Journal of Textile Research ›› 2024, Vol. 45 ›› Issue (09): 164-174.doi: 10.13475/j.fzxb.20230904501

• Apparel Engineering • Previous Articles     Next Articles

Lightweight parser-free virtual try-on based on mixed knowledge distillation and feature enhancement techniques

HOU Jue1,2, DING Huan1, YANG Yang1,2, LU Yinwen1, YU Lingjie3, LIU Zheng2,4()   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Key Laboratory of Silk Culture Inheritance and Digital Technology of Product Design, Ministry of Culture and Tourism, Hangzhou, Zhejiang 310018, China
    3. School of Textile Science and Engineering, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
    4. International Institute of Fashion Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2023-09-18 Revised:2024-03-23 Online:2024-09-15 Published:2024-09-15
  • Contact: LIU Zheng E-mail:koala@zstu.edu.cn

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Abstract:

Objective In order to address the issues of low accuracy in clothing deformation, texture distortion, and high computational costs in image-based virtual try-on systems, this paper proposes a lightweight parser-free virtual try-on based on mixed knowledge distillation and feature enhancement techniques.

Method Firstly, by integrating global features and calibrating the results of flow computation at different scales, an improved appearance flow estimation method was proposed to enhance the accuracy of appearance flow estimation. Moreover, a lightweight try-on network based on depth separable convolution was constructed by decoupling image segmentation results and virtual try-on processes using knowledge distillation. Finally, a garment complexity index GTC (garment texture complexity) based on the pixel-wise average gradient was proposed to quantitatively analyze the texture complexity of clothing. Based on this, the VITON dataset is divided into a simple texture set, a moderately complex texture set, and a highly complex texture set.

Results This paper used the VITON dataset to verify and analyze the proposed model. Compared with the SOTA (state-of-art) model, the number of parameters and computational complexity (flops) was decreased by 70.12% and 42.38%, respectively, suggesting a faster and better model to meet the deployment requirements of the mobile Internet. Moreover, the experimental results showed that the scores of the proposed model in image quality evaluation indicators (FID, LPIPS, PSNR, KID) were increased by 5.06%, 28.57%, 3.71%, and 33.33%, respectively, compared with the SOTA model. In the segmentation analysis of clothing complexity, the score of KID and LPIPS in this model was 48.08%, 30.45%, 1.03%, 35.54%, 30.41%, and 12.94% higher than that of the SOTA model, respectively, proving that the method proposed is superior to other methods in restoring and preserving original clothing details when warping clothing images with complex textures.

Conclusion A lightweight parser-free virtual try-on based on mixed knowledge distillation and feature enhancement techniques is proposed, which uses an efficient appearance flow estimation method to reduce registration errors, complex texture loss, and distortion during the clothing distortion process. In addition, the method proposed is shown to reduce the size and computational complexity of the final model by mixing distillation and using depth-separable convolution effectively and speeding up the running of the model. Finally, a quantitative index used for characterizing the complexity of clothing texture is proposed and the VITON test set is divided into samples. Compared with other virtual try-on methods, the experimental results show that on the VITON test set, the evaluation index results obtained from the proposed method are better than the current virtual try-on method with the best performance, and the ability of the proposed method to deal with clothing with complex patterns is also better than other methods. In addition, the ablation experiment proves that the proposed method has an obvious improvement on the final virtual try-on result.

Key words: virtual try-on, appearance flow, knowledge distillation, feature enhancement technique, garment texture complexity, apparel e-commerce

CLC Number: 

  • TS942.8

Fig.1

Network backbone"

Fig.2

Backbone of coarse flow estimator"

Fig.3

Basic Res-Net structure of each model. (a) Basic Res-Net structure of GN-T; (b) Basic Res-Net structure of GN-S"

Tab.1

Quantitative comparison of methods on VITON test dataset"

方法 FID SSIM LPIPS KID/10-2 PSNR
CP-VTON+ 22.36 0.82 0.12 0.92 21.81
ACGPN 17.95 0.84 0.11 0.73 23.11
PF-AFN 10.09 0.89 0.06 0.21 27.25
Style-VTON 8.89 0.91 0.07 0.12 26.70
RT-VTON 11.66 — — — —
SDAFN 10.42 0.87 0.08 0.16 26.48
本文模型 8.44 0.90 0.05 0.08 27.69

Fig.4

Six methods in each garment complexity segmentation of image evaluation quantization map"

Tab.2

Comparison of model size and computational complexity of six methods"

方法 模型参数量/106 计算复杂度/108
CP-VTON+ 40.41 136.0
ACGPN >100 >1 000
SDAFN 37.65 747.0
PF-AFN 73.20 689.3
Style-VTON 88.41 542.9
本文模型 26.41 312.9

Fig.5

Qualitative comparison of methods on VITON test dataset. (a) Clothing style 1; (b) Clothing style 2; (c) Clothing style 3; (d) Clothing style 4; (e) Clothing style 5; (f) Clothing style 6"

Tab.3

Quantitative comparison form model configurations"

模型
配置		 外观流
校准		 粗糙流
估计		 精细流
估计		 FID KID/10-2 SSIM
配置1 √ 9.62 0.115 0.891
配置2 √ √ 9.41 0.110 0.893
配置3 √ √ 8.54 0.089 0.902
配置4 √ √ √ 8.44 0.083 0.903

Fig.6

Qualitative comparison for model configurations. (a) Colthing style 7; (b) Clothing style 8"

Tab.4

Quantitative comparison for different training loss function configurations"

方法 FID SSIM LPIPS KID/10-2 PSNR
未加入 L S K D 8.60 0.89 0.06 0.09 27.48
加入 L S K D 8.44 0.90 0.05 0.08 27.69
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