Progressive Limb-Aware Virtual Try-On

Chenyang Wang; Qinglin Liu; Shengping Zhang; Xiaoyu Han; Zonglin Li

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Progressive Limb-Aware Virtual Try-On

Xiaoyu Han, Shengping Zhang, Qinglin Liu, Zonglin Li, Chenyang Wang

Computer Vision and Pattern Recognition · Submitted 16 Mar 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.06 · human participants 0.10 · controlled comparison 0.53 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.93 · mentions code or data availability 0.98.

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Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/PL-VTON.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances.	background 0.65	0.03	0.04	asserted 1.00
s1	In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments.	background 0.76	0.04	0.02	asserted 0.99
s2	To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results.	contribution 0.98	0.91	0.17	asserted 1.00
s3	Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements.	method 0.92	0.95	0.03	asserted 1.00
s4	A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions.	method 0.94	0.92	0.03	asserted 1.00
s5	Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features.	method 0.85	0.95	0.04	asserted 0.99
s6	Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.	result 1.00	0.95	0.84	asserted 1.00
s7	The code is available at https://github.com/xyhanHIT/PL-VTON.	contribution 0.45	0.39	0.02	asserted 0.97

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

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The paper

Original abstract

Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the transferred clothing geometry and textures, which causes incomplete and blurred clothing appearances. In addition, these methods usually mask the limb textures of the input for the clothing-agnostic person representation, which results in inaccurate predictions for human limb regions (i.e., the exposed arm skin), especially when transforming between long-sleeved and short-sleeved garments. To address these problems, we present a progressive virtual try-on framework, named PL-VTON, which performs pixel-level clothing warping based on multiple attributes of clothing and embeds explicit limb-aware features to generate photo-realistic try-on results. Specifically, we design a Multi-attribute Clothing Warping (MCW) module that adopts a two-stage alignment strategy based on multiple attributes to progressively estimate pixel-level clothing displacements. A Human Parsing Estimator (HPE) is then introduced to semantically divide the person into various regions, which provides structural constraints on the human body and therefore alleviates texture bleeding between clothing and limb regions. Finally, we propose a Limb-aware Texture Fusion (LTF) module to estimate high-quality details in limb regions by fusing textures of the clothing and the human body with the guidance of explicit limb-aware features. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/PL-VTON.

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States the same result 1 paper

Extensive experiments demonstrate that our PL-VTON outperforms the state-of-the-art methods both qualitatively and quantitatively.

Limb-Aware Virtual Try-On Network with Progressive Clothing Warping · 1.00

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Uses the method or tool of 2 papers

The clothing loss $L_cloth$ and the perceptual loss $L_vgg$ are formulated as: [equation] [equation] where $ϕ_i(·)$ denotes the feature maps of the i-th layer in the visual perception network VGG19 [ref] pre-trained on ImageNet [ref] , and $λ_i$ is the corresponding weight.

Very Deep Convolutional Networks for Large-Scale Image Recognition · Loss · 0.92
Adam [ref] optimizer is used with $β_1=0.5$ and $β_2=0.999$ in both stages.

Adam: A Method for Stochastic Optimization · Implementation Details · 1.00

Cites as support 1 paper

Note that Inception Score (IS) is abandoned because Rosca et.al [ref] have demonstrated that there are misleading results when IS is applied to the model trained on the datasets other than ImageNet.

Variational Approaches for Auto-Encoding Generative Adversarial Networks · Quantitative Results · 1.00

Cites as background 3 papers

Existing methods for virtual try-on are mainly based on 3D modeling [ref] or 2D images [ref] .

Detailed Garment Recovery from a Single-View Image · Fashion Analysis and Virtual Try-on · 0.99
Document image rectification [ref] adopts the appearance flow network to regress a dense 2D vector field that samples pixels from the distorted document images to the rectified ones.

DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction · Flow Estimation · 0.81
Document image rectification [ref] adopts the appearance flow network to regress a dense 2D vector field that samples pixels from the distorted document images to the rectified ones.

DocScanner: Robust Document Image Rectification with Progressive Learning · Flow Estimation · 0.81

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