Dual-Branch Collaborative Transformer for Virtual Try-On

Fenocchi, Emanuele; Morelli, Davide; Cornia, Marcella; Baraldi, Lorenzo; Cesari, Fabio; Cucchiara, Rita

doi:10.1109/cvprw56347.2022.00246

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…A neural network was also used to learn the transformation parameters of TPS in CP-VITON [81]. Later, Fenocchi et al [82] introduced self-and cross-attention operations to the warping module. They aligned the refined representation of a person and an in-shop garment using two-branch cross-modal attention blocks.…”

Section: Virtual Try-onmentioning

confidence: 99%

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Zhang

Shi

et al. 2023

Preprint

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With the development of artificial intelligence (AI) and the constraints on offline activities imposed due to the sudden outbreak of the COVID epidemic, the Metaverse has recently attracted significant research attention from both academia and industrial practitioners. Fashion, as an expression of a consumer’s aesthetics and personality, has enormous economic potential in both the real world and the Metaverse. In this research, we provide a comprehensive survey of two of the most important components of fashion in the Metaverse: virtual digital humans, and tasks related to fashion items. We survey state-of-the-art articles from 2007 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the applications of these topics in the Metaverse from the perspectives of designers and consumers. Finally, we describe possible scenes involving fashion in the Metaverse. The current challenges and open issues related to the fashion industry in the Metaverse are also discussed in order to provide guidance for fashion practitioners, and to shed some light on the future development of fashion AI in the Metaverse.

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Section: Virtual Try-onmentioning

confidence: 99%

Fashion Intelligence in the Metaverse: Promise and Future Prospects

Zhang

Shi

et al. 2023

Preprint

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“…For instance, context-driven virtual try-on network (C-VTON) ( 38 ) employs discriminators specific to different types of contextual information, allowing enhanced clothing synthesis quality. The dual-branch collaborative transformer (DBCT) ( 39 ) utilizes a transformer-based architecture and cross-modal information to improve the virtual try-on process. To address the alignment of target garments to corresponding body parts, a novel global appearance flow estimation model has been proposed, which warps clothing spatially ( 40 ).…”

Section: Related Workmentioning

confidence: 99%

Anonymizing at-home fitness: enhancing privacy and motivation with virtual reality and try-on

Yoon,

Jeong,

Kim

et al. 2023

Front. Public Health

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IntroductionThis study aimed to address privacy concerns associated with video conferencing tools used in home-based exercise training. To that end, a method that could anonymize participants' appearances and exercise environments during at-home fitness sessions was proposed.MethodsThis method combines virtual reality for 3-D human-model rendering using key-points tracking with a virtual try-on system enhanced by UV mapping and instance segmentation. To validate the proposed method, we conducted a user study by recruiting participants to assess effectiveness of virtual reality and virtual try-on in terms of privacy protection, self-confidence, and coaching satisfaction.ResultsExperimental results demonstrated the effectiveness and improved user experience of using virtual reality or virtual try-on in remote fitness, particularly in enhancing privacy protection and self-confidence with statistical significance. However, no significant differences were noted in coaching satisfaction.DiscussionThese findings confirmed the efficacy of our proposed approach. We believe that the proposed approach can significantly contribute to the future of remote fitness training, offering a more secure and engaging environment for users, thereby potentially increasing adherence to fitness regimens and overall physical wellbeing.

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“…Virtual Try-on. Virtual try-on approaches can be categorized into 3D-based [7,21,30,38] and image-based methods [9,17,22,23,28,29,32,40,41,51]. Imagebased methods are more promising because of their lightweight nature and the ability to generate reasonable results using large-scale try-on datasets.…”

Section: Related Workmentioning

confidence: 99%