Multi-Pose Virtual Try-On Via Self-Adaptive Feature Filtering

Du, Chenghu; Yu, Feng; Jiang, Minghua; Xiong, Wei; Peng, Tao; Hu, Xinrong

doi:10.1109/icassp43922.2022.9747847

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Most virtual try-on models have three major modules: segmentation, warping, and try-on synthesis [41], [49], [182]. The segmentation module is responsible for generating a semantic layout that aligns with the desired target pose.…”

Section: Multi-pose Virtual Try-onmentioning

confidence: 99%

“…The segmentation module is responsible for generating a semantic layout that aligns with the desired target pose. There are two ways in which a multi-pose virtual try-on model can warp a garment: either through a commonly used TPS [41], [206] or appearance flow [49], [198]. With both methods, the garment is warped and transformed to align with the target pose.…”

Section: Multi-pose Virtual Try-onmentioning

confidence: 99%

“…That being said, the models that performed best in the FID metric are SS-VTON [69] for 2022 and VTON-MP [198] for 2023. The models that performed best in the SSIM metric are MV-TON [49] for 2022 and CF-VTON [46] for 2023. TABLE 8 presents the quantitative evaluation of video virtual try-on models.…”

Section: E Comparisonmentioning

confidence: 99%

“…Higher values indicate better results for SSIM and IS, while lower values are desirable for FID and LPIPS. Some scores have been reported in papers other than the original publication: α: TB-VTON [182], β: CF-VTON [46]. using virtual try-on technology results in a positive hedonic experience for customers [28], [101], [107].…”

Section: Tablementioning

confidence: 99%

See 3 more Smart Citations

Deep Learning in Virtual Try-On: A Comprehensive Survey

Islam,

Miron,

Liu

et al. 2024

IEEE Access

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Virtual try-on technology has gained significant importance in the retail industry due to its potential to transform the way customers interact with products and make purchase decisions. It allows users to virtually try on clothing and accessories, providing a realistic representation of how the items would look and fit without the need for physical interaction. The ability to virtually try on products addresses common challenges associated with online shopping, such as uncertainty about fit and style, ultimately enhancing the overall customer experience and satisfaction. As a result, virtual try-on technology has the potential to reduce returns and optimise conversion rates for businesses, making it a valuable tool in the e-commerce landscape. In this paper, we provide a comprehensive review of deep learning based virtual try-on models, focusing on their functionality, technical details, dataset usage, weaknesses, and impact on customer satisfaction. The models are categorised into three main types: image-based, multi-pose, and video virtual try-on models, with detailed examples and technical summaries provided for each category. Additionally, we identify and discuss similarities and differences in these methods. Furthermore, we examine the datasets currently available for building and evaluating virtual try-on models, including the number of images/videos and their resolutions. We present the commonly used methods for both qualitative and quantitative evaluations, comparing synthesised images with previous work and performing quantitative evaluations across various metrics and benchmark datasets. We discuss the weaknesses of current deep learning based virtual try-on models, including challenges in preserving clothing characteristics and textures, the level of accuracy of applying the clothing to the person, and the preservation of facial identities. Additionally, we address dataset bias, particularly the domination of female models, limited diversity in clothing featured, and relatively simple and clean backgrounds in the datasets, which can negatively impact the model's ability to handle challenging situations. Moreover, we explore the impact of virtual try-ons on customer satisfaction, highlighting the benefits that customers can enjoy, which also reduces returns and optimises conversion rates for businesses.INDEX TERMS Virtual try-on (VTO), deep learning, image synthesis, generative adversarial networks (GANs), diffusion models (DMs).

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Section: Multi-pose Virtual Try-onmentioning

confidence: 99%