Pith. sign in

REVIEW 4 major objections 5 minor 43 references

Size-Variable Virtual Try-On with Physical Clothes Size

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A mask-deformation network turns physical size labels into visible garment-size changes in virtual try-on images.

desk verdict A genuinely new task and dataset, but the central claim of size-variable virtual try-on is only demonstrated for the same garment type, and the evaluation is too thin to verify the rest. read the letter →

arxiv 2412.06201 v1 pith:7WCM42WB submitted 2024-12-09 cs.CV cs.GR

Yohei Yamashita , Chihiro Nakatani , Norimichi Ukita This is my paper · ORCID
classification cs.CVcs.GR
keywords virtualtry-onsize-variablegarmentfittingmaskdeformationnetworkphysicalclothessizeevaluationmetricdatasetimagewarpinghumanparsing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces size-variable virtual try-on, a problem setting in which the garment in a synthetic try-on image changes its on-body size according to the physical size labels of the clothes, not just the person's body shape. The authors claim that a size-variable mask deformation network can produce a clothing mask whose hem and sleeve regions match the garment's physical size when conditioned on the five-dimensional size parameters of both the reference garment and the target garment. To train and evaluate this, they collect a paired-image dataset of 1,524 images from 26 subjects wearing the same garments in different sizes, and they propose a Size Evaluation Metric that measures hem and sleeve area differences. Their experiments report that this approach beats general virtual try-on baselines on the size metric while staying comparable on perceptual quality. If this holds, users could specify a garment size in advance and see a realistic preview of how that size would fit.

What carries the argument

The load-bearing component is the size-variable Mask Deformation Network, built from a Residual Mask Deformation Network and a Mask Refiner. The residual encoder-decoder encodes the reference person image and its segmentation mask into a mask feature map, while a Size Feature Extractor turns the reference and target size vectors into a size feature map; the two are fused by elementwise multiplication. The decoder estimates the residual mask between the reference mask and the target mask, and that residual is added to the reference mask before a two-convolution-layer refiner produces the final deformed mask. By modelling the residual rather than the full mask, the network can concentrate on the small hem and sleeve displacements that encode garment size. The paired dataset and the Size Evaluation Metric are auxiliary machinery that make training and evaluation of this size-conditioned deformation possible.

What would settle it

Fix a reference image with a known reference size and generate deformed masks for target sizes S, M, L, and XL; if the hem and sleeve areas of the deformed mask do not increase monotonically with the labeled target size, or if swapping the reference and target sizes does not reverse the size ordering, the network is not actually conditioning on physical size. A concrete test is to train only on certain garment types and then evaluate on a never-seen garment while holding the person fixed: if the Size Evaluation Metric on that held-out garment is close to chance, the size control does not generalize to new garments.

Watch

Extended reading notes

Core claim

The central claim is that the physical size of a garment can be propagated through an image-based virtual try-on pipeline by learning to deform the clothing mask, rather than by warping pixels directly or by adjusting generative style vectors. The proposed mask deformation network takes the reference person image, the reference garment mask, and the size vectors for the reference and target garments, fuses the mask features with the size features by elementwise multiplication, and predicts the residual between the reference mask and the ground-truth try-on mask. Adding that residual to the reference mask and passing it through a mask refiner yields the deformed mask used for subsequent Thin Plate Spline warping and content fusion. The paper reports that this residual formulation is what lets the network extend or shrink the torso hem and sleeve areas in line with the physical size labels, and that the resulting try-on images preserve visual quality comparable to non-size-aware baselines.

Load-bearing premise

The method assumes that the physical size of the clothes worn by the person in the reference image is known at inference time, because the size feature extractor needs that reference size alongside the target size; nothing in the paper estimates it from an arbitrary photo.

Editorial extensions

If this is right

  • A user can choose an arbitrary target size label for a garment shown in a different size, and the predicted try-on mask will extend or shrink the hem and sleeves before the clothes texture is warped onto the person.
  • The residual-mask formulation makes size adjustment robust to small differences between reference and target silhouettes, since the network only needs to predict the delta between them rather than a whole new mask.
  • The new dataset pairs images of the same person in the same garment across sizes with synchronized poses, providing a training signal that previous try-on datasets lacked because they had no size annotations.
  • The Size Evaluation Metric gives a quantitative handle on size correctness, measuring torso-hem and sleeve area changes separately instead of relying only on full-image perceptual similarity.
  • Because the mask network is modular, it can be plugged into existing warping-based virtual try-on systems in place of their mask-estimation stage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: a practical deployment would need to estimate the reference garment size from a single photograph, since the method assumes that size is given at inference time and the paper does not provide such an estimator.
  • Inference: the residual-mask mechanism could be applied to other garment attributes beyond size, such as length or looseness, by replacing the physical-size feature vector with a different conditioning signal.
  • Inference: the proposed Size Evaluation Metric could be reused to evaluate body-garment alignment in related tasks such as size recommendation and fit prediction, not only virtual try-on.
  • Inference: the paper's suggestion to normalize input images by pixel-to-physical-size ratio could in principle support cross-camera use, but the authors do not test that scenario.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper defines size-variable virtual try-on as a new problem in which the garment worn by a reference person is resized in the output image according to the physical size parameters of the target garment. The proposed pipeline first estimates a deformed clothes mask MD from the reference person image PR, the reference segmentation mask MR, and the 5D physical-size vectors of the reference and target garments (SR and ST); afterward, the existing ACGPN components TPS and CFN warp the target clothes image CT into the deformed mask and fuse it with the person. The authors collect a paired dataset of 26 subjects wearing 14 garments in different sizes, using posture matching and projective alignment, and propose a Size Evaluation Metric (SEM) based on hem and sleeve area differences. Experiments compare the method with ACGPN and StyleGAN and include ablations of the residual mask path, the mask refiner, the reference image input, and several loss choices.

Significance. The problem setup is timely, and the dataset is a potentially useful asset for a largely unexplored task. The idea of learning a residual between the reference mask and the target-size mask, conditioned on physical size vectors, is clean and pragmatic, and the modular use of existing warping and fusion components is sensible. The authors also explicitly acknowledge the dataset's limited scale in the conclusion. However, the current evidence does not support the broad claim of fitting 'any size of clothes to a reference person': the mask estimator never sees the target garment image, the dataset and test set contain only same-garment pairs, and the quantitative evaluation relies on a self-proposed metric with no error bars and no public code or data release. These limitations are within the authors' control and should be addressed before publication.

major comments (4)
  1. [Sec. 3.2, Fig. 6, and the Inference paragraph] The Mask Deformation Network takes PR, MR, SR, and ST as inputs and does not receive the try-on clothes image CT. The predicted deformed mask MD is therefore independent of the silhouette of the target garment. The dataset in Sec. 3.1 contains only pairs of the same garment type in different sizes, and the 24 new-clothes test items are also of this same-garment form. This supports a claim about size-conditioned deformation of the reference garment mask, but not the abstract's 'fitting any size of clothes to a reference person' or Sec. 4.3's 'can deform the mask according to the physical size of the clothes' when the target garment is a different type from the reference garment. The authors should either re-scope the claims to the same-garment setting or modify the architecture to condition on CT and evaluate on cross-garment pairs.
  2. [Sec. 4.2 and Tables 2-5] All quantitative results are single point estimates computed on 96 test pairs, with no error bars, confidence intervals, or significance tests. FID is known to be unstable at such small sample sizes, and the paper does not state how many samples were used for each FID computation. LPIPS is essentially identical (0.44) across nearly all methods and ablations, so the reported differences are not demonstrably meaningful. The authors should report multiple runs or seeds, per-pair distributions, and significance tests, and they should disclose the exact sample size for each metric.
  3. [Sec. 3.3, Eqs. (2)-(4)] SEM is proposed by the same authors and directly measures area differences in the hem and sleeve regions between the estimated mask MD and the ground-truth mask MG. MG is generated by the same Graphonomy segmentation estimator used to produce MR, and it is the exact training target of the mask losses LW, LD, and LA in Eq. (1). SEM therefore tracks the training objective, and no evidence is provided that SEM correlates with human judgments of correct garment size. To support the central quantitative claim, the authors should validate SEM against human ratings or an independent segmentation/reference procedure, and they should report SEM for baseline methods with the same post-processing.
  4. [Sec. 3.2, Inference] The method requires the physical size SR of the clothes in the reference person image to be provided at inference time, but no estimator for SR from an arbitrary photograph is proposed. In the dataset this quantity is known by construction, so the experiments do not address the deployment scenario implied by the title. If SR is intended as user-supplied metadata, this should be stated explicitly; otherwise, a size-estimation module or a robustness experiment with noisy SR values is needed.
minor comments (5)
  1. [Abstract] The word 'closes' should be 'clothes'.
  2. [Sec. 4.1] 'ReLe activation' should be 'ReLU activation', and 'The encoder and decode consist' should be 'the encoder and decoder consist'.
  3. [Sec. 3.1] The sentence 'This gap can be suppressed by rescaled/normalizing an input image' mixes participle and gerund forms; it should read 'by rescaling/normalizing an input image'.
  4. [References] References [3] and [4] appear to be duplicates of the same 'Multi-garment net' paper; the duplicate should be removed.
  5. [Sec. 4.2] The paper does not mention any plan to release code or data, which weakens reproducibility; even a commitment to release the dataset would strengthen the contribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the size-conditioned mask deformation is trained and evaluated on held-out pairs, and the SEM metric is not a training loss.

full rationale

The paper's central claim is that a mask deformation network (MDN) maps a reference person image PR and physical size vectors SR, ST to a deformed mask MD matching the ground-truth try-on mask MG. This is a supervised learning claim. The loss functions (1) optimize MD against MG and RMD against RMG, and evaluation with SEM, LPIPS, and FID is computed on held-out test pairs (96 test data including 24 new clothes and 72 new persons). SEM, although proposed by the authors, is not a training objective; it is a definition of area differences in hem and sleeve regions derived from Densepose and the masks. Comparing MD with MG via SEM is therefore a direct task evaluation, not a fitted parameter renamed as prediction. The concern that the ground-truth masks are produced by the same segmentation tool is also not circular: MR and MG are produced by Graphonomy, while SEM uses Densepose; in any case using the same estimator for inputs and ground truth is a data-quality issue, not a circular derivation. The omission of the try-on clothes image CT from the MDN inputs (Inference: 'PR, SR, and ST are given and fed into our size-variable mask deformation network') is a genuine scope limitation—the method cannot adapt the mask to a different garment silhouette—but it is not a circularity, because the claimed size-conditioned deformation of the reference mask is still an empirically testable mapping from sizes to masks. Self-citations ([34], [36], [37]) appear only in future-work remarks and are not load-bearing. No step in the paper's derivation chain equates a prediction to its inputs by construction.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The central claim rests on a small private dataset, a self-defined evaluation metric, and several domain assumptions about segmentation and dense pose tools. The only explicit free parameter is the loss weighting, which is unreported. The SEM metric is an invented construct with no independent validation.

free parameters (1)
  • Loss weights lambda_W, lambda_D, lambda_A = not specified
    Weights for the weighted BCE, Dice, and adversarial losses in Eq. (1) are neither reported nor ablated, yet they directly affect the predicted mask and the reported SEM values.
assumptions (4)
  • domain assumption Graphonomy provides sufficiently accurate clothes segmentation for both reference and ground-truth images.
    Graphonomy is used (Section 4.1) to generate MR and MG; errors in segmentation propagate to training and to the SEM evaluation, which relies on these masks.
  • domain assumption DensePose provides body-part segments that reliably identify torso and sleeve areas.
    The SEM metric (Section 3.3) depends on DensePose segments to define hem and sleeve regions; if DensePose is inaccurate for clothed bodies, the metric is not meaningful.
  • ad hoc to paper The 5D physical size vector is a sufficient descriptor for the image-space size difference between two garments.
    The method conditions on five hand-chosen size measurements (Section 3.1). The paper provides no evidence that these measurements capture all silhouette differences relevant to size change, such as fabric drape or garment shape.
  • ad hoc to paper Projective alignment of paired images preserves the physical size difference between garments.
    In the ProjDataset (Section 3.1), one image is warped to align keypoints. The paper assumes this warp does not erase or distort the size difference the network must learn.
invented entities (1)
  • Size Evaluation Metric (SEM)
    purpose: Quantify size differences in hem and sleeve areas between predicted and ground-truth masks.
    SEM is proposed by the same authors and is not validated against human perception or any external benchmark. It only measures aggregate area and ignores spatial shape, and it is not shown to correlate with perceived clothing size.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Size-Variable Virtual Try-On with Physical Clothes Size." pith.science (2026). https://pith.science/paper/7WCM42WB

@misc{pith2026241206201,
  author       = {Pith},
  title        = {Pith review of: Size-Variable Virtual Try-On with Physical Clothes Size},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7WCM42WB}},
  note         = {Machine review of arXiv:2412.06201}
}
read the original abstract

This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can produce highly natural try-on images, these methods fit the clothes on the person without considering the relative relationship between the physical sizes of the clothes and the person. Different from these methods, our method achieves size-variable virtual try-on in which the image size of the try-on clothes is changed depending on this relative relationship of the physical sizes. To relieve the difficulty in maintaining the physical size of the closes while synthesizing the high-fidelity image of the whole clothes, our proposed method focuses on the residual between the silhouettes of the clothes in the reference and try-on images. We also develop a size-variable virtual try-on dataset consisting of 1,524 images provided by 26 subjects. Furthermore, we propose an evaluation metric for size-variable virtual-try-on. Quantitative and qualitative experimental results show that our method can achieve size-variable virtual try-on better than general virtual try-on methods.

Figures

Figures reproduced from arXiv: 2412.06201 by the authors.

Figure 1
Figure 1. Comparison between previous methods and our method. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of warping-based virtual try-on methods. The [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Two different approaches of mask deformation. (a) Ex [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Sample images of 14 clothes. For size-variable virtual try-on, a straightforward scheme is to provide the physical size of try-on clothes as auxiliary cues to a previous virtual try-on method, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 6
Figure 6. Figure 6: Overview of our size-variable mask deformation network. The final output mask ( [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Size Evaluation Metric (SEM). RMG ∈ R 1×H×W are generated from the ground-truth try￾on person image PG. As with MR, MG is generated by SE. RMG is generated from the elementwise subtraction between MR and MG. Both MG and RMG are used to train the whole network by the fo…
Figure 8
Figure 8. Figure 8: Visual comparison in comparative experiments. In this example, M-size and XL-size clothes are used as clothes in the reference [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Visual results for validating the effectiveness of RMDN and MR in ablation studies. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Visual comparison of loss functions for comparing [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Visual comparison of loss functions for comparing [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 38 canonical work pages

  1. [1]

    Im- age2stylegan: How to embed images into the stylegan latent space? In ICCV, 2019

    Rameen Abdal, Yipeng Qin, and Peter Wonka. Im- age2stylegan: How to embed images into the stylegan latent space? In ICCV, 2019. 2

    work page 2019

  2. [2]

    Im- age2stylegan++: How to edit the embedded images? In CVPR, 2020

    Rameen Abdal, Yipeng Qin, and Peter Wonka. Im- age2stylegan++: How to edit the embedded images? In CVPR, 2020. 2

    work page 2020

  3. [4]

    Multi-garment net: Learning to dress 3d people from images

    Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, and Gerard Pons-Moll. Multi-garment net: Learning to dress 3d people from images. In ICCV, 2019. 1

    work page 2019

  4. [5]

    Realtime multi-person 2d pose estimation using part affinity fields

    Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, 2017. 5, 8

    work page 2017

  5. [6]

    VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization

    Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo. VITON-HD: high-resolution virtual try-on via misalignment-aware normalization. CoRR, abs/2103.16874,

    work page Pith review arXiv

  6. [7]

    Editing in style: Uncovering the local semantics of gans

    Edo Collins, Raja Bala, Bob Price, and Sabine S ¨usstrunk. Editing in style: Uncovering the local semantics of gans. In CVPR, 2020. 1

    work page 2020

  7. [8]

    Dovenet: Deep image har- monization via domain verification

    Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, and Liqing Zhang. Dovenet: Deep image har- monization via domain verification. In CVPR, 2020. 1

    work page 2020

  8. [9]

    Splines minimizing rotation-invariant semi- norms in sobolev spaces

    Jean Duchon. Splines minimizing rotation-invariant semi- norms in sobolev spaces. In Constructive Theory of Func- tions of Several Variables, 1976. 2

    work page 1976

Show all 43 references
  1. [10]

    Texture overlay for virtual cloth- ing based on PCA of silhouettes

    Jun Ehara and Hideo Saito. Texture overlay for virtual cloth- ing based on PCA of silhouettes. In ISMAR, 2006. 1

    2006

  2. [11]

    Disentangled cycle consistency for highly- realistic virtual try-on

    Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu, and Ping Luo. Disentangled cycle consistency for highly- realistic virtual try-on. In CVPR, 2021. 2

    2021

  3. [12]

    Parser-free virtual try-on via distilling appearance flows

    Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, and Ping Luo. Parser-free virtual try-on via distilling appearance flows. In CVPR, 2021. 2

    2021

  4. [13]

    Graphonomy: Universal human pars- ing via graph transfer learning

    Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, and Liang Lin. Graphonomy: Universal human pars- ing via graph transfer learning. In CVPR, 2019. 5

    2019

  5. [14]

    Densepose: Dense human pose estimation in the wild

    Riza Alp G ¨uler, Natalia Neverova, and Iasonas Kokkinos. Densepose: Dense human pose estimation in the wild. In CVPR, 2018. 5

    2018

  6. [15]

    Intrinsic image harmonization

    Zonghui Guo, Haiyong Zheng, Yufeng Jiang, Zhaorui Gu, and Bing Zheng. Intrinsic image harmonization. In CVPR,

  7. [16]

    Xintong Han, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S. Davis. VITON: an image-based virtual try-on network. In CVPR, 2018. 1, 2, 3

    2018

  8. [17]

    Virtual try-on through image-based rendering

    Stefan Hauswiesner, Matthias Straka, and Gerhard Reitmayr. Virtual try-on through image-based rendering. IEEE Trans. Vis. Comput. Graph., 19(9):1552–1565, 2013. 1

    2013

  9. [18]

    Style-based global appearance flow for virtual try-on

    Sen He, Yi-Zhe Song, and Tao Xiang. Style-based global appearance flow for virtual try-on. In CVPR, 2022. 2

    2022

  10. [19]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. In NeurIPS, 2017. 5

    2017

  11. [20]

    Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adver- sarial networks. In CVPR, 2017. 5

    2017

  12. [21]

    Do not mask what you do not need to mask: A parser-free virtual try-on

    Thibaut Issenhuth, J ´er´emie Mary, and Cl ´ement Calauz`enes. Do not mask what you do not need to mask: A parser-free virtual try-on. In ECCV, 2020. 2

    2020

  13. [22]

    SC-FEGAN: face editing generative adversarial network with user’s sketch and color

    Youngjoo Jo and Jongyoul Park. SC-FEGAN: face editing generative adversarial network with user’s sketch and color. In ICCV, 2019. 1

    2019

  14. [23]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. CoRR, abs/1812.04948, 2018. 2, 6

    2018 arXiv

  15. [24]

    Analyzing and improving the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, 2020. 2

    2020

  16. [25]

    Maskgan: Towards diverse and interactive facial image ma- nipulation

    Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image ma- nipulation. In CVPR, 2020. 1

    2020

  17. [26]

    Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman

    Kathleen M. Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman. Tryongan: body-aware try-on via layered interpolation. ACM Trans. Graph., 40(4):115:1– 115:10, 2021. 1, 2

    2021

  18. [27]

    Self- correction for human parsing

    Peike Li, Yunqiu Xu, Yunchao Wei, and Yi Yang. Self- correction for human parsing. CoRR, abs/1910.09777, 2019. 5

    1910 arXiv

  19. [28]

    MAT: mask-aware transformer for large hole image in- painting

    Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia. MAT: mask-aware transformer for large hole image in- painting. In CVPR, 2022. 1

    2022

  20. [29]

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: a skinned multi- person linear model. ACM Trans. Graph. , 34(6):248:1– 248:16, 2015. 1

    2015

  21. [30]

    V-net: Fully convolutional neural networks for volumetric medical image segmentation

    Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 3DV, 2016. 5

    2016

  22. [31]

    Learn- ing to transfer texture from clothing images to 3d humans

    Aymen Mir, Thiemo Alldieck, and Gerard Pons-Moll. Learn- ing to transfer texture from clothing images to 3d humans. In CVPR, 2020. 1

    2020

  23. [32]

    Qureshi, and Mehran Ebrahimi

    Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Z. Qureshi, and Mehran Ebrahimi. Edgeconnect: Structure guided image inpainting using edge prediction. In ICCV-W, 2019. 1

    2019

  24. [33]

    Foreground-aware semantic representations for image har- monization

    Konstantin Sofiiuk, Polina Popenova, and Anton Konushin. Foreground-aware semantic representations for image har- monization. In WACV, 2021. 1

    2021

  25. [34]

    Active transfer learning for efficient video-specific human pose estimation

    Hiromu Taketsugu and Norimichi Ukita. Active transfer learning for efficient video-specific human pose estimation. In WACV, 2024. 8

    2024

  26. [35]

    Texture overlay onto flex- ible object with PCA of silhouettes and k-means method for search into database

    Hiroshi Tanaka and Hideo Saito. Texture overlay onto flex- ible object with PCA of silhouettes and k-means method for search into database. In MVA, 2009. 1

    2009

  27. [36]

    Complex volume and pose tracking with probabilistic dy- namical models and visual hull constraints

    Norimichi Ukita, Michiro Hirai, and Masatsugu Kidode. Complex volume and pose tracking with probabilistic dy- namical models and visual hull constraints. In ICCV, 2009. 8 9

    2009

  28. [37]

    Real-time shape analysis of a human body in clothing using time-series part-labeled volumes

    Norimichi Ukita, Ryosuke Tsuji, and Masatsugu Kidode. Real-time shape analysis of a human body in clothing using time-series part-labeled volumes. In ECCV, 2008. 8

    2008

  29. [38]

    Toward characteristic- preserving image-based virtual try-on network

    Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang. Toward characteristic- preserving image-based virtual try-on network. In ECCV,

  30. [39]

    Towards photo-realistic virtual try-on by adaptively generating↔preserving image content

    Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, Wang- meng Zuo, and Ping Luo. Towards photo-realistic virtual try-on by adaptively generating↔preserving image content. In CVPR, 2020. 1, 2, 5, 6, 7

    2020

  31. [40]

    Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S. Huang. Free-form image inpainting with gated convolution. In ICCV, 2019. 1

    2019

  32. [41]

    VTNFP: an image-based virtual try-on network with body and clothing feature preservation

    Ruiyun Yu, Xiaoqi Wang, and Xiaohui Xie. VTNFP: an image-based virtual try-on network with body and clothing feature preservation. In ICCV, 2019. 2

    2019

  33. [42]

    Efros, Eli Shecht- man, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 5

    2018

  34. [43]

    M3D-VTON: A monocular-to-3d virtual try-on network

    Fuwei Zhao, Zhenyu Xie, Michael Kampffmeyer, Haoye Dong, Songfang Han, Tianxiang Zheng, Tao Zhang, and Xi- aodan Liang. M3D-VTON: A monocular-to-3d virtual try-on network. In ICCV, 2021. 1

    2021

  35. [44]

    Image-based clothes animation for virtual fitting

    Zhenglong Zhou, Bo Shu, Shaojie Zhuo, Xiaoming Deng, Ping Tan, and Stephen Lin. Image-based clothes animation for virtual fitting. In SIGGRAPH, 2012. 1 10

    2012

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.