garm-UNe for image generation. However, suboptimal outcomes are observed in our experiments with this trivial dual-branch network. We argue that the reason Main-UNet fails to establish precise visual correspondence between the given garment in l and the synthesized person in the output image I by simply using the appearance features from Garm- synthesized garment is prone to inconsistent fine-graines details with the input garment. Therefore, we propose to explicitly incorporate visual correspondence prior into the diffusion model to boost VTON. Specifically, novel semantic point matching (SPM) is additionally introduced to SPM- Diff. In SPM, we interpret the fine thee-grided appearance and texture details as a set of local semantic points, which denote the locations the garment that are stable and repeatable from different view points. These semantic points are matched to the corresponding ones on the target person through local flow warping. These 2D cues are then converted into 3D-aware cues by depth/normal map augmentation, which act as visual correspondence prior to enhance Main- UNet for detail preservation.
Consequently, the
Specifically, the semantic point set PG on the given garment is first sampled and mapped into the corresponding one Ри on the target person via local flow warping. Then, the multi-scale point features of PG Pи are extracted by a pre-trained garment/geometry feature encoder, which are further augmented and injected into the Main-UNe through the proposed SPM module. Formally, given the latent code x,, the denoised X;-1 predicted by our SPM-Diff can be computed as
1 Va
1 Cr: VI- oe
X1-
(x,
+ OrE,
En Ex(x,, X.y Xdi, X,, XI, CI P.,t).
where o; = 1111-- 3r, 07 B, 0 are the parameters of our SPM-Diff. x,, and x. are the latent codes of normal map I, and depth map Ia of the target person, respectively. X1, and cı, denote the latent code and CLIP image embedding of the in-shop garment Ig. respectively. x, is the latent code of curment-agnoistt image I, that is derived by applying a mask over the potential regions in I, to be filled with the input garment. Fig. 2b) illustrates the overview of our SPM-Diff.
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Augmentation
Point Sampling
Pc
Encoderr
Docosor
Block
Linears
Escader
CLIP Encoder
Fu
Warping
Semantic Point Matching
Linears
Encove
Decodda Bock
Grometry
Encodirr
Ma
Main-UNet
Resultt
(a) Semantic Paint Matching
(b) Overall Framework of SPM-Diff
(c) Point Matching Results
Figure 2: The overall framework of our SPM-Diff. (a) Illustration of our semantic point matching (SPM). SPM, set of semantic points on the garment are first sampled and matched to the corresponding points on the target person via local flow warping. Then, these 2D cues are augmented into 3w-aware cues with depth/normal map, which act as semantic point matching supervise diffusion model. (b) Dual-branch framework includes Garm- UNet and Main-UNet for garment feature learning and image generation, respectively. Note that Main-UNer upgraded with SPM for high-fidelity synthesis in SPM-Diff. (c) Visualization of garment-to to-person point correspondence.
3.2.2 SEMANTIC POINT MATCHING
As shown in Fig. 2 (),, a set of local semantic points Pc (c.g., red, green, blue points) on the in-shop garment are first sampled and projected to the corresponding points P'R on the depth/normal map Ia / In of the target person via local flow warping. Note that the garment-agnoste depth/normal map is derived by rendering from estimated SMPL model of the target person Lin al. (2023). The descriptive point features Fo / F h of the points in Pg / Ри are extracted by pre-trained garment/geometry feature encoder. Then, the 2D representations Fa are further augmented into 3D-aware ones FGA that perceive the body shape of the target person by fusing with F Finally, the derived features FG and FGA are integrated into the Main-UNet boost VTON.
Point Sampling. In order to faithfully retain the local fine-grained details (e.g., texture, shape) of the in-shop garment, sparse semantic points of interest are sampled for affordable computation overhead. Specifically, Superpoint DeTone et al. (2018) is employed to sample set of N pixel- level interest points P. on the garment image Ig. Then, the farthest point sampling strategy is adopted to select a subset of K semantic points from P.,. denoted as Pg ((** y²) E R2I=²* e [1, H", E [1, W']. k 1...K,, that approximates P.V' with fewer points while preserving the important characteristics of the garment. Note that (a** x*) the 2D coordinate on the image In-
Then, we associate the semantic points Pa on the garment I, with the corresponding ones Pи on
the target person I, through dense displacement field MG- E Hx x2, Each element M G-
represents the relative displacement (i.e., 2D offset vector) for each point coordinate (i.j)
in-shop garment Ig relative to the person image Ip. Here we employ flow warping module like GP-VTON Xie et al. (2023) to estimate the dense displacement field in between. More details of our flow warping module will be depicted in the Appendix A.4 Then we can retrieve the corresponding semantic points Ри the target person I, by applying MG-#H the point set Pa follows:
(a y²) = (o y²) + M
We visualize some 2D garment-te to to-person semantic point pairs from (Pc. PH) in Fig. 2 (c).
Feature Augmentation. The features Fc F of the point sets Pc / Pи are first extracted from a pre-trrined garment/geometry feature encoder. The garment feature encoder and geometry feature encoder share the similar architecture of Garm-UNet and Main-UNet, respectively. Next, Fe is
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further augmented by the Fir into FGA:
FGA(#E: yv) ( ( yk)) (TK:: Vk.)
(5)
As such, the 2D cues of the semantic points are transformed into 3D-aware ones, mimicing the way garment changes shape with human body and enabling better visual correspondence between the garment and the target person.
Feature Injection. To effectively facilitate semantic point matching for high-fidelity VTON, we inject the derived features EG and FG into the self-attenion layers of Main-UNet manner. Specifically, series of MLPs ΦGA = {da 1=1 and Φg = 1 are exploited to project FGA and Fc, respectively, into the multi-scale features FGA and F that match the feature dimensions of self-attention layers in the Main-UNet as follows:
multi-scale
(TK, yk) = &'A GALKK Uk)), F' (Ikk un. ) d'a (FO( ()))
Given the geometry features F'GA and garment features F'G of semantic points, first augment inter- mediate hidden states of Main-UNet/Garm UNet (i.e., K'K;)) with /FF. Then the augmented intermediate features of Main-UNet/ Main-UNet/Garm-UNNe are concatenated, followed with I-th self-attention layer in Main-UNet (Please refer Fig. 16 in Appendix for more details of the feature injection process):
Attn K. V F',FFAFFE FFc) Softmaz( QQ' + FaA) IK' Fr GA K
V\),
d
Q' = W K' wk'hh, K²
W V [W hi, wy g g:
where hf and hn are the intermediate hidden states from the Main-UNet and Garm-UNet, respectively, WQQ, W.K. and WVV are the projection matrices in the I-th self-attention layer of the Main-UNet. w'R,' and W.V. are the projection matrices in the (-th self-attention layer of the Garm-UNel [.] is the concatenation operation over the token sequence. In practice, the point features F / F are repositioned onto an empty spatial feature map according to the interpolated coordinates of Pa Pи for efficient feature addition as shown in leftmost part of Fig. (a). Since the middle layers in Main-UNe perceive high-level visual concepts instead of the fine- one-grained garment details, only the first two and last two self-attention layers considered in point feature injection.
GA
By explicitly incorporating the visual correspondence between the garment and the target person and further augmenting the 2D cues with 3D geometric conditions via the proposed semantic point matching, our SPM-Diff can better preserve the local garment details and shape more accurately.
3.3 TRAINING OBJECTIVE
To amplify the semantic point matching along the whole diffusion process, devise point-focused diffusion loss that emphasizes on the reconstruction of semantic points over target persons by increasing the loss weight of each semantic point. Thus, the final training objective is
_point_focused focused ||c á|l2 + All(c é) @ M M IIE
(8)
where is defined in Eq. 3 and M(PH) denotes binary mask that only triggers on the semantic points PH and À is the trade-off coefficient.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUPS
Datasets. We train our model on two virtual try-on benchmarks, VITON-HD Choi al. (0221) and DressCode Morelli al. 2022). VITON-HD dataset contains 13,679 frontal-view woman and upper garment image pairs. Following the general practices of previous works Gou et al. (2023) : Morelli et al. (223), the dataset is divided into two disjoint subsets: training set with 11,647 pairs and testing set with 2,032 pairs. DressCode dataset consists of 53,795 image pairs, which are categorized into three macro-categories: 15,366 for upder-body clothes, 8,951 pairs lower-body clothes, and

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Table 1: Quantitative results in single dataset evaluation on VITON- HD and DressCode upper-body
(D.C.Upper). The subscript *sdzi indicates the use of superior base model (stable diffusion xl).
Train/Test
VITON-HDWITON-ID
D.C.UpperD.CCUpper
Method
SSIM 1
LPIPS 1
FID
KID
SSIM
LPIPS 1 FID. J KID !
GF-AFN et al.
10211
0.888
0.087
9.654
1.04
0.910
0.049
17.653
5.43
HR- HR-YTON Lae er a 2202
0.878
0.105
12.265
2.73
0.936
0.065
13.820
2.71
SDAFN
0.880
0.082
9.782
0.907
0.053
12.894
1.09
FS-VTON HJ ar 2022
0.886
0.074
9.908
1.10
0.911
0.050
16.470
4.22
GP-VTOON Xie
2025
0.884
0,081
9.072
0.88
0.769
0.268
20.110
8.17
LaDI-VTON Morelli et al.
2022)
0.864
0.096
9.480
1.99
0.915
0.063
14.262
3.33
SToNe-VTONiKing
0.852
0.084
8.698
0.88
0.911
0.050
11.266
0.72
DCI-VTON
Gol of 20263
0.880
0.080
8.754
1.10
0.937
0.042
11.920
1.89
OOTDiffusion
XU al
20221a
0.881
0.071
8.721
0.82
0.906
0.053
11.030
0.29
IDM-VTON
Chol et d.
2024
0.877
0.082
9.079
0.79
0.891
0.065
10,860
0.32
IDM-VTON Adoa
2024
0.916
0.061
7.033
0.53
0.926
0.040
9,561
0,16
SPM-Diff SPM-Diff
0.911
0.063
8,202
0.67
0.927
0.042
10.560
0.19
0.917
0.055
6.871
0.52
0.933
0.038
9.622
0.14
29,478 for dresses. As in the original splits, 1,800 image pairs from each category are used for testing, and the remaining pairs are used as training data.
Additionally, we leverage a human image dataset (SSHQ-1.0 F. al. (2022) to evaluate our method beyond standard virtual try-on datasets. Note that we follow Stable- VTON Kim et al. (2024) and adopt the first 2,032 images for evaluation in SHHQ-1.0.
Evaluation. We evaluate the performances in two testing settings, i.e., paired setting and unpaired setting. The paired setting uses a pair of a person and the original clothes for reconstruction, whereas the unpaired setting involves changing the clothing of person image with different clothing item. Meanwhile, we adopt single dataset evaluation that performs training and evaluation within a single dataset, and cross-dataset evaluation implies extending our evaluation over different datasets. The experiments on all settings are conducted at the resolution of 512 x 384.
In the paired setting, the input garment is highly correlated to the one depicted in primary person image. Hence we directly follow the standard evaluation setup to leverage Structural Similarity (SSIM) Wang et al. (2004) and Learned Perceptual Image Patch Similarity (LPIPS) Simonyan & Zisserman (2015) for measuring the visual similarity between the generated image and the ground- truth one. In the unpaired setting, since the given target person originally wears different garment from the input in-shop garment and the ground-truth try-on results are unavailable, only the Fréchet Inception Distance (FID) Heusel al. (2017) and Kernel Inception Distance (KID) Binkowski et al. (2018) can be used to evaluate the quality of outputs.
Implementation Details. In our SPM-Diff, Garm-UNet and Main-UNet are initialized from the pre- trained Stable Diffusion 1.5, which are further finetuned over VTON datasets. The garment/geometry feature encoder shares similar model structure as Garment/Main UNet. We jointly pre-train these two encoders in a basic dual- dual-branch network with additional depth/normal map as inputs but without our proposed SPM mechanism, i.e., the ablated run Base+Geo map in Table Then, the pre-trained garment/geometry feature encoder is utlized to extract garment/geometry point features, which are further injected into the self self-attention layers of Main-UNet in our SPM-Diff. We employ AdamW Loshchilov & Hutter (2019) (31 0.9, 33 0.999) to optimize the model. The learning rate is set to 0.00005 with linear warmup of 500 iterations. The Syyee--parmeter
in Equation (8) is set to 0.5. OpenCLIP ViT-H/14 Tharco et al. (2021) is utilized to extract the CLIP visual embeddings of the input garment. To enable classifier- guidance Ho & Salimans (2022), the embeddings of the garment and depth/normal map are randomly dropped with a probability of 0.1. We train SPM-Diff on a single Nvidia 1100 GPU with 45,000 iterations (batch size: 16). At inference, the output person image is progressively generated via 20 steps with a UniPC Zhao al. (2023) sampler, and the scale of blassfiir-ffree guidance is set as 2.0. Compared with a trivial dual branch model, the computation overhead is slightly increased with SPM-Diff: 1.97 secs (SPM-Diff) vs 1.50 secs (OOTDiffusion).
4.2 BENCHMARK RESULTS
Quantitative Results in Single Dataset Evaluation. We first present the VTON results on VITON- HD and DressCode (three categorre)))) in Table I and under in-distribution setup, where both training and testing data are derived from the same dataset/category Note that for fair comparison with IDM-VTON. sazt that adopts superior base model (Stable Diffusion xl), we include additional
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Table 2: Quantitative results in single dataset evaluation on DressCode lower-body (D.C. Lowwr) and DressCode dresses (D. DDC.DD C.Dresses) datasets. The subscript * *ddt indicates the use of superior base
model (stable diffusion xl).
Train/Test
D.C.LoerDD..Lower
D.C.Dres/D...Drr C.Dress
Method
SSIM f
LPIPS $
FID
KID
SSIM f
LPIPS 1 FID 4 KID 1
PF-AFN al 2020
0.903
0.056
19.683
6.89
0.875
0.074
19.257
7.66
HR-YTON
ze ara al
0.912
0.045
16.345
4.02
0.865
0.113
18.724
4.98
SDAFN
Bal ale 2022
0.913
0.049
16.008
2.97
0.879
0.082
12.362
1.28
ES-VTON
eral 2024
0.909
0.054
22.031
7.46
0,888
0.073
20.821
8.02
GP-VTON
Xle et 2026
0.923
0.042
16.65
2.86
0.886
0.072
12.65
1.84
LaDI-VTON Murelli el al 1202
0.911
0.051
13.38
1.98
0.868
0.089
13.12
1.85
OOTDiffusion Xu
0.892
0.049
9.72
0.64
0.879
0.075
10.65
0.54
DCI-VTON
Gou cla
2025
0.924
0.035
12.34
0.91
0.887
0.068
12.25
1.08
IDM-YTON Chor et 2024
0.919
0.041
12.05
0.93
0,881
0.074
12.33
1.41
IDM-VTON,.C ade! Chol et 3l.
2024
0.934
0.032
8.77
0.44
0,904
0.060
9.87
0.47
SPM-Diff SPM-Diff
0.932
0.034
9.02
0.48
0.899
0.063
10.17
0.50
0.934
0,030
8.68
0.42
0,909
0.060
9.62
0.44
Table Quantitative results in cross dataset evaluation. We train VTON models on one of VITON- HD and DressCode upper-body (D.C.Upper), and then evaluate them on different datasets. The subscript *ddzl indicates the use of superior base model (stable diffusion xl).
Train/Tes
VITON-IIND.C.Upp
D.C.Uppe/VIITON.II -ID
VITON-IDSSSION
Method
SSIM 1
LPIPS J FID
KID
SSIM 1
LPIPS 1 FID
KID
FID 1
KID 1
HR-VTON ge et W2133
0.862
0.108
24.170
7:35
01811
0.228
45,923
36.69
52.732
31.22
GP-VTON A di 2023
0.897
0.385
12.451
6.67
0.804
0.262
22.431
27.90
20.99
8,76
LIDI-TTON Morelli et al 2023
0.901
0.101
16.336
5.36
0.801
0.243
31.790
23.02
24.914
6.07
DCI-TOO Cou
0.905
0.142
17.3426
12.03
01825
0.179
16.670
6.40
24.850
6.68
Stable VTON
0.914
0.065
13.182
2.26
0.855
0.118
10.104
1,70
21.077
5.10
OOTDifusson
0.915
0
11,965
1.20
0.839
0.123
10.220
2.72
19.62
5.45
IDM-VTON
0.909
0,066
12.398
1.18
0.821
10.091
2.88
18.78
4.88
IDM-VTON...
2021
0.925
0.052
10.003
0.89
0.879
8,203
1,98
14.32
0.922
0.055
1.01
SPM-DiIL.02
0.929
0.050
9.235
0.65
0.882
8.196
13,68
variant of our SPM-Diff (i.c., SPM-Diff.dss) that uses the same base model of Stable Diffusion xl. Overall, for VITON-HD and each category in DressCode, our SPM- Diff consistently achieves better results against existing VTON approaches across most metrics, including both GAN-based models (HR-VTÓN, SDAFN, GP-N etc) and Liffusion-basse models (e. (e.g..LaDI-vi VTON, Stable-VTON, OOTDiffusion, etc). In particular, the FID score in unparied setting of our SPM-Diff can reach 0.911 and 8.202, which leads to the absolute gain of 0.030 and 0.519 against the strong competitor OOTDiffusion. The results basically demonstrate the key merit of taming diffusion model with semantic point matching mechanism to facilitate virtual try-on task. More specifically, diffusion-based methods commonly exhibit better unpaired scores than GAN-based models. This observation is not surprising, since diffusion model reflects more powerful generative capacity than GAN in image synthesis. In between, instead of simply encoding in-shop garment via CLIP as additional condition in DCI-VTON, Stable-VTON and OOTDiffusion capitalize on VAE encoder and UNet to achieve more fine- greingr appearance representations for triggering diffusion process, thereby leading to better VTON results. Nevertheless, the performances of them are still inferior our SPM-Diff, since the visual correspondence between in-shop garment and output synthetic person image is under-explored in existing diffusion- -bsed approaches. Instead, by novelly formulating VTON task as semantic pointing matching problem, SPM-Diff tempers the stochasticity of diffusion model with explicit visual correspondence prior, and achieves competative VTON results.
VITON-HD dataset, the SSIM score in paired setting and
Quantitative Results in Cross Dataset Evaluation. Proceeding further, we evaluate all VTON models under out-of-distribution setup in Table 3 where training and testing data are derived from different datasets. Similar to the observations in single dataset evaluation, our SPM-Diff still exhibits better performances than other baselines across most metrics. The results again demonstrate the advantage of modeling semantic point matching for virtual try-on.
Qualitative Results. Next, we perform a qualitative evaluation of different methods through case study. Fig. 3 shows some examples of VITON-HD dataset. Overall, our SPM-Diff can better preserve fine-grained garment details and the contour of the synthesized garment across varied human body shapes compared with other baselines. This verifies the merit of incorporating semantic matching prior and garment feature augmentation with 3D geometric conditions into diffusion model. For example, both GAN-based models (GP-VTON) and diffusion-based models (e.g., Stable-VTON,
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GP-VTON
LaDI-VTON
DCI-VTON
Stable-VTON
OOTDiffusion
IDM-VTON
SPM-Diff
Figure 3: Qualitative results on the VITON-HD dataset.
Presovation of Appcoranse Detall
Preseraatoon Hamon Boay shapes
Imaage Rediioe
Figure 4: User study 100 garment-persase pairs randomly sampled from VITON-HD dataset. OOTDiffusion, etc) fail to retain accurate garment details 1st row. while our SPM-Dif Difff successfully restores the appearance of the garment. Please refer to Appendix [ for more qualitative results.
User Study. A comprehensive user study is conducted to examine whether the synthesized images conform to human preferences. We randomly sample 100 unpaired garment-person from VITON-HD dataset for evaluation. 10 participants from diverse education backgrounds (i.e., fashion design (4), journalism (2), psychology (2), business (2)) are invited and asked to choose the winner between our SPM-Diff and the competing approaches based three criteria: 1) preservation of the fine-grained appearance details, 2) preservation of the garment contour across diverse human body shapes. 3) the perceived image realism. Fig. summarizes the averaged results by all the participants of different methods over 100 garment-pent pairs, As shown in Fig, 4 our SPM-Diff significantly outperforms the rival models by large margin regarding garment detail, shape preservation and image realism.
4.3 DISCUSSIONS
Effeet of Pivotal Components in SPM-Diff. Here, we investigate the effect of each pivotal compo- nent in our SPM-Diff. We consider one more design at each run and the overall performances on VITON-HD dataset are listed in Table. Base is implemented as the trivial bual-branch framework with two UNets described in Sec. 3.2. which takes (X,, X., X1, C12) as input. By incorporating the depth and normal maps (denoted Geo map in Table. 44) as additional conditions, Base + Geo map better controls the body pose and shape the synthesized person, yielding marginal improve- ments over Base. Base + Geo map + SPM significantly boosts the performances by enforcing the alignment of semantic points between the garment and target person and further augmenting the 2D features of these points with 3D geometric conditions. Particularly, Base + Geo map + SPM makes the relative gains of 16.9% and 27.7% on LPIPS and KID against Base + Geo map, respectively. This again demonstrates the effectiveness of capturing better visual correspondence for VTON synthesis. Moreover, the woint-focused diffusion loss (P-loss) is devised to emphasize the reconstruction semantie points over target persons, and best results are attained by Base + Geo map SPM P-loss. Some examples generated by each run are illustrated in the Appendix A.2
Effect of Semantic Point Count SPM. We then analyze the effect of semantic point count K. Table. 5 summarizes the performances of our SPM-Diff with varying K E [10, 25, 50, 75] on VITON- HD dataset. When fewer semantic points (K = 10) are sampled, the fine- fine-griined garment details may be not fully captured, which hinders the preservation of all the garment details in the synthesized images. Increasing K from 10 to 25 alleviates this aforementioned issue and improves the results. However, employing semantic points (K- 50 75) yields worse results. Specifically, more

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Published as a conference paper at ICLR 2025
LaDI-VTON
DCI-VTON
Stable-VTON
GP-VTON
OOTDiffusion
SPM-Diff
Figure 5: Accuracy of visual correspondence between in-shop garment and synthesized person image.
Table 4: Ablation study of our proposed SPM-Diff Table 5: Ablation study of semantic point the VITON-HI dataset.
count K on the VITON-HD dataset.
Model
SSTM
LPIPS FTD
KID
Point Count SSIM 1 LPIPS L FID KID
0.876
K= 10
0.890
0.083
8.689
0.85
(2) Base Geo map
0.881
0.083
8.621
0.94
K= 25
0.911
0.063
8.202
0.67
Base Gee mup SPM
0.904
0.069
8.320
0.68
0.901
0.070
8.503
0.73
(4) Base Geo map SPM P-loss
0.911
0.063
8.202
0.67
K 75
0.893
0.081
8.599
0.78
semantic points are inevitably clustered in local neighborhoods in pixel space with large K, which will be interpolated same locations the low-dimensional feature map. However, their corresponding points projected onto the target person may be different due to the significant offsets from imprecise local flow map MG-H, leading to one-to-many point misalignment and compromising the holistic coherence of the synthesized images. Some examples visualized in the Appendix A.2
Effect of SPM in Visual Correspondence. In addition, we evaluate the visual correspondence accuracy between in-shop garment and synthesized person image, which can be generally regarded as one kind of detail detail- preseriin capability. We follow the typical point mapping method in Super- Point(DeToon
al. (2018) to perform HPatches homography estimation. Specifically, we perform nearest neighbor matching between all interest points and their descriptors detected in the in-shop garment image and those the synthesized person image. Then employ OpenCV's implementa- tion of HindHomography() with RANSAC to compute the correctly matched points in the image pairs, which are further marked in green (Fig. 5). In this way, the denser the green point mappings covered over two images, the better the perservation of interest points (i.e., garment details) for synthesized person image. can easily observed that our SPM-Diff manages to align more semantic points and therefore preserve more lie-ggaiied garment details than the other competing baselines, which validates our proposal of semantic point matching.
5 CONCLUSION
We have presented SPM-Diff, new diffusion-basel model for virtual try-on task. Different from holistically encoding in-shop garment as appearance reference, SPM-Diff uniquely excavates visual correspondence between the garment and synthesized person through semantic points of interest rooted in the given garment. Such structured prior is fed into diffusion model to facilitate garment detail preservation along diffusion process. Extensive experiments validate the superiority of our SPM-Diff when compared to state-of of-e---f-te-r- VTON approaches in terms of both single dataset evaluation and cross dataset evaluation.
Acknowledgement. This work was supported in part by the Beijing Municipal Science and Technol- ogy Project No. 7241 100001324002 and Beijing Nova Program No. 20240484681.
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A
APPENDIX
A.1
MORE QUALITATIVE RESULTS
Fig. 6 showcases more examples generated by different methods on the VITON-HD dataset, while the visual results of other clothing categories (i.e., lower-body clothes and dresses) the DressCode dataset are summarized in Fig.
HR-VTON
GP-VTON
LaDI-/TON
DCI-VTON
Stable-VTON
0OTDiffusion
SPM-Diff
Figure More qualitative results on the VITON-HI dataset.
GP-VTON
LaDI-VTON
OOTDiffusion
SPM-Dif
GP-VTON
LaDI-TTON
OOTDiffusion
SPM-Diff
Figure 7: Qualitative results the DressCode dataset.
A.2 DISCUSSIONS
Effect of Pivotal Components in SPM-DifE. Fig. illustrates some examples generated by the different runs in Table. 4
Effect of Semantic Point Count in SPM. Fig. takes an example to demonstrate the effect of semantic point count K. Similar the observation Table the most visually pleasing result is achieved when K 25. We agree that significantly increasing semantic point count (e.g., from K 25 K 50 75) will result in redundancy and interpolation/proocction error, leading to degraded results discussed in the main paper. However, when taking an in-depth look at the sensitivity of semantic point count around the optimal value K 25, the semantic point count not sensitive. To validate this, experimented by varying semantic point count K in the range of [20, 40] within an interval of on VITON-HD dataset. As shown in Table 7 the performance under each metric only fluctuates within the range of 0.1, which basically validates that the performance is not sensitive to the change of semantic point count within this range.
Effect of Hyper-paramett different hyper--pruun parameter À on VITON-HD dataset, and = 0.5 achieves the best performances across all the metrics.
À in (q. Table 6 summarizes the results of our SPM-Diff with
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Target In-shop Garment
map
map
Base Geo SPM P-loss
Figure 8: Ablation study of the pivotal components in SPM-Diff VITON-HD dataset.
Target Person
K=10
K=25
K=50
K=75
Wiscler
Whangls
wasele
Wrangler
Wrangler
Wrangfer
Wiangler
Wrangler
Figure 9: Ablation study of semantic point count K VITON-HD dataset.
Evaluation of 3D Conditions Learnt by Various Methods. The 3D cues (i.e., depth/normal map) adopted in our training and inference are the estimated results by using pre-trained 3D human LisXLinoo reconstruction model (OSX Lin et al. (2023)), rather than manually annotated accurate 3D conditions. conducted experiments by evaluating SPM-Diff with varied estimated 3D cues achieved from different 3D human reconstruction models (c.g., PyMAF-X Zhang et al. (2023a). OSX Lin al. (2023), 4DHumans Goel al. (2023) on VITON-HD dataset. The results are summarized in Table S and similar performances attained across different pre-trained 3D human reconstruction models.
Generalization to In-the- Wild VTON. Moreover, to further evaluate the generalization of our SPM-Diff under challenging scenario, take in- the-wild person images with complex backgrounds as inputs (in contrast to the clean backgrounds in VITON-HI dataset). As shown Fig, 10 our SPM-Diff manages achieve promising results.
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Table 6: Effect of hyper-parameter À in E
Table 7: Ablation study of semantic point count
SSIM f LPIPS + FID 4 KID
K on the VITON-HD dataset.
Point Count SSIM f LPIPS
FID + KID I
1.0
0.899
0.071
8.318
0.689
0.5
0.911
0.063
8.202
0.670
K = 20
0,903
0,061
8.201
0.66
K = 25
0,911
0,063
8.202
0.67
0.1
0.910
0.066
8.287
0.687
K = 30
0,908
0,060
8.210
0.65
0.05
0.906
0.067
9.309
0.682
K = 35
0,905
0.062
8.228
0.63
0.01
0.905
0.067
8.320
0.680
K = 40
0.889
0.066
8.215
0.64
Table 8: Quantitative results with varied estimated 3D cues achieved from different 3D human reconstruction models on VITON-HD dataset.
3D human reconstruction model SSIM
LPIPS
FID 4 KID +
PyMAF-X Zhang et al. (2023a)
0.901
0.064
8.265
0.63
OSX Lin et al. 20233
0.911
0.063
8.202
0.67
4DHumans Goel et al. (2023)
0.908
0.060
8.211
0.68
Discussion with Warp-based Methods. In particular. existing warp-based methods Dong et al. (2019a); |Han et al. (2019); Xie et al. (2023); Li et al. (2023b) commonly adopt warping model to warp the input garment according to the input person image, and then directly leverage the warped garment image as hard/strong condition to generate VTON results. This way heavily relies on the accuracy of garment warping, thereby easily resulting in unsatisfactory VTON results given the inaccurate warped garments under challenging human poses (see the results of GP-VTON in Fig. [T]. On the contrary, our proposed SPM-Diff adopts soft way to exploit the visual correspondence prior learnt via the warping model as a soft condition to boost VTON. Technically, our SPM-Diff injects the local semantic point features of the input garment into the corresponding positions the human body according to a flow map estimated by the warping model. This way nicely sidesteps the inputs of holistic warped garment image with amplified pixel-level projection errors, and only emphasizes the visual correspondence of the most informative semantic points between in-shop garment and output synthetic person image. Note that such visual correspondence of local semantic points are exploited in latent space (corresponding to each local region), instead of precise pixel-level location. Thus when encountering mismatched points with slight displacements within local region, SPM-Diff still leads to similar geometry/garment features as matched points, making the visual correspondence more noise-resistant (i.e., more robust to warping results). As shown in Fig. IT given warping results with severe distortion, our SPM-Diff still manages to achieve promising results, which basically validate the effectiveness of our proposal.
Evaluation of Point-matching Capability. we examine the point- voint-matching capability of the pre- trained warping module adopted in our SPM-Diff, in comparison with a vanilla stable diffusion model. Note that here we use training-free free approach (DIFT the stable diffusion model to match the corresponding points between two images. Specifically, we construct the point-matching test set by manually annotating semantic points on subset of garment images and their corresponding points on the person image. Next, given the target semantic points on the garment image, our adopted warping module and DIFT perform point matching on the person image. Finally, we report the mean square error (MSE) between the matched points via each run and the annotated ground truth. As shown in the Table 9. our adopted warping module demonstrates stronger mitt-matchhng capability than the vanilla stable diffusion model. In addition, we visualize the noint-matching results of each run in Fig. [2. and our adopted warping module yields more accurate point-matching results than vanilla stable diffusion model.
al. (2023) baseline that exploits
Table 9: Evaluation of the point-matching method.
Model DIFT Tang et al. (022)) Our Warping Model
MSE (L)
39.46
29.84
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Target In-shop
Try-on In-shop Try-on In-shop
Try-on
MOT
MON
Figure 10: Tryon results in-the-wild person images with complex backgrounds.
Target Person
In-shop Garment
Warped Garment
GP-VTON
SPM-Diff
love
Nove
Nove
oove
Figure 11: Comparisons with warp-based methods the VITON-HD dataset.
Discussion Semantie Point Matching for Loose Garments. For the of loose garments like dresses or skirts, some semantic points of the garment may fail to exactly fall on the human body. However, these points still convey certain local details of the garment (garment features), and
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In-shop Garment
Our Warping Module
DIFT
Tommy tulfy
Timnytiily
Tipey
Figure 12: The visualization results of the point matching.
the visual correspondence prior in latent space also robust to warping results (see discussion in Discussion with warp-based methods). This facilitates VTON of loose garments in local detail preservation. Both Figure 7 and Figure 13 demonstrate the effectiveness of our SPM-Diff for loose clothes like dresses skirts.
A.3 FULL-BODY VIRTUAL TRY-ON
Our SPM-Diff can naturally support full-body outfits by successively performing VTON of upper and lower garments one by one, and Fig. 14shows some VTON results for full-body outfits.
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Figure 13: Tryon result for loose clothes like dresses or skirts.
Garment
Target Person
Tryon Result
Garment
Target Person
Tryon Result
Figure 14: Tryon result of full-body outfits generated by our SPM-Diff.
A.4 MORE DETAILS OF SPM-DIFF.
Flow Warping Process. As shown Fig. 15 given in-shop garment I, and the condition triplet C' of target human, we leverage two feature pyramid networks, garment feature extraction E-)() and person feature extraction Ez(-) to extract multi-scale features, denoted as Ey (Ig) {91- 92 ..... .9x] } and EC(C) {c1, C2 C.N) ). These pyramid features then fed into N fusion blocks to perform local flow warping. Note that these fusion blocks exploit local-flow global global-parsing parsing blocks to estimate
N multi-scale local flows ft, yielding the final estimated displacement field MG-#-
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In Shop Sarment
Uppamplin
Corr abonn
Conditon Crighen
Posson Feature
Feature
Desse Displicemnnt
warp
Germent Neauure
Person Feature Edrrctioon
Incoming Local Flows
ft
corti'
fx
Fusion Block2
Fusion Block3
Relined Local Howss
[a] Warp Module
(b) Fusion Block
Figure 15: Overview of the flow warping module.
Kg
Feature Augment
Alignment Feature
(2d->3d) 3dl
Self-Attn
Cross-Attn
K
FeA
Kg: Garm-UNet Hidden State
K: Main-UNet Hidden State
Garment CLIP Feature
Figure 16: Details of the feature injection process.
Feature Injection Process. As shown in Fig. 16 the projected geometry features FGA and garment features FG of semantic points are added to the intermediate hidden states of Main-UNet (i.e., K) and Garm-UNet (i.e., Kg). respectively, which are further concatenated together. Finally, the derived features are further fed into the self-attention layer Main-UNet
A.5 LIMITATIONS
Although our SPM-Diff nicely preserves most texture details of in-shop garments, this approach still suffers from artifacts in human hands and fails to exactly preserve other decorative items of target person, when garment overlaps with decorative items. Taking Fig. T7as as an example, the primary handbag of target person becomes larger with extra parts in the output synthesized person image. Meanwhile, some artifacts are observed on human hands. We speculate that these artifacts might be caused by the stochasticity in diffusion model, and VTON techniques only focus the preservation of in-shop garment, while leaving the decorative items of target persons unexploited. One possible solution inject more conditions of human hands (e.g., hand pose) and decorative items (e.g., the canny edge information), and we leave it as one of future works.
A.6 ETHICS STATEMENT.
have thoroughly read the ICLR 2025 Code of Ethics and Conduct, and confirm our adherence to it. Our proposed SPM-Diff is originally devised to achieve state-of-the-art VTON performances with a novel semantic point matching and thereby enhance the online shopping experience E- commerce industry, which poses threat to human beings the natural environment. However, these synthesized person images risk exacerbating misinformation and deepfake. To alleviate the misuse of our model, will require that users adhere usage guidelines, and meanwhile integrate
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In-shop Garment
Target Person
VTON Result
Figure 17: SPM-Diff suffers from artifacts in human hands and fails to fully preserve decorative items where they overlap with garment.
model with additional pipeline to automatically add digital watermark into the synthesized images. The limitations of SPM-Diff are included in Appendix All references involved this work have been properly cited the best of our ability. Datasets and pre-trained models used in ways consistent with their licences in this work. The user study is conducted under appropriate ethical approvals.
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Reviews:
[{"review_id": "8rqkwnjPXS", "reviewer": "Reviewer_37Fc", "summary": "This paper introduces a novel approach for virtual try-on tasks, addressing the challenge of preserving garment shape and fine-grained details. Specifically, the authors propose Semantic Point Matching Diffusion (SPM-Diff), a model that leverages structured semantic points as visual correspondence cues between garments and target human models. By mapping 2D semantic points to 3D-aware cues through depth and normal maps, SPM-Diff aligns garment details closely with the target human body. Experimental results on VITON-HD and DressCode datasets demonstrate the effectiveness of SPM-Diff.", "questions": "- Could you provide more details on the local flow warping process?\n\n- In Figure 5, are all the points shown the correct correspondences? If not, would it be possible to use different colors to distinguish correct from incorrect point mappings? Additionally, how do you assess the accuracy or correctness of these point mappings?\n\n- It would be better to include a figure to illustrate the feature injection process, specifically showing how point features are integrated into the Main-UNet?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper leverages semantic point matching as a prior to enhance garment shape and texture preservation.\n\n- Extensive testing on the VITON-HD and DressCode datasets demonstrates the model's robustness and superior performance.\n\n- The authors have provided the code to ensure reproducibility of their results.", "weaknesses": "- In line 254, local flow warping is used as a method to associate semantic points with their counterparts on the target person. It would be better to provide more detail on the local flow warping process for better understanding.\n\n- For Figure 5, it’s difficult to assess the accuracy of the matched points. Using different colors (e.g. red and green) to illustrate correct and incorrect mappings would improve clarity.\n\n- Adding a figure to illustrate feature injection would help clarify how point features are incorporated into the Main-UNet.", "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": "ya4BQCFAlA", "reviewer": "Reviewer_SQRJ", "summary": "This paper presents SPM-Diff, a virtual try-on framework utilizing diffusion models that maintain garment details and shape leveraging visual correspondence. Due to warping variability, traditional VTON methods using diffusion models have difficulty preserving garment features. SPM-Diff addresses this issue by focusing on \"semantic points\" in garment images that capture detailed textures and shapes. Experiments on the VITON-HD and DressCode benchmarks show that SPM-Diff achieves state-of-the-art results in virtual try-on.", "questions": "The core idea of this paper is to integrate visual correspondence into the diffusion model. However, visual correspondence is not a new thing that is widely used in traditional try-ons. In addition, some people used warping + diffusion, such as : WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on.\n\nPlease clarify their differences and provide more details about the novelty, not just replace \"warping\" with \"visual correspondence\".", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. By identifying and aligning stable \"semantic points\" between garment and human images, SPM-Diff effectively reduces randomness in diffusion models, enabling precise and accurate garment reproduction.\n\n2. The incorporation of 3D depth and normal maps enhances realism by accurately controlling garment fit over the body, reflecting considerable thought in modeling garment behavior in 3D space.", "weaknesses": "1. SPM-Diff's dependence on semantic points may lead to instability when many points are used due to interpolation and projection errors, as discussed regarding point count sensitivity.\n\n2. SPM-Diff relies heavily on accurate depth and normal maps, which may limit its generalization to datasets or images without dependable 3D cues.\n\n3. Although the model effectively preserves garment details, evaluations primarily concentrate on image quality metrics, neglecting user-centered metrics such as perceived realism in end-user studies.\n\n4. Introducing \"visual correspondence\" into diffusion for virtual try-on is not new. Please clarify the \"Semantic Point Matching\" with \"Warping parameters learning\"", "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": "BKUFm8MPIc", "reviewer": "Reviewer_THne", "summary": "This paper presents a new method for virtual try-on by enforcing explicit correspondence through structured semantic points, which are first extracted from the garment image and then warped into the target body pose through local flow warping. The pair of points then serve as priors to guide the overall generation process of the try-on diffusion model, composed of a garment reference net and a main generation net. Experiments demonstrate this method generates high-quality try-on results.", "questions": "There are a few questions apart from the weakness:\n1.\tAs the local flow warping adopted in the paper relies on the coarse human body estimated with SMPL, I doubt if the warping method is capable of dealing with loose clothes like dresses or skirts. If not, will the proposed point prior still work to facilitate the generation process and get a good result?\n2.\tIn lines 262-264, the author claims that pre-trained garment/geometry feature encoders are adopted to extract features for the following generation process, but I could not find any clarification on how these encoders were obtained. The author should clarify this.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tThe proposed method is able to generate high-quality virtual try-on results. Experiments show that the proposed method outperforms existing pipelines.\n2.\tThe idea of the paper that adopts pair of semantic points to facilitate the generation process and serve as an extra supervision signal is interesting and reasonable. If robust corresponding points could be found between the garment and the target body, they could be very good priors to boost the diffusion process. \n3.\tThe paper is well-written and easy to follow.", "weaknesses": "The main weakness of the paper is on semantic point matching.\na)\tHow to acquire robust and accurate point matching should be the focus of the paper. However, this paper did not address this problem but rely on the local warping method proposed in GP-VTON[1], which is another virtual try-on method. This raises the concern about the contribution.\nb)\tThe evaluation of the accuracy of the point-matching method is very limited. The point matching should achieve much more accurate results than the base diffusion model thus it could be used as a prior to guide the generation process. However, this is not demonstrated in the paper.\n\n[1] GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning https://arxiv.org/abs/2303.13756", "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)