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@inproceedings{WACV2022_Fele,

title = {C-VTON: Context-Driven Image-Based Virtual Try-On Network},

author = {Benjamin Fele and Ajda Lampe and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/12/WACV2022_Benjamin_compressed-1.pdf},

year  = {2022},

date = {2022-01-04},

urldate = {2022-01-04},

booktitle = {IEEE/CVF Winter Applications in Computer Vision (WACV)},

pages = {1--10},

abstract = {Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and  convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art.},

keywords = {computer vision, deepbeauty, fashion, generative models, image editing, try-on, virtual try-on},

pubstate = {published},

tppubtype = {inproceedings}

}