ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On
Authors: Yuhan Li
Zhiyu Jin
Yifan Tong
Wenxiang Shang
Benlei Cui
Xuanhong Chen
Zhu Hangcheng
Bingbing Ni
Venue: NeurIPS 2025 poster ()
Keywords: diffusion model
virtual try-on
controllable generation
Primary area: deep_learning

Abstract: Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse footwear and model images while maintaining 3D consistency and texture authenticity. 

Unlike conventional garment-focused VTON methods, VFTON presents unique challenges due to (1) Data Scarcity, which arises from the difficulty of perfectly matching product shoes with models wearing the identical ones, (2) Viewpoint Misalignment, where the target foot pose and source shoe views are always misaligned, leading to incomplete texture information and detail distortion, and (3) Background-induced Color Distortion, where complex material of footwear interacts with environmental lighting, causing unintended color contamination. 

To address these challenges, we introduce MVShoes, a multi-view shoe try-on dataset consisting of 7305 well-annotated image triplets, covering diverse footwear categories and challenging try-on scenarios. Furthermore, we propose a dual-stream DiT architecture, ShoeFit, designed to mitigate viewpoint misalignment through Multi-View Conditioning with 3D Rotary Position Embedding, and alleviate background-induced distortion using the LayeredRefAttention which leverages background features to modulate footwear latents. The proposed framework effectively decouples shoe appearance from environmental interferences while preserving high-quality texture detail through decoupled denoising and conditioning branches. 

Extensive quantitative and qualitative experiments demonstrate that our method substantially improves rendering fidelity and robustness under challenging real-world product shoes, establishing a new benchmark in high-fidelity footwear try-on synthesis. The dataset and benchmark will be publicly available upon acceptance of the paper.

ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On
Yuhan Li Zhiyu Jin"", Yifan Tong", Wenxiang Shang". Benlci Cui',
Xuanhong Chen', Hangcheng Zhu?, Binghing Ni
Shannhaai Jiao Tong University
"Alibaba Group
with diffccltt moteh
(2) Vienpolat (1) Data Scarcity
Niisalgnmmnt
Back Rackgrrand weaekkgrnnd--ddiee d-duudddeeedd Color Disturtion
(a) We illustrate the challenges in VFTON, (1) Subtle variations make it difficult mutch data pairs. (2) Information needed by the target view often not provided given product that interact with environment lighting. loading confusion
Human feet (Target vlew) (b) ShoeFit has demomsiratee excellent preservation of fidelity of footwear roal-world
Product shues (Source view)
Try-on results
Shoes oflen exhibit complex reflections and shudows
natural color.
Figure 1: VFTON presents three challenges, while ShoeFit gives successful solution.
Abstract
Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful tr results given diverse footwear and model images while maintaining 3D consistency and texture authenticity. Unlike conven- tional garment-focused VTON methods, VFTON presents unique challenges due to (1) Data Scarcity, which arises from the difficulty of perfectly matching prod- uct shoes with models wearing the identical ones, (2) Viewpoint Misalignment, where the target foot pose and source shoe views are always misaligned, leading to incomplete texture information and detail distortion, and (3) Background-indune Color Distortion, where complex material of footwear interacts with environmen- lighting, causing unintended color contamination. To address these challenges, we introduce MVShoes, multi-view shoe try-on dataset consisting of 7305 well- annotated image triplets, covering diverse footwear categories and challenging try-on scenarios. Furthermore, we propose a dual-stream DiT architecture, ShoeFit, designed to mitigate viewpoint misalignment through Multi- View Conditioning with 3D Rotary Position Embedding, and alleviate background using the LayeredRefAttentionn which leverages background features modulate
-indguuuddddd induced distortion
Equal contributions.
Work done during an internship Alibaba. :Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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footwear latents. The proposed framework effectively decouples shoe appear- ance from environmental interferences while preserving high-quality texture detail through decoupled denoising and conditioning branches. Extensive quantitative and qualitative experiments demonstrate that our method substantially improves rendering fidelity and robustness under challenging real-world product shoes, es- tablishing a new benchmark in figh-fidelity footwear try-on synthesis. The dataset and benchmark will be publicly available upon acceptance of the paper.
1 Introduction
Virtual Footwear Try-On (VFTON) involves synthesizing product shoes onto human foot images, preserving their geometric structure and material properties, based on input images of both the source product and the target foot. VFTON offers significant commercial potential and academic value, with Nike's 's annual footwear sales reaching 33,427 million dollars (48), while shoes, as complex 3D multi- view objects (57). present unique challenges in research on controllable generation. Unfortunately, this field has not been sufficiently explored within the community.
There are three main challenges hindering the advancement of VFTON. (1) Data Scarcity: Due to the prevalence of footwear with similar appearances but subtle variations, as in Fig. [ (a-1), such as slight differences in shoelaces, heels, and stripes, it is challenging to accurately match product shoes with models wearing the identical ones. Further exacerbating this issue, companies tend to keep data related to shoe try-ons confidential due its clear commercial value (9). As result, there is currently no available open-source dataset for footwear try-ons. (2) Viewpoint Misalignment: Unlike flat-lay 2D garment VTON, where both the larget garment and human pose are captured from aligned front-facing perspectives, as in Fig. [] (a-2), virtual footwear try-on inherently involves mismatched viewpoints between in in-shop product images (source view) and target human poses (target view). Specifically, information needed by the target view is often not provided in the given product image due 3D constraints, creating an inoormatioo-dfiiieen scenario that results detail distortion and artifacts (42) (5).. (3) Background-induce
Color Distortion: Unlike the diffuse reflection properties of knitted fabries in clothing, which ensure visual color largely remains unaffected by the background, shoes often exhibit complex reflective properties that interact with ambient lighting (32) making them more susceptible to visual interference from background colors, as Fig. T (a-3). This interaction between appearance and background leads to color contamination from surroundings and distorted material rendering in synthesized results. The above issues are first identified and explicitly proposed in this paper, which make it impossible for VFTON to share the same pipeline as VTON, thus presenting VFTON with unique challenges and research value.
To tackle these challenges. we introduce MVShoes, the first multi-view footwear try-on dataset. It comprises 7305 annotated high- resolutooo image triplets, each containing two product shoe images from different views and a corresponding try-on result, as shown in Fig. F The dataset covers diverse range of shoe categories and try-on scenarios, supported by a comprehensive and rigorous data cleaning pipeline to ensure accurate matching of identical product shoes with their human images.
Furthermore, we propose ShoeFit, a customized mual-mmage-stream DiT (5l) architecture for VFTON, which generates high-fidelity try-on results through decoupled denoising and conditioning branches. ShoeFit is also equipped with two key improvements: (1) Mieti-View Conditioning, incorporat- ing reference concatenation (IO) and corresponding 3D Rotary Position Embedding (3D RoPE). While a single source product view often fails to provide complete shoe texture features required for the target try-on view, the try-on results can significantly benefit from multiple source views, effectively reducing uncertainty in target surface rendering and minimizing texture distortion. Lee ttention Module. LayeredRefAAtentio leverages background features to modulate shoe features (28), decoupling the intrinsic shoe appearance from the surroundings. Additionally, the subsequent denoising attention suppresses background-reltter computations, effectively mitigating background color contamination. This layer-aware attention mechanism significantly enhances robustness in real-world footwear try-on scenarios. Our contributions are summarized as follows:
We formally define the VFTON problem, reporting the unique issues of Data Scarcity, View- point Misalignment and Background-inducee color distortion across real-world scenarios.
We curate the first shoe try-on dataset, containing 7305 pairs of high-quality multi-view garment-mon samples, paving the way for subsequent footwear try-on studies.

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(b) Data procesing pipeline (N) Data examples from the MY MtVShoes
Human Feet
Agnostic mask
Pose image
Multi-view product shoes
Multi-view product masks
Feature Extraction
Mask Preparation
Human Detection
Deduplication
Manual Filtering
Human
Feature matching for identical shoes
Deduplication
Feature Extraction
Image
Dataset
Product Amtiluse
O
Extmetion
images
images
Figure 2: We illustrate examples of the MVShoes dataset in (a) and data processing pipeline in (b).
We introduce dual-stream architecture with Multi-View Conditioning and LayeredRefAtt tention modules address the challenges in VFTON.
Both quantitative and qualitative experiments validate the superiority of our approach.
2
Related Works
Image-based Virtual Try-on. The virtual try-on task is concerned with synthesizing images of person donning the designated garment with appropriate fit (21), while retaining salient characteristics of the original garment and person, given a pair of images depicting person and a target garment.
To execute this task, numerous works 04 26. 36, 4 M 20) have utilized Generative Adversarial
Networks (GANs) (17) with two-stage strategy (37 16 66): (1) warping the clothing to the desired shape (5) 40) and (2) fusing the deformed clothing via try-on generator based on GAN. As significant progress in Text-to-Image diffusion models (22) 47 25 is witnessed in recent years, some works 6
29 38) have been motivated incorporate pre-trained diffusion models (55) 52)
as priors into virtual try-on task. Most recently. OOTDiffusion (65). IDM (9, and MMTryon (75)
achieve garment feature extraction with parallel U-Net and feed them through self-attention for enhanced integration. CatVTON (10) concatenates the garment and person images along the spatial dimension and feed them into a single UNet. Unfortunately, these methods serve fabric clothing, intrinsically lack support for faithful try-on to 3D multi-view shoes with various materials, exhibiting severe color contamination from the background.
Controllable Diffuusio Models. To attain conditional control in diffusion models, T2I-Adapter (46) and IP-Adapter (69) incorporate additional trainable modules fuse condition features. Additionally, some investigations utilize various prompt engineering techniques (39 68 73) and implement cross-
attention constraints 64 74) to facilitate more controllable generation. Recently, the release of
FLUX (33) and SD3 (15) has sparked renewed interest in DiT architectures (51) with subsequent conditional frameworks such as Flux.1 Fill (44 and Flux. Redux (35) achieving inpainting and object injection capabilities through enhanced encoder designs. However, these methods remain constrained by coarse-gres coarse-grained conditioning mechanisms, which fail to ensure generation paatiityapptiuuarrlyy in VFTON tasks with high geometric variability and stringent fidelity requirements.

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3 MVShoes Dataset
We collect the first footwear try-on dataset which contains a total of 7305 annotated high- high-resolution image triplets, each consisting of two product shoe images from different views, and corresponding try-on result. The dataset features diverse shoe categories and includes foot-specific pose landmarks and shoes-agnostic masks to facilitate precise alignment.
3.1 MVShoes Collection
All images sourced from the Internet and include diverse range of model scenarios such as full-body models, half-body models, foot close-ups, as well as a comprehensive variety of shoe categories, effectively meeting practical application needs. Initially,
collected approximately 25,000 raw shoe product image sets, each consisting of to 5 images per shoe, including standalone product images and images of people wearing the shoes from different perspectives.
Data Processing. We first employ Qwen- VI. (3) to differentiate between product footwear images (without humans) and try-on images featuring human models. Subsequently,
use the CLIP model (53) to extract image features and set a similarity threshold to eliminate duplicate images within the same raw image set. Next, segment the shoe region in each image, match the DINO (43) features and assess their similarity to filter those data pairs with different footwear colors and styles. This process results in the formation of preliminary human-footwest try-on triplets, followed by manual filtering to eliminate any errors or oversights. We then remove visually blurred images and compile statistics shoe categories and try-on scenarios. Finally, following the method proposed in DWPoss Posse (67) and Grounding DINO (43), we extract foot poses and shoe-agnostic masks. This process culminates in the creation of high-resolution, calegory-comprehensive try-on triplets. The data processing pipeline illustrated in Fig. (b).
3.2 Dataset Statistic
Rich Scenes. In conventional VTON tasks (37) 12], model images typically consist of full-body or upper-body views. However, given that footwear occupies relatively small proportion of the body in VFTON tasks, full-body and half- body models are less effective showcasing shoe details. To address this, we simulate the distribution of footwear model scenes observed on e-commerce platforms and curate 4 model scenarios: top-down foot close-up, horizontal foot close-up, half-body model, and full-body model, as illustrated Fig. 3 where horizontal foot close-ups constitute the largest proportion, In such cases, accurate preservation of side de- tails is particularly challenging, especially when compounded by viewpoint misalignment.
Sippoers
Sandala
Libee cesicd
Chelue loot
High tuczs
Topt deve
foot
Morbeontal
Casual shows
Htlli body
Hight breel Afoiic
Canver
Allkano
Lady candt
Diverse Footwear Categories. We also prior- itize the comprehensiveness of footwear cate- gories. By annotating shoe attributes (2), we establish two-level product category label set and ensure that its distribution aligns with ev- eryday use, as depicted in Fig. Notably, we observe that environmental lighting often affects the visual appearance of footwear, where reflections introduce background color contamination, particularly in categories such as boots, leather shoes, and high heels.
Figure 3: The category distribution of MVShoes.

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Existing pipeline for image conditions: integrate image conditions concatemation
(b) Our proposed duall-img image-stream ShoeFit with Multi-viww
mismsiches background puticies, thereby prevencing color conlusion.
Conditioning Layaned Module
Figure 4: We illustrate existing and our DiT framework comparison in (a)-(b), and heatmaps visual- ization for component validation of Multi-view Conditioning and LayeredRefAttention in (c)-(d).
4 Method
An overview of the ShoeFit is presented in (ig. Given a human image X and target shoe images Cf ShoeFit is aimed to generate an authentic try-on image. The backbone of ShoeFit employs the FLUX.I-Lite (33), with a customized dual-image-street
architecture in Sec. 4.1 denoising image stream and a copy of it as as conditioning branch to replace text modality. The features of human images and multi-view shoes are then integrated through Reference Concatenation and LayeredRefAttention modules, which are described in Sec. 4.2 and 4.3
4.1 Dual-image-stream DiT Framework
The original FLUX (33)
text-to-image model composed of a series of stacked MM-DiT blocks. Farlier works (60 61 directly concatenate mmage/Text condition tokens with noisy image tokens such as [T;C;; Cr relying on a single denoising stream to finish condition extraction and denoising, while the other deal with text modality, as shown in Fig, a). However, for VFTON, the generated try-ons are primarily determined by the given shoe images rather than limited semantic prompts. Thus, we remove the text stream from FLUX, achieving approximately 40% parameter savings, including those for T5 (54) and CLIP models (53). Instead, we replicate a portion of the denoising stream's s architecture and weights to initialize the conditioning image stream, establishing dual- image-stream framework. This design decouples feature preservation of shoe details from the high-quality generation process, allocating additional effective model capacity to enhance overall fidelity performance compared to direct token concatenation approaches (60) bT].
4.2 Multi-view Conditioning
In VFTON, the information about shoes from the try-on view remains incomplete due to view mis- match and 3D-to-2D projection occlusion (63). The inherent misalignment between source product images and target human images leads to unfaithful and physically implausible try-on results. Ob- serving that e-commerce platforms often provide multiple-view product images (e.g., front, side, top views), we introduce multi-view shoe image dataset and propose a multi-view conditional injection mechanism via Reference Concatenation and 3D RoPE. The additional perspectives comprehensively
5

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encompass more aspects of the shoe, as illustrated in Fig. 4 (c), significantly reducing uncertainty during generation and delivering high-fidelity and commercially viable synthetic results.
Reference Concatenation. Inspired by CatVTON (10), we adopt a simple yet effective multi-view injection strategy: concatenate multi-view conditioning shoe images along the spatial dimension as: Craat = [C7, C.....I. E RxxN-ixe, where bs the batch size, denotes the number of image tokens per conditioning image, and e is the feature channel. The concatenated multi-view latent Cgat is then fed into the conditioning image stream for feature extraction and interaction with the target human image. Concurrently, the denoising stream receives concatenated components with masked image
16 + 16 + 1 + 16 channels: the noisy human latents X,, the latent E(Xmasked). the resized agnostic mask Xm- and the foot pose landmarks pXXpose), where E-)) represents VAE (30) encoding. The effectiveness of Reference Concatenation stems from the global attention interactions in attention layers. Intuitively, during the attention, each Query patch in the human image accesses and matches the most semantically relevant Key- Value pairs from the full set of concatenated multi- view product features, as illustrated in Fig. 4 (c). As a result, our method ensures consistent alignment between all regions in the target pose and their corresponding details in the product images, thereby achieving high-fidelity virtual try-on.
3D RoPE. However, naive Reference Concatenation introduces position misalignment in the relative positional relation of conditioning images. FLUX employs an essentially 2D RoPE scheme (58) to encode spatial relative relationships along the height and width dimensions, where the inner product in attention depends solely on the token embeddings I'm Zn and their relative distances m n
(fg(Im, m), f& (xn, m)) g Fn, m n).
When multi-view conditioning images are naively concatenated, discontinuous semantic jumps emerge at the concatenation boundaries, where tokens with small relative distances may correspond to semantically distant regions (i.e., different views of the footwear).
To address this issue. we extend the 2D RoPE to three dimensions. Specifically, EposID = concat D: E Ep Ey E #=#xxx3 where the first channel EjpnsD serves as index mask to separate different conditioning views, while the second and third channels E E encode spatial positions within each image along the height and width dimensions, respectively. Within each channel of F E/oal"" frequency encoding is performed given base frequency of f = 10000.
Wd Pad/D for o,.,... D 1, V(n.hw)) postD -Wd IR xx (D.. (2)
where parammeesss Dn, Dns Dw is predefined as 16,56, 56, 56. Subsequently, we obtain positional rotation matrices RID for each of the n, h, w channels internally. By concatenating these matrices
along D dimension, we construct 3D RoPE matrix RAD Blxx (D.+D) +D++ Dw) x2x2 as follow:
R km cos(P(m,A,.)) sin ((
sin("(,,,...)) costterrbbu)))
1DD
RaD = concat RRip: R RyDl
(3)
4.3 LayeredRefAttention Module
We observe that the appearance of the synthesized shoes is highly susceptible to contamination from the background colors of the conditioning images. This phenomenon arises from the complex reflective material of footwear interacting strongly with environmental lighting, termed Background- induced Color Distortion. Specifically, the issue arises when Query patches from the human image inadvertently match key-value pairs originating from the background regions of the conditioning image, as illustrated in Fig. (d). A naive solution involves masking out background regions during attention, but this would neglect critical illumination cues embedded in the background, which are essential for accurately inferring the shoe's 's intrinsic appearance and texture (32).
To address this challenge, we propose a layer-aware attention module called La Rettttttioonn to explicitly differentiate between background and foreground content in the conditioning images, as illustrated Fig. (b). By leveraging background features to modulate foreground shoe features, our method decouples the intrinsic shoe appearance from its background context (28). Subsequent denoising stream attention computations are restricted to interactions between the foreground shoe content and the target human image, effectively climinating background illumination effects while

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Table 1: Quantitative comparison MVshoes. ShoeFit significaly surpasses all baselines.
View Num.
Method
SSIM f
LPIPS 4
FID 4
KID 4
DISTS
FLUX.1-Fill 34
0.725
0.201
24.44
2.221
0.1293
FLUX.I-Fill--E
35
0.724
0.199
24.67
2.086
0.1274
1-View
OOTDiffusion-SDXL (65
0.724
0.194
22.60
2.345
0.1114
CatVTON-Flux-Lite 10
0.745
0.185
22.71
3.327
0,1179
ShoeFit-IV
0.772
0.159
21.07
0.944
0.1059
CaIVTON-Flux-Lite--2 110 ShoeFit-2V
0.756
0.171
22.64
3.318
0.1040
2-View
0.780
0.149
20.18
0.485
0.0941
avoiding background color contamination. Specifically, for (1,..., N} conditioning view c( we employ suneere-and-Fxciien (SE) block (24) followed by global average pooling over spatial dimensions to compute channel-specific weights:
p(n) AugPool RR
Subsequently, we employ two linear layers F(-) to extract background modulation parameters and modulate foreground shoe features, filtering irrelevant environmental lighting and background reflection to ensure faithful material preservation of shoes
Bladter
aim) shift F(P())),
c(a) fg = (1 + simdle) ) (LN(C))) @ @M/ + Shifee
(S)
where (.). means layer normalization and (()) represents the binary shoe masks for conditioning
product image. The attention variables {K, V V) conditioning feature concatenated with the corresponding (Q, K, V Fing from human image for subsequent denosing-strean attention:
(n) extracted from N views of modulated foreground
C Kin))
V.
QKV(C)O
(6)
Qimg concat Concat(Kimp))
K.....NTT C.ff
Allmimagee softmax(
concat(V V.....
Vd
Meanwhile, the conditioning-streamm branch performs attention computation using unmasked condi- tioning latents as:
Kob N,T )) V..... (8)
da K v(( QKV(LN(d)'),,
Attnc softmax
Vd
To clarify our contributions, we omit the distillation strength normalization and final residual con- nections in the attention layer of FLUX. 1-Lite (33) that remain unchanged from the original imple- mentation. In summary, LayeredRefAttention effectively enables by modulating foreground shoe features recover intrinsic color properties, resulting in texture- preserving footwear try-on synthesis, as illustrated in Fig. (d).
decoupling
Experiments
5.1 Experimental Setup
the backbone and customize a dual- image-stream architecture as Sec. Our experiments are carried out on MVShoes resolution of 768 x 768, with 6305 pairs for training and 1000 pairs for testing. The model is trained for 7 days on 88GBBAA 100 GPUs with DeepSpeed (I) ZeRO-2 to reduce memory usage, at a batch size of All parameters of DiT are trainable, using an AdamW optimizer (44) with a constant learning rate of 3e inference time, we run ShoeFit on single A10 GPU for 25 steps. The data augmentation follows the same protocol as in STaN (29). Please refer to Appendix for more details.
Implementation Details. We employ the FLUX.1-Lite (33)

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buman foct
product shoes
ShocFit (Ours)
CaIVTON
OOTDifiuson
FLUX.1- F-I- Redux
FLUX.I-FII
(1) Accurate
(2) Background decoupling
(3) Diverse pose
Figure 5: Comparisons on the hard scenes, where ShoeFit delivers high-fidelity and rubost results.
Conditions
Ablotion
Full
Conditions
Ablationn
Fulli
Conditions
Ablution
Full
Duak waa.iiigg--ttoooo framework
M)Mtttrieww LaveedRee Condiining
Figure 6: Ablation study. We highlight the improvements by all three components of method in red.
Evaluation Protocols. Following evaluation in prior VTON works (45). measure reconstruction accuracy by LPIPS (72), SSIM (62) and DISTS (13) in paired setting given ground truth images, and authenticity of unpaired synthesized images by FID (50) and KID (59) without ground truth.
Baselines. As a subtask of inpainting. we first select two widely used inpainting FLUX variants as baselines: FLUX.1-Fill (34) and FLUX. 1- 1-Fill-Reduu (35). The former involves concatenating product images with human images on the spatial dimension, similar to CatVTON (10). The latter enhances the conditioning stream with SigL (70) to incorporate product shoe images. Additionally, we compare ShoeFit to two state-of-the-an methods the VTON field: OOTDiffusion (65) based on the SDXL (52) and CaNN (10) built upon the FLUX.I-Lite All models are implemented as

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originally deployed for single-view product shoe conditional generation. Considering CatVTON's capability to easily extend to multi-image injection, we further train version of CatVTON to simultaneously inject two product views, the same as ShoeFit, denoted as CarTTON-FI.U--Liee22 Please refer to Appendix for more implementation details.
5.2
Qualitative Results
Fig. 5 provides a qualitative comparison between ShoeFit and the baselines on MVShoes, addressing challenges such as complex structures, background interference, and difficult target poses. For fair comparison, we report the results of the single-view conditioning version of all methods. Notably, observe that OOTDiffusion (65) tends to exhibit extreme color variations, while FLUX.I-Fill- Redux (35) and FLUX. I-Fill (34,, possibly affected by the limitation of only LoRA (23) being trainable (1=64), often produce visually plausible results that do not strictly adhere to the input conditions. In contrast, ShoeFit accurately generates structural details of the shoes (rows 1 and 2), decouples environmental lighting (row 3), avoids background confusion (row 4), and demonstrates robustness to uncommon poses (row 5). Please refer to the Appendix for more results.
5.3 Quantitative Results
For a fair comparison, we report the results of one/two product view conditioning generation separately in T.b. [1 FLUX.I-Fill-Redie (35) and FLUX.1 1-Fil (34) achieve the worst FID and DISTS scores,
while OOTDiffusion (65) and CatVTON (10) show complementary performance in unpaired and paired evaluations. ShoesFit, benefiting from the additional specialized parameters provided by the aall-mmaee-stean DIT framework and background decoupling enabled by the aayeredRefAttentinn significantly surpasses all baselines under both single-view and multi-view conditional generation.
5.4
Ablation Study
To validate our technical contributions, we define a Vanilla Model that uses I-Lite as the backbone, deploying both text-stream and an image-stream as in Fig. (a). Multi-view Conditioning and LayeredRefAAtention Module are removed, using only one footwear image as conditioning.
Dnal-image-stream DiT Framework. Since the text-stream only provides high-level semantics, its utility is limited in VFTON, which demands high image fidelity. Therefore, we first remove the text-stream and replicate portion of the denoising stream's is architecture and weights to initialize the conditioning image stream, effectively decoupling the conditional feature extraction from the denoising process, while keeping the Vanilla Model's 's other settings unchanged(denoter as "+ D-i-s"). As shown in Tab. and Fig. the vanilla model often generates artifacts and incorrect structures facing high-frequency textures due to the limitation in effective network capacity and the weakness in coupling of conditional images and noise. This demonstrates that the Dual-image-strean DiT framework plays fundamental role in enhancing the model's ''s expressive capabilities.
Lay ttention Module. Observing the visual contamination from background and environ- mental lighting, we further integrate the LayeredRefAttention into the "+ D-i-s" version, denoted as "+ D-i-s + LRA", which effectively decouples the intrinsic appearance of the shoe from its surroundings. As illustrated in Tab. and Fig. 6 the model without LayeredRef/Atenntii reproduce the natural colors of the shoe where environmental lighting or shadows of product images are prominent, resulting in outputs that are mixed with environmental light shadows. Additionally, mismatches with background patches in Attention can introduce colors confusion from the back- ground content, such as slippers being contaminated by floor colors in Column 3 of Fig. 6 In contrast, the one with "LRA" effectively avoids such color contamination, delivering superior try-on results.
often fails to accurately
To investigate the efficacy of simple background masking, we conducted an ablation study utilizing standard attention while removing the background from the product shoe images. Visually, this naive approach exacerbates color artifacts, particularly for highly saturated hues. We hypothesize that this occurs because removing the background discards crucial illumination cues embedded in the product image, which are essential for the model to infer the shoe's intrinsic material properties and texture. Given that the training set contains inherent color variations between in-studio product shots and on-foot images due to disparate lighting, forcing the model to operate without background context introduces lighting ambiguity. Consequently, the model struggles to maintain color constancy, leading to degraded try-on performance and reduced visual fidelity.

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Table 2: Quantitative ablation study of cach component in Section of Method.
Method
SSIM f
LPIPS 4
FID 4
KID +
DISTS 4
Vanilla Model
0.745
0.185
22.71
3.327
0.1179
D-iiss
0.761
0.174
21.57
1.493
0,1038
D-i-s Standard Attn (w/ mask)
0.756
0.181
21.70
1.698
0,1056
D-i-s LRA (w/ mask)
0.772
0.159
21.07
0.944
0,1059
D-i-s LRA + MC (wo 3D RoPE)
0.778
0.152
20.18
0.695
0.0971
D-i-s LRA MC (Full)
0.780
0.149
20.18
0.485
0.0941
Multi-view Conditioning System. While a single source product view often fails capture the complete texture footwear, try-on results can benefit from multiple source views, thereby effectively reducing uncertainty in generation. Thus, introduce the Multi-view Conditioning pipeline using Reference Concatenation and 3D RoPE in the "+ D-i-s + LRA" version, denoted as "+ D-i-s + LRA + MC". As demonstrated in Tab. 2 and Fig- 6 the model without "MC", which uses only one product shoe image as input (yellow box), often generates unrealistic speculations and artifacts in unseen areas due to insufficient conditional information. In contrast. ShoeFit benefits from the Multi-view Conditioning system, producing details that are more faithful to the input across different views. Furthermore, also conduct an ablation study upon 3D RoPE's impact in Tab [ demonstrating the performance gain we achieve using the proposed RoPE without incurring any additional computational overhead. Please refer to the Appendix for more ablation results.
5.5 Inference Time and Memory Usage
We evaluated the efficiency of our proposed enhancements by measuring inference time and memory footprint NVIDIA H20 GPU (with batch size of I, 25 steps, and forch.bfloat precision). The results, presented in Table 3 reveal that the ShoeFit model with our Dual-image-streds architecture substantially outperforms CAT-VTON in both speed and memory efficiency. This stems from removing the text stream and its computationally expensive T5 and CLIP encoders.
(D-i-s)
Table 3: Comparison of inference time and GPU memory usage across different architectures.
Method
Inference Time per Image
GPU Memory Usage
Cat- (image text streams) ShoeFit (Dual-image-streanl D-i-s) ShoeFit D-i-s LayeredRefAAtention
22.27 seconds
35.5 GB
17.44 seconds
22.0 GB
21.15 seconds
28.5 GB
Conclusion
In conclusion, we formally define the VFTON problem and curate the first shoe try-on dataset, MVShoes, containing 7305 pairs of high-quality multi-view sorment-moded samples. Our proposed framework addresses the critical challenges of viewpoint misalignment and background induccdddd color distortion by leveraging a dual-stream architecture with Multi- View Conditioning and Layere- dRefAttention modules. Through comprehensive evaluation on the proposed MVShoes dataset, we demonstrate significant improvements in detail fidelity and texture consistency across diverse scenar- ios. This indicates that our method effectively mitigates background interference while preserving fine-grained shoe details across views. establishing strong bascline for future VFTON research.
Broader impacts. With the ability to synthesize images, arises the risk that ShocFit might be used for inappropriate purposes such producing media that breaches intellectual property rights or privacy norms. Because of these risks, we strongly advocate for the conscientious use of this technology.
Limitation and future work. ShoeFit demonstrates substantial improvements in generating high- fidelity footwear try-on images across diverse scenes. However, certain limitations persist. Firstly, the model occasionally struggles with accurately rendering small logos and intricate text, particularly critical for e-commerce sellers. Secondly, we hope to introduce explicit 3D geometric and material priors in the future achieve more robust multi-view representation and refined visual fidelity.
10

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Acknowledgements
This work is supported by the Science and Technology Commission of Shanghai Municipality under research grant No. 352R1401187.
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In Section we provide a more detailed discussion about the limitations of ShoeFit.
Section we provide additional details about the MVShoes datasets, including data processing and data statistics.
In Section C
we provide preliminary knowledge about the FLUX 33 model, as well as how LayeredRefAttention modules are applied Single Stream DiT Block.
In Section D we provide additional details about the experimental setup, including baseline training, data augmentation, and hyperparameters.
In Section|E] we present a multitude of ShoeFit generated images, including more ablation results, more comparisons with the baselines, with additional results displayed in challenging scenarios.
A Limitations and Future Work
We propose ShoeFit, dual-stream DiT frame- work that addresses the critical challenges of viewpoint misalignment and background- induced color distortion in VFTON by Multi- View Conditioning and LayeredRefAttention modules. However, certain limitations persist. Firstly, similar to the common constraints faced by existing generative models, the model oc- casionally struggles to accurately render small logos and intricate text due to their small portion images and high-frequency variations. We lustrate this limitation in Fig. [ These small logos are particularly critical for e-commerce sellers as they often convey brand information that consumers care about. Therefore, we plan to develop detailed supplementary condition- ing method in future research to pre-detect and enhance the injection of such patterns, funda- mentally addressing this issue. Secondly, we aim introduce explicit 3D geometric and ma- terial priors in the future to achieve more robust multi-view representation and refined visual fidelity.
Aipas Gaawen fort
Figure 7: Similar to the common constraints faced by existing generative models, ShoeFit occasion- ally struggles to accurately render small logos and intricate text due their small portion in images and high-frequency variations.
B MVShoes Dataset Supplement
In the main body of the paper, we provide a brief description of the data processing pipeline and a rough visual presentation of the dataset statisties terms of Rich Scenes and Diverse Footwear Categories due to space constraints. In this section, we offer more detailed information on the data processing pipeline to ensure reader comprehension. Additionally, we present the specific distribution statistics of MVShoes in quantitative table format.
Data Processing Details. Given dataset comprising raw images of various shoes and human models, we employ Qwen2.5-VI. 3 which operates with 7 billion parameters, to distinguish between footwear images and human model images. The prompting framework utilized is as follows: "Analyze the provided image and determine whether it depicts model image shoe image. A model image is defined as one that portrays a human model's lower body or legs adorned with shoes, while shoe image solely comprises images of shoes without any human presence. Assign value of 'I' it qualifies as a model image and '0' if does not."
Following this classification, we extract image features from the shoe images using the CLIP (53) model, implementing a similarity threshold of 0.9 effectively eliminate duplicate images within the dataset. Subsequently, we perform segmentation by SAM (31 to isolate the shoe regions in each image, aligning the DINO (49) features to filter out shoes-model data pairs exhibiting inner similarities greater than 0.8.

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This procedure facilitates the initial construction of human- footwear try-on triplets, which are then subjected manual filtering address any potential errors or oversights. Additionally, we exclude visually blurred images and compile statistical analyses pertaining to shoe categories and try-on scenarios. In accordance with methodologies established in DWPose (67) and GroundingDINO (33, we extract foot poses and shoe masks, ultimately resulting in the generation of high-resolution, calegory-comprhhensiee try-on triplets. All models referenced above are open-source, and their respective URLs are provided as follows:
Qwen2.55 (): http://angginnggecccooveeeeeeeee
SAM 31
CLIP (53
https//ggiibb.ccm//peem///LPPP
DINO 49
DWPose http://ggttbbbooomm//Daaaassssss
Grounding DINO http://gghaoooao////EEEttttttttt
Dataset Statisties. The dataset features diverse shoe categories and rich human scenes, We present the quantitative distribution statistics MVShoes Tab. and Tab. We also provide more visual samples from MVShoes datasets in Fig.
Pose Estimation for Shoe Try-On. Traditional open-source pose estimation methods (e.g., Open- Pose, DWPose) in VITON pipeline, struggle capture lower-body/foot poses VFTON, which often only include partial leg shots or full lower-body views. To address this, we fine-tune the dwpose model on specially collected datasets containing diverse lower-body and close-up leg poses. This enables our model to generate accurate pose descriptions for various leg postures, significantly enhancing its generalization capability for shoe-related applications.
Generalizable Foot Mask Design. Given the diversity of shoe types (e.g., sneakers, boots, sandals) and their varying leg coverage, we propose a leg- beg-pose-aware mask generation strategy. Unlike VTON's body-centric masks, method combines leg pose information with bounding box expansion from shoe detection to create adaptive masks. This allows seamless cross-shoe category try-on capabilities, for example, model wearing long boots flip-flops can be transformed into sneakers while maintaining anatomical alignment and realistic occlusion. As illustrated the first row in Fig. II this approach lays the foundation for robust, universal shoe try-on systems.
However, our mask generation strategy is not without its limitations. A failure case is presented in the second row of Fig. bb).. Here, the region of the rear shoe is both heavily occluded and minimally visible, which leads the failure of our keypoint and shoe detection modules. Consequently, this rear shoe area is erroncously classified background, and only the front foot is designated as the inpainting region. This incomplete mask ultimately results in an unsuccessful virtual try-on. We contend that this is an inherent challenge for all methods reliant on explicit masking strategy, and developing more robust solutions to handle such severe occlusion cases remains an important direction for our future work.
Shoe Expansion Phenomenon. The volumetric expansion of footwear observed during the try-on process naturally occurring phenomenon. In real-world datasets, is common to find that shoes with stiffer tongues tend to bulge outwards when worn, resulting in slight expansion of the shoe's 's overall volume. This effect illustrated in Fig. 5 where the third column clearly shows prominent bulge in the tongue area, leading to body expansion. This expansion occurs stochastically real-world data pairs, as exemplified the third row of Fig. [[]. where the shoe upper in second column exhibits a slightly thicker contour. Consequently, by effectively fitting to the training data distribution, our model learns to replicate this real-world phenomenon, accurately rendering the corresponding deformations inherent to the try-on process.

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Table 4: Shoe Category Statistics.
We report the sample count for each subeategory, with its
proportion indicated in parentheses.
Primary Categories
Subcategory
Casual Shoes: 3158 (43.23%)
Lifestyle Casual Shoes: 803 (10.99%)
Sneakers: 1428 (19.55%)
Canvas Shoes: 141 (1.93%)
Children Casual Shoes: 99 (1.36%)
Men Casual Shoes: 687 (9,40%)
Athletic Shoes: 558 (7.64%)
Running Shoes: 322 (4.41 %)
Basketball Shoes: 121 (1.66%)
Training Shoes: 45 (0. (0.62%)
Football Shoes: 70 (0.96%)
High-Heel Shoes: 895 (12.25%)
Classical High-heel Shoes: 340 (4.65%)
Ladies Casual Shoes: 401 (5.49%)
Mary Jane Shoes: 154 (2.11%)
Boots: 1045 (14.31%)
Ankle Boots: 416 (5.69%)
Chelsea Boots: 56 (0.77%)
High Boots: 153 (2.09%)
Snow Boots: 183 (2.51%)
Martin Boots: 237 (3.24%)
Sandals: 279 (3.82%)
Flip Flops: 183 (2.51%)
Beach Sandals: 36 (0.49%)
Strap Sandals: 60 (0.82%)
Dress Shoes: 389 (5.33%)
Loafers: 209 (2.86%)
Formal Leather Shoes: 180 (2.46%)
Slippers: 981 (13.43%)
Clogs: 82 (1.12%)
Thong Slippers: 18 (0.25%)
House Slippers: 881 (12.06%)
Table 5: Human scene statistics.
Scene Types
Number of samples
Percentage
Top-down Foot
1719
23.53%
Horizontal Foot
4330
59.27%
Half-body Model
833
11.40%
Full-body Model
423
5.79%
C Method Supplement
C.1 Preliminary
FLUX.1 Our ShoeFit is an extension of Stable Diffusion (55) and FLUX.1 1 (33 which are the most commonly used text-to-image diffusion models based on Flow Matching and DIT(5)). FLUX.1 (33) employs variational autoencoder (30) (VAE) that consists of an encoder & and decoder enable image representations the latent space. is also equipped with rotary positional embeddings (RoPE) (8) and denoise-fext-strean attention layers to improvement performance. FLUX.1 1 implements an actual two-dimensional RoPE scheme for encoding spatial positions the
latent space:
Wp O2d/D forD = D/2-1
(9)
where 0 is typically set to 10000. The position encoding applies rotation matrix:
cos(wa pos)
-sin(wa pos)
(10)
sin(wa pos)
cos(wa pos)
This rotation is applied to query and key vectors in the attention mechanism, enabling the model to capture relative positional relationships in the latent space.

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Flow Matching Flow matching (41) aligns the flow of information between noise and data distributions by optimizing a velocity field uy which progressively converts noise into data over time. This technique ensures that the generative model maps the noise distribution to the actual data distribution structured manner. The text encoders (53) To are employed deal with the given text prompt The flow matching loss is defined as follows:
L = (e (2, ro(U)) (z1e)))
(11)
In this context, ve(2,1, I, (y)) signifies the conditional velocity field determined by the weights of the neural network, while (=(=)e) represents the vector field created by the model to delineate the probabilisticc trajectory between the noise and actual data distributions. The symbol E stands for the expectation, which involves either integration or summation over time t, latent variables conditions y, and noise e. This expectation computes the mean the squared differences for all conditions, ensuring that the model's performance is evaluated over numerous instances to yield a dependable estimate of its generative capability.
C.2 LayeredRe[Attentiinn in Single-stream DiT Block
The original FLUX is text-to-image model composed of series of stacked MM-DiT (double- stream) blocks, followed by a series of stacked single-stream DiT blocks. Due to space constraints, the framework of the LayeredRefAttentee the double-stream DiT blocks. Here, we provide how it is used within a single-stream DiT block Fig. TO Similar to the way in double-stream blocks, we employ a Squeeze-and Exxeiittiooii (SE) block (24 followed by global average pooling over spatial dimensions to compute channel-specific weights:
module shown in the main text its structure within
p(n) AugPool RxC
(12)
Subsequently, we employ two linear layers F(-) extract background modulation parameters and modulate foreground shoe features, filtering irrelevant environmental lighting and background reflection to ensure faithful material preservation of shoes as:
Bla) g(n) shift F(p(")), C (1 + plml (LN(C))) + M + Shafee:
(13)
where LN).) means layer normalization and / represents the binary shoe masks for conditioning product image. The subsequent operations are then performed as regular single-stream DiT blocks.
D Implementation Supplement
D.1 Baseline Training Details
We here provide detailed descriptions of the training processes for the baseline models, focus- ing specifically on the training configurations. All models are trained on MVShoes at a res- olution of 768 768, utilizing 6305 pairs for training and 1000 pairs for testing.
Middee
Nore
Yosille
Halaraad
For Flux.1 Fill (34, we implement the train- ing and inference pipeline by concatenating footwear images with human model images. In the trainable components, we apply LoRA (23) with rank of 64 all attention modules in the model and a Flux ControlNet (71) comprising single layers and 6 double layers, injecting the poses at every denoising step. The model is trained for 6 days on 8 80GB-A 100 GPUs using DeepSpeed ZeRO-2, with a batch size of 4. We utilize the AdamW optimizer, setting a constant learning rate of 3e-5 for training, and operate
Ang Peil
with aepeatiictiiietii
Figure 10: We provide how LayeredRefAttention 4s used within single-stream DiT block.

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the model on single A100 GPU for 25 steps during inference.
For Flux.1 1 Redux 35,, we adopt the same train- ing settings those used for FL.X..1-FilL. Ad- ditionally, we incorporate two linear layers for projecting SigLip (70) features of the product shoe images, training these two layers concurrently with the attention LoRAs and ControlNet. The parameters of SigLip remain frozen throughout the training process. We retain the same inference settings as employed FLUX.1-Fill.
For OOTDiffusion (65), we execute the training and inference pipelines based on the official code. We also implement ControlNet to facilitate pose injection. The model is trained for 6 days on 80GB-A 100 GPUs with DeepSpeed ZeRO-2, at a batch size of 8. During inference, the model is run single A10 GPU for 25 steps.
For CatVTON- FEION-FFlx--i Litx--Lite-aee Lite-2V, we maintain the text stream for FLUX-lite and concatenate footwear images with human model images. The model undergoes training for 7 days on 12 80GB-A 1100 GPUs utilizing DeepSpeed ZeRO-2, with a batch size of All other training and inference configurations align with those of ShoeFit-2V.
D.2 Data Augmentation
We have implemented data augmentation techniques that could potentially enhance the model's generalization ability as well as its fidelity performance. Specifically, the data augmentation operations include (a) horizontal flipping of images, (b) resizing footwear and human figures through padding (up to 10% of the image size). (c) randomly adjusting the image's s hue within range of -5 to +5, and (d) randomly adjusting the image's contrast within a specified range (between 0.8 and 1.2 times the original contrast). Each of these operations occurs independently with 50% probability. Moreover, these operations are simultaneously applied to both the footwear and model images.
D.3 LayeredRefAttention Hyperparameters
In the LayeredRefAttenntio layer, we primarily introduced new Linear layer and an SE block. The Linear layer for the foreground follows the same dimensions as other linear layers within the module, which is the hidden size. The SE block takes input features with the same dimensions of hidden size, and internally, we use compression rate of reduction = for SE Block, as Linear (chanmels,,,, chonnels/4) ReI.U(), Linear(channels/ /4, channels).
E More visual results
Fig.
9 provides more results about the ablation study. We highlight the improvements by all three components of the method in red.
Fig. [T provides more results on MVShoes for comparisons between the baselines and ShoeFit. For a fair comparison, we report the results of the single-view conditioning version of all methods. Our method substantially improves rendering fidelity and robustness under challenging real-world product shoes, establishing new benchmark in high-fidelity footwear try-on synthesis.
Fig. 12 provides more results on MVShoes for inspection to demonstrate that ShoeFit synthesizes high-fidelin and dethil-faithful try-on results.
Fig. 13 details two key limitations of our model. First, in the early training phase, the model has not yet mastered spatial reasoning, resulting in structurally distorted outputs (row (a)). Second, generating precise masks for tall footwear remains a challenge. As shown in the row (b), inaccurate masks for items like boots can cause significant visual artifacts or fitting errors.
Fig. 14 illustrate more results on dataset. The high-quality try-on results demonstrate the model's notable stability and strong general- ization capabilities.
(OOD) samples not present in the MVShoes
5

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Human feet Agnostic mask Pose image
Multi-view product shoes
Multi-view product masks
Figure 8: We provide more visual samples from MVShoes datasets.

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Conditions
Ablation
Full
Conditions
Ablation
Full
a) Dual-image- wawiiiaaag-erraamnmm DiT framework
FLL
RARIC
Jeep
b) LayeredRe AAttention Module
c) Multi-view Conditioning
SURD
Figure 9: More visual results of the ablation study. We highlight the improvements by all three components of the method in red. Best viewed when zoomed in.

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Input
Input product shoes
ShoeFit (Ours)
CaIVTON
OOTDiffusin
FLUX.I-FiIL-RR F-Fi--Reuuu
Figure 11: More visual comparisons on the MVShoes by ShoeFit, Best viewed when zoomed in.

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Conditional shoes
Imput human
Generated results
Conditional shocs
Input human
Generated results
ASG
Elmos
Ebuo
Figure 12: More visual results on the MVShoes by ShoeFit. Best viewed when zoomed in.

---

Conditional shoes
Generated results
Conditional shoes
Generated results
Conditional shoes
Generated results
(b)
Figure 13: More visual results on two primary failure modes of our method: (a) structural failure observed during early training stages, and (b) artifacts arising from inaccurate masks in long footwear scenarios.
Conditional shoes
Generated results
Conditional shoes
Generated results
Figure 14: More visual results on out-of-distribution (OOD) samples not present in the MVShoes dataset. The high-quality try-on results demonstrate the model's notable stability and strong general- ization capabilities.
10


Reviews:
[{"review_id": "kCXLZMVdXB", "reviewer": "Reviewer_NYQo", "summary": "This paper explores a new task of virtual footwear try-on (VFTON). To address the challenges of data scarcity, viewpoint misalignment, and background-induced color distortion in VFTON, this paper first establishes a new dataset called MVShoes, which comprises 7305 annotated high-resolution image triplets, each containing two product shoe images from different views and a corresponding try-on result, then proposes a new dual-image-stream DiT framework (named ShoeFit) by using Multi-View Conditioning and LayeredRefAttention modules. These modules enhance the fidelity of try-on results by managing complex lighting interactions and preserving high-quality texture details. Both quantitative and qualitative experiments validate the superiority of ShoeFit in generating high-fidelity and robust try-on results.", "questions": "My questions are mainly about the weaknesses listed above, i.e., \n1. Personally, I don't think the VFTON is more challenging than the VTON of clothing, especially for loose and unrigid clothing. Moreover, the dataset consisted of 7305 image triplets is not large, which may limit the performance of VFTON, degrading the contribution of this work.\n2. Some visualization results of the proposed method are distorted, such as Figure 5(3) and the second example in Figure 6(b). As its limitation says, for some special texture materials, it is difficult for this method to generate the same results as the input. I think this may be because the dataset is not large or rich enough.\n3. Can the proposed method perform well on other public datasets, such as some images from DeepFashion that are suitable for VFTON?", "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. This paper pioneers a new task of VFTON, creates a new shoe dataset, and proposes a somewhat novel framework to achieve good VFTON results.\n2. The qualitative results, quantitative results, and ablation study all demonstrate the effectiveness of the proposed method and dataset.\n3. This paper is well organized and easy to read.\n\nWeaknesses:\n1. Personally, I don't think the VFTON is more challenging than the VTON of clothing, especially for loose and unrigid clothing. Moreover, the dataset consisted of 7305 image triplets is not large, which may limit the performance of VFTON, degrading the contribution of this work.\n2. Some visualization results of the proposed method are distorted, such as Figure 5(3) and the second example in Figure 6(b). As its limitation says, for some special texture materials, it is difficult for this method to generate the same results as the input. I think this may be because the dataset is not large or rich enough.", "was_revised": true, "final_justification": "Considering this paper pioneers a new task of VFTON, creates a new shoe dataset, and achieves good results. I tend to accept it. I also thank the authors for helping me to understand the paper's contributions better in the rebuttal stage. However,  this paper still needs to be improved. Thus, I maintain my original score, i.e., borderline accept.", "rebuttal": "# Response to Reviewer NYQo\n\nWe sincerely appreciate your recognition of our work's significance in the VFTON (Virtual footwear try-on) proposal, dataset contribution, comprehensive experiments, and clear writing style. We are also grateful for your constructive suggestions. Some of the concerns you raised stem from misunderstandings of our work, which we clarify below. Due to the NeurIPS 2025 policy prohibiting PDF attachments, we can only provide textual explanations instead of visual examples.\n\n---\n\n## Question 1.1: Difference between VFTON and VTON\n\nThis is a natural thought, reflecting your expertise in the field. While VFTON does not require generating loose, deformable clothing (as shoes are nearly rigid bodies), it introduces unique challenges distinct from VTON. Specifically, **multi-view understanding** is one of the central to VFTON. \n\n- **Task Alignment**: VTON typically generates front-facing clothed avatars from flat-lay front-view clothing images, involving fixed-perspective, aligned generation. In contrast, footwear try-on requires **arbitrary-perspective generation**, as shown in the multi-view nature of VFTON. \n- **Model Capabilities**: We conducted VTON training previously and observed that pre-trained diffusion models inherently generate realistic fabric folds with consistent lighting even in early stages. This suggests that deformable clothing generation is an emergent capability of such models. However, **spatial reasoning for footwear** is not natively supported. For example, multi-view shoe product images often show different patterns on the outer and inner sides (e.g., Figure 2(a) row 1 column 5). Early-stage VFTON results frequently exhibited flipped inner/outer textures, indicating the need for long-term spatial understanding training.\n\nThus, while both tasks involve virtual try-on, their technical challenges and focus areas differ, making direct difficulty comparisons inappropriate.\n\n---\n\n## Question 1.2: Dataset Scale of MVShoes\n\nThank you for your expertise; your comments have been very enlightening, and we're grateful. We acknowledge the current dataset scale limitations and outline the reasons below:\n\n1. **Multi-view Collection Complexity**:  \n   - Unlike try-on datasets (e.g., VITON-HD, DressCode) that use single-view clothing-product pairs, MVShoes requires collecting **multi-view shoe product images**. Such data is significantly rarer and more expensive to acquire (only ~25,000 qualified raw shoe product image sets were collected at submission time). Please refer to Fig. 2 in the Appendix PDF.\n\n2. **Strict Data Filtering Pipeline**:  \n   - Multi-view shoe images often exhibit content inconsistencies (e.g., same style from the same shops but different color variants), which drastically reduces valid triplet counts after filtering.\n\n3. **In-the-wild Person Images**:  \n   - MVShoes includes real-world person images with complex backgrounds, requiring stricter segmentation and attribute extraction compared to clean studio-shot datasets (e.g., VITON-HD, DressCode). Our zero-tolerance policy for \"bad cases\" further limits sample counts after careful human filtering. Please refer to Fig. 2 in the Appendix PDF.\n\nWe will continue expanding MVShoes and plan to open-source more when sufficient scale is achieved, continually contributing to the research community in the future.\n\n---\n\n## Question 2: Visualization Concerns\n\nThank you for your meticulous and responsible review. We re-examined the ShoeFit results for the cases you mentioned and did not observe shape or material distortions. Could you please clarify the specific aspects of concern? We agree the current dataset size is limited, and our ongoing data collection efforts aim to address this.\n\n---\n\n## Question 3: Public Datasets for VFTON\n\nWe appreciate your suggestion and carefully examined the DeepFashion and DeepFashion2 benchmarks. However, as far as we know, we found **no publicly available image pairs** containing both product shoe images and corresponding person images suitable for VFTON. Actually, the lack of public datasets motivated us to create MVShoes, which we consider a major contribution of this work.\n\n---\n\nWe hope these clarifications address your concerns and look forward to your further feedback."}, {"review_id": "vCxZ20zF4e", "reviewer": "Reviewer_B4SC", "summary": "This paper addresses the unique challenges of Virtual Footwear Try-On (VFTON), including data scarcity, viewpoint misalignment, and background-induced color distortion, which differentiate it from garment-based VTON. To tackle these issues, the authors introduce MVShoes, the first large-scale multi-view footwear try-on dataset with 7,305 annotated image triplets. They also propose ShoeFit, a dual-stream DiT-based architecture that incorporates Multi-View Conditioning with 3D Rotary Position Embedding and a LayeredRefAttention module to enhance texture fidelity and reduce environmental interference. The model effectively decouples shoe appearance from background noise and improves rendering quality under real-world conditions. Extensive experiments demonstrate that ShoeFit sets a new benchmark in high-fidelity footwear try-on synthesis.", "questions": "One notable failure case appears in the teaser image, specifically in the second row's final example, where the rear shoe is not properly changed in the try-on result. This highlights a critical challenge in VFTON: handling spatially separated footwear, such as when shoes are not symmetrically aligned or are partially occluded. These cases are inherently more difficult due to depth ambiguity, occlusion, and the lack of explicit 3D reasoning in the model. It would be valuable to understand whether such failures are isolated or occur frequently across the dataset, and whether the model is robust to varying foot poses and inter-shoe distances. (It would be better discussing such observations in the limitation part, which is not clearly discussed in the current paper.)", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths\n- The most significant strength of this paper lies in the introduction of the MVShoes dataset, which, if released publicly, could catalyze research in the underexplored domain of Virtual Footwear Try-On (VFTON). The lack of publicly available footwear datasets has been a major bottleneck in expanding VTON research beyond garments like tops and bottoms. By providing a high-quality, multi-view dataset, this work opens the door for adapting existing VTON models (e.g., CATVTON) to a new task, potentially establishing VFTON as a standard benchmark in the community.\n- The proposed ShoeFit architecture integrates multiple components—such as dual-image streams, multi-view conditioning, and background-aware attention—which collectively contribute to improved realism and robustness in try-on synthesis.\n\n### Weaknesses\n- While the dataset itself is valuable, the data processing pipeline lacks technical novelty, appearing to follow conventional VTON data collection and preprocessing practices without introducing new methodologies tailored to footwear-specific challenges.\n- The ablation study is insufficiently thorough, making it difficult to isolate the contributions of individual components. For instance, the Multi-View Image Concatenation is directly borrowed from CATVTON and involves simple concatenation without architectural innovation. Similarly, the use of 3D RoPE is somewhat misleading in terminology—it merely adds an index-based channel per image, which may not fully justify the \"3D\" label and could benefit from clearer naming.\n- The LayeredRefAttention module includes a conditional mask mechanism, but it is unclear whether the ablation study isolates the effect of the mask itself versus the attention mechanism. This ambiguity raises questions about whether performance gains stem from the attention design or simply from background suppression via masking, as seen in works like Diptych Prompting [1].\n- Additionally, the impact of 3D RoPE is not clearly quantified. A comparison with 2D RoPE (as used in CATVTON) would help clarify whether the proposed positional encoding offers meaningful improvements.\n\n[1] Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator, CVPR 2025\n\n\nI believe the paper presents a promising direction for expanding the VTON domain into footwear, and the introduction of the MVShoes dataset is a valuable contribution. However, the current version lacks sufficient ablation analysis to convincingly demonstrate the effectiveness of each proposed component. If the authors can provide a more thorough and transparent ablation study—particularly isolating the effects of 3D RoPE, LayeredRefAttention, and the conditional masking mechanism—I would be inclined to raise my score. I look forward to the authors’ response and clarification on these points.", "was_revised": true, "final_justification": "Since my major concerns have been satisfactorily resolved, I will raise my score.", "rebuttal": "# Response to Reviewer B4SC\n\nWe sincerely thank you for your recognition of our work's novel task, dataset contribution, and model performance. Your feedback—particularly the suggestion to supplement ablation experiments—has significantly enhanced the rigor of our paper. We appreciate your expertise in this field and meticulous review. Please rest assured that the **MVShoes dataset will be publicly released upon paper acceptance**, which we consider a major contribution of this work. We have conducted all requested experiments and address your concerns as follows:\n\n---\n\n## Weakness 1: Data Processing Improvements\n\nCompared to VTON, our data processing improvements focus on **dwpose preparation** and **mask design** with two key innovations:\n\n### 1. **Pose Estimation for Shoe Try-On**\nTraditional open-source pose estimation methods (e.g., OpenPose, DWPose) in VITON pipeline, struggle to capture lower-body/foot poses in VFTON, which often only include partial leg shots or full lower-body views. To address this, we fine-tuned the **dwpose model** on specially collected datasets containing diverse lower-body and close-up leg poses. This enables our model to generate accurate pose descriptions for various leg postures, significantly enhancing its generalization capability for shoe-related applications.\n\n### 2. **Generalizable Foot Mask Design**\nGiven the diversity of shoe types (e.g., sneakers, boots, sandals) and their varying leg coverage, we propose a **leg-pose-aware mask generation strategy**. Unlike VTON's body-centric masks, our method combines leg pose information with bounding box expansion from shoe detection to create **adaptive masks**. This allows seamless cross-shoe category try-on capabilities—for example, a model wearing long boots or flip-flops can be transformed into sneakers while maintaining anatomical alignment and realistic occlusion. Please refer to the row 1 in Fig.5 of Appendix PDF. This approach lays the foundation for robust, universal shoe try-on systems.\n\n---\n\n## Weakness 2: Multi-View Concatenation\n\nThank you for your valuable feedback on the **Multi-View Concatenation**. While similar to CAT-VTON in concept, our implementation has distinct objectives and functionalities, with experiment insights:\n\n1. **Insights of viewpoints in VFTON**:  \n   We discovered and identified that shoes, as rigid bodies, exhibit **viewpoint misalignment** phenomena. Our contribution lies in introducing **multi-view conditioning** to address this, validating its effectiveness in VFTON generation, which has not been studied and reported in previous try-on works.\n\n2. **Differences from CAT-VTON About Concatenation**:  \n   - **CAT-VTON**: CAT-VTON shares a single image-stream for both generation and condition branches, with concatenation occurring between noise and condition features (acting as condition injection).  \n   - **ShoeFit**: We employ a **dual image-stream** architecture, separately processing generation and condition branches. Concatenation occurs **within the condition features**, serving as **information supplementation**. \n\nFor details on **3D RoPE**, please refer to **Weakness 4**.\n\n---\n\n## Weakness 3: Ablation Study Clarification\n\nThank you for your expert insights. We acknowledge the unclear statement in our original ablation study. During the LayeredRefAttention ablation, we replaced it with standard attention layers **without masks**. To validate whether background suppression via simple masking contributes to performance gains, we conducted an additional experiment using standard attention but **masked out product shoe backgrounds** (following the Diptych Prompting you mentioned). \n\nDue to NeurIPS rebuttal policy, we cannot include visual results but report the quantitative metrics below:\n\n|           Methods                   |       SSIM     |      LPIPS     |      FID     |      KID     |      DISTS      |\n|-------------------------------------|----------------|----------------|--------------|--------------|-----------------|\n| + D-i-s                             |      0.761     |      0.174     |      21.57   |     1.493    |      0.1038     |\n| + D-i-s + standard ATTN (with mask) |      0.756     |      0.181     |      21.70   |     1.698    |      0.1056     |\n| + D-i-s + LRA (with mask)           |      0.772     |      0.159     |      21.07   |     0.944    |      0.1059     |\n\nVisually, **simple background masking** exacerbates color deviations, especially for bright, pure colors. As discussed in the paper (Line 205), naive background suppression neglects critical **illumination cues** embedded in product images, which are essential for inferring shoes' intrinsic appearance and texture. This is because product shoe images and shoes on human feet often differ slightly in color **in the trainset** due to **varied lighting conditions** during collection. Removing backgrounds allows the model to freely guess lighting conditions during training, leading to reduced color fidelity.\n\n---\n\n## Weakness 4: 3D RoPE Ablation\n\nThank you for your suggestion. We have supplemented an ablation study on **3D RoPE**'s impact as following: \n\n\n|           Methods                   |       SSIM     |      LPIPS     |      FID     |      KID     |      DISTS      |\n|-------------------------------------|----------------|----------------|--------------|--------------|-----------------|\n| + D-i-s + LRA + MC (w/o 3D RoPE)    |      0.778     |      0.152     |      20.18   |     0.695    |      0.0971     |\n| + D-i-s + LRA + MC (Full)           |      0.780     |      0.149     |      20.18   |     0.485    |      0.0941     |\n\nIt demonstrates the performance gain we achieved using the proposed RopE without incurring any additional computational overhead.\nYour advice is very valuable: **3D RoPE** adds an index-based channel per image, increasing dimensionality but differing from traditional 3D semantics. We will revise the terminology in the revision to avoid ambiguity.\n\n---\n\n## Question 1: Teaser Image Corner Case\n\nThe issue in the **second row's final example** in the teaser image is actually caused by **extreme occlusion** and **inpainting mask strategy**, not depth ambiguity or lack of 3D reasoning. Our model is robust to **foot poses** and **inter-shoe distances** due to **separate masking** and **joint inpainting**. Spatially separated, asymmetric, or partially occluded input footwear does not degrade try-on quality, **as demonstrated row 1 of Fig.6 in Appendix PDF, and in rows 2–5 of Fig. 5 (notably the rear shoe in row 4) in main paper**.\n\nIn the teaser image's second row, the **extremely small and occluded rear shoe area** led to failure in **keypoint detection** and **shoe Detection**, marking the rear shoe as a background region. Only the front foot area was designated as the inpainting region, resulting in the bad case. We consider this an engineering limitation, resolvable with advancements in segmentation and detection models. We will explicitly discuss such limitations in the revised manuscript. Thanks for your responsible review!\n\n---\n\nWe hope these clarifications address your concerns and look forward to your further feedback."}, {"review_id": "GjXKXTQPHq", "reviewer": "Reviewer_8hJn", "summary": "The paper introduces ShoeFit, a framework for virtual footwear try-on (VFTON), addressing key challenges unique to shoes: data scarcity, viewpoint misalignment, and background-induced color distortion. To tackle the lack of realistic datasets, the authors curate MVShoes, a multi-view shoe try-on dataset comprising annotated image triplets, covering various footwear categories and real-world scenes.\n\nAt the core of their solution is a dual-image-stream DiT architecture, built upon FLUX.1-Lite, that separates denoising and conditioning to preserve shoe texture fidelity. The method incorporates two key modules: Multi-View Conditioning, using Reference Concatenation and 3D Rotary Position Embeddings to align different shoe perspectives, and LayeredRefAttention, which helps disentangle shoe appearance from environmental lighting and background. Their experiments show that ShoeFit significantly outperforms existing baselines, establishing a new benchmark for future VFTON research.", "questions": "1. Generalization Beyond Footwear: Can the authors elaborate on whether the ShoeFit framework, especially the LayeredRefAttention and multi-view modules, could generalize to other reflective or hard-structured apparel items (e.g., handbags, belts, or sunglasses)?\nSuggestion: Even a small exploratory discussion or experiment would give insight into the versatility of the architecture.\n\n2. Efficiency and Inference Time Trade-offs: Given the dual-image-stream design and multi-view input handling, could you provide more details on inference time, GPU memory usage, and how this might scale to production systems?\n\n3. Failure Cases and Robustness: You mention limitations with small logos and fine text rendering. Could you show a few concrete failure cases or patterns of breakdown (e.g., shoe types or lighting scenarios that remain challenging)?", "limitations": "yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\nThis paper offers a contribution to the virtual try-on space by zooming into the challenges of virtual footwear try-on (VFTON), which has been largely underserved. The introduction of the MVShoes dataset, with 7305 annotated triplets across multi-view scenes and diverse shoe categories, and the authors have been meticulous in data curation, including pose alignment, background separation, and high-resolution filtering. From a quality standpoint, this dataset sets a strong potential for future work in VFTON.\n\nThe proposed ShoeFit model, built upon a dual-image-stream DiT framework, and the architectural decisions such as decoupling conditioning from denoising, using Multi-View Conditioning with 3D RoPE, and introducing LayeredRefAttention, are well motivated and grounded in the unique difficulties of shoes (reflectivity, misalignment, subtle variations). The experiments are well executed, with comprehensive metrics (SSIM, FID, DISTS, etc.) and comparisons to both strong baselines and state-of-the-art models like CatVTON and OOTDiffusion. Ablation studies are also nicely done, showing the incremental value of each module.\n\nWeaknesses: \n\nWhile the technical content is strong, the clarity of presentation could be improved in some parts. The paper occasionally leans on heavy architectural jargon (e.g., long sequences explaining attention math) without providing more visual or intuitive explanations, which might make it harder for readers to digest. A clearer visual roadmap, especially of the dual-stream pipeline, would help.\n\nFrom a significance angle, this is a clear step forward for VFTON, but the broader impact could be more explicitly explored. The work is very tailored to footwear, which limits its generalization to other product categories. It would be good to know whether the architecture can scale or adapt to accessories, or other reflective materials like handbags or sunglasses.\n\nOriginality: \n\nThe originality is solid. While it builds on existing diffusion and DiT frameworks, the way it adapts them specifically for VFTON is novel and well justified. Concepts like LayeredRefAttention and multi-view RoPE adaptation are interesting technical contributions.", "was_revised": true, "final_justification": "I will maintain my original score, as it already represented a strong endorsement of the paper’s quality and impact. The authors have provided clear and comprehensive responses to the questions raised during the rebuttal process. While the points discussed were valid, they ultimately reinforced the robustness of the methodology rather than uncovering any major shortcomings. The authors’ replies demonstrated a thorough understanding of the subject and further validated the soundness of their approach. I have no remaining reservations and remain fully in favor of accepting this paper.", "rebuttal": "# Response to Reviewer 8hJn\n\nWe sincerely appreciate your great praise of our work's significance in VFTON proposal, dataset contribution, network architecture design, and comprehensive experiments. We are encouraged by your feedback, which has highlighted key improvements. We address your concerns as follows:\n\n---\n\n## Weakness 1: Technical Explanation Clarity\n\nIf we understand correctly, the \"heavy architectural jargon\" you mentioned (e.g., long sequences explaining attention math) should refer to Section 4.3 LayeredRefAttention. Due to space limitations, we provide limited visual or intuitive explanations in the paper. We promise to provide more detailed explanations to assist readers in understanding in the revised version. In addition, we are working on plotting a more clear roadmap with case diagrams to complement Figure 4 and strengthen the explanation of LayeredRefAttention. This will also be included in the revised version (maybe supplementary materials). \n\nThank you for your professional feedback on this aspect!\n\n\n---\n\n## Weakness 2: Future Research Direction on Accessories\n\nYour suggestion regarding handbags, sunglasses, and jewelry is highly visionary! These items are indeed part of our long-term research agenda for e-commerce personalized recommendations and full-body try-on systems. However, current limitations include:\n\n- **Dataset Gaps**: No publicly available try-on datasets exist for these categories (unlike clothing pairs in VITON-HD/DressCode).\n\nWe are happy to discuss the technical chanllenges of there types of research here.\n\n- **Technical Challenges**:\n  - **Sunglasses**: Simplest case (single facial region, limited pose variation). VFTON models can likely generalize here.\n  - **Handbags**: Challenge lies in removing hand-part artifacts. Even with strong base models like Flux, inpainting in small regions on full-body avatars may produce distorted hands or disconnected straps.\n  - **Jewelry**: High structural complexity demands pixel-level detail preservation, testing the model's perceptual granularity.\n\nWe plan to initiate data collection for these categories soon and welcome further discussions. While exploratory experiments are not feasible now due to data scarcity, we believe ShoeFit's material rendering capabilities are transferable to these domains.\n\n---\n\n## Question 1: Refer to Weakness 2 for detailed discussion.\n\n---\n\n## Question 2: Computational Cost Analysis\n\nWe appreciate your attention to efficiency. Our model architecture improvements primarily focus on LayeredRefAttention and Dual-image-stream components. We report the inference time and memory costs introduced by these two improvements in the table below. The experiments were conducted on a single NVIDIA H20 GPU, using a batch size of 1, 25 inference steps, and computation precision set to torch.bfloat16.\n\n\n|           Methods                         | Inference Time per Image |      GPU Memory Usage    |\n|-------------------------------------------|--------------------------|--------------------------|\n| Cat-VTON (image + text streams)             |      22.27 seconds       |        35.5 GB           |\n| ShoeFit (Dual-image-steam, D-i-s)         |      17.44 seconds       |        22.0 GB           |\n| ShoeFit + D-i-s + LayeredRefAttention     |      21.15 seconds       |        28.5 GB           |\n\nWe observe that ShoeFit achieves significant memory savings and inference speedup over CAT-VTON when using the Dual-image-stream (D-i-s) architecture. This improvement is attributed to the removal of redundant text stream and their associated text encoders, i.e., the T5 and CLIP encoders.\n\n---\n\n## Question 3: Failure Case Analysis\n\nYes! We have documented failure cases in the technical appendix of the supplementary material. Please refer to **Figure 1** in the attached PDF for detailed examples and error analysis.\n\n---\n\n\nWe hope these clarifications address your concerns and look forward to your further feedback!"}, {"review_id": "K04WkMjUOD", "reviewer": "Reviewer_Bocv", "summary": "This paper focuses on the task of Virtual Footwear Try-On (VFTON) and introduces MVShoes, a multi-view shoe try-on dataset with 7,305 well-annotated image triplets, covering various footwear categories and challenging try-on scenarios. In addition, the paper proposes a customized dual-image-stream DiT architecture called ShoeFit for generating high-fidelity try-on results. If MVShoes will be released publicly, I believe this work will make a contribution to the field.", "questions": "1. Since Virtual Footwear Try-On is a task closely tied to real-world applications, it’s important to consider actual consumer behavior. In practice, many users prefer to virtually try on different styles within the same shoe series (e.g., Nike Dunk, adidas Ultraboost). These shoes typically share similar geometry but differ in materials, textures, and fine decorative details. The scenarios explored in the paper primarily focus on trying on shoes with substantial variations in style, shape, and color, such as transitioning between sneakers, boots, or high heels, which differs from this specific use case. However, both scenarios demand high attention to detail in the virtual try-on results. I would like to see ShoeFit’s performance on such subtler, yet practically relevant, use cases.\n\n2. It is noted that in Figure 5, the sock regions in the ShoeFit results sometimes differ from those in the input images. This may have undesirable effects—for example, in the first row, the generated socks differ from those in the input, potentially affecting try-on accuracy. However, it might also lead to positive effects in certain cases—for instance, in the fourth row, after trying on high heels, the socks in the result may be replaced by sheer stockings or bare feet. Is the change in socks controllable?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Virtual Footwear Try-On (VFTON) is a promising and worthwhile direction to explore.\n2. The proposed MVShoes dataset includes human foot images, pose annotations, masks, and multi-view product shoe images, which—if released—would be a valuable contribution to the field.\n\nWeaknesses:\n1. The proposed generation method, ShoeFit, appears to lack substantial novelty. However, since the main contribution of the paper seems to be the MVShoes dataset, this may be acceptable.\n2. There are questions regarding the virtual try-on methodology, please refer to the “Questions” section.", "was_revised": true, "final_justification": "I have reviewed the authors’ response and read the comments from the other reviewers. Overall, I believe that if MVShoes is publicly released, it will contribute to the field. However, it should be noted that I still find the results for the shoe series style variations limited, and the accuracy in the sock region insufficient. Reviewer NYQo also pointed out that the paper should further provide real-world results on data that do not come from the authors' own dataset. Based on this, I maintain my original score of 4: Borderline accept.", "rebuttal": "# Response to Reviewer Bocv\n\nWe sincerely thank you for your high recognition of our work's novelty in defining the VFTON task, constructing the MVShoes dataset, and strong paper quality. The **MVShoes dataset will be publicly released once this paper is accepted**. We address your concerns as follows:\n\n---\n\n## Weakness: Contribution Clarification\n\nThank you for your insightful feedback! As you noted: *\"the main contribution of the paper seems to be the MVShoes dataset, this may be acceptable\"*. Besides, we emphasize that **our network architecture is also a good contribution**, developed through iterative improvements in VFTON experiments. Key innovations include:\n\n- **Dual-Image-Stream DiT Architecture**: Enables parallel processing of multi-view shoe products and person images.\n- **Multi-View Conditioning Mechanism**: Resolves viewpoint misalignment issues inherent in footwear try-on.\n- **LayeredRefAttention Module**: Mitigates background-induced color distortion by layer-wise reference attention.\n\nThese innovations collectively enable our method to outperform baselines like CatVTON, which lack specialized mechanisms for footwear-specific challenges.\n\n---\n\n## Question 1: Shoe Series Style Variations\n\nThank you for your suggestion regarding consumer-oriented evaluation scenarios. We think your suggestion is very reasonable. Due to NeurIPS 2025's PDF submission restrictions, please refer to rows 4-6 in Fig.6 in the Appendix PDF about  **try-on visualizations of different styles within the same shoe series**. Your feedback is well-founded, and we observe:\n\n- **Structure requirements**: Trying on shoes with small variations is a less difficult type of try-on, in which case the model is possible to obtain footwear shape hints from the inpainting mask.\n- **Texture detail requirements**: Both small- and large-variation scenarios demand similar attention to detail in virtual try-on results, as you noted: *\"both scenarios demand high attention to detail in the virtual try-on results\"*\n\nWhile we cannot present intra-series style variations in rebuttal results, please refer to Fig. 5&6 in the Appendix for more generalized cases of our method. Notably, our **unpaired testing protocol** randomly shuffles human-shoe pairs from the diverse MVShoes dataset (covering 20+ shoe categories), resulting in most test cases involving substantial design differences.\n\n---\n\n## Question 2: Sock Generation Behavior\n\nYour meticulous observation reflects your expertise and carefulness as a reviewer. Sock generation depends critically on the **inpainting mask design**:\n\n1. **Mask Strategy**: Following VTON conventions, we use keypoint detection and shoe detection to remove original footwear in person images. This inevitably affects adjacent sock regions.\n2. **Case Analysis (Figure 5)**:\n   - **Row 1**: Inpainting mask covers most sock area, leaving only a small unchanged top edge. Model generates socks matching the preserved edge color (with minor pattern variations).\n   - **Rows 2, 3, 5**: Most socks remain in unchanged regions, preserving input sock appearance.\n   - **Row 4**: Nearly all of the sock area under the inpainting mask leads to heel-style sock removal (consistent with high-heel wear habits).\n3. **The same as VTON**: Similar phenomena occur in upper-body cloth try-on, where pants shape near the waist often changes when the lower section is in the inpainting region.\n\nIn general, **the model generates reasonable images of the sock area inside the inpainting mask that are consistent with the training data distribution**, which generally leads to **harmonious appearance**. In our opinion, for VFTON tasks, preserving original socks is not strictly required. For more controlled sock retention, we recommend exploring **mask-free try-on approaches** like BooW-VTON [1], which avoid inpainting masks to preserve full input sock context.\n\n---\n\nThank you for your suggestions on paper formatting! We will correct them in the revised version.\n\n---\n\n## References\n\n[1] BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training \n\n---\n\n\nWe hope these clarifications address your concerns and look forward to your further feedback."}]

Author rebuttal:

Meta-review: This paper addresses the underexplored problem of Virtual Footwear Try-On, introducing both a new dataset and a novel architecture tailored to the unique challenges of footwear try-on, including data scarcity, viewpoint misalignment, and background-induced color distortion. The proposed dual-stream DiT framework leverages multi-view conditioning with 3D rotary position embeddings and a layered attention mechanism to decouple shoe appearance from environmental effects while preserving texture detail. Experiments demonstrate substantial improvements in fidelity and robustness compared to existing methods.

All four reviewers rated the paper as borderline accept. Reviewers NYQo, Bocv find that the scope of validation could be broader, particularly including results on external real-world data. Reviewer Bocv finds the performance limitations remain for style variations and in handling the sock region. Overall, reviewers acknowledged that while the contributions are solid, the paper would benefit from further refinement and expanded validation.

The reviewers converge on a borderline accept rating, emphasizing that the paper makes a novel and timely contribution by opening up a new subfield with both a benchmark dataset and a tailored architecture. While there are valid concerns regarding the scope of evaluation and certain qualitative limitations, these do not outweigh the significance of releasing a dedicated dataset and establishing a baseline framework. Thus, the AC recommends accept.
Decision: Accept (poster)