An Application to Generate Style Guided Compatible Outfit
Authors: 
Debopriyo Banerjee, Harsh Maheshwari, Lucky Dhakad, Arnab Bhattacharya, Niloy Ganguly, Muthusamy Chelliah, Suyash AgarwalAuthors Info & Claims
Pages 260 - 264
Abstract
Fashion recommendation has witnessed a phenomenal growth of research, particularly in the domains of shop-the-look, context-aware outfit creation, personalizing outfit creation etc. Majority of the work in this area focuses on better understanding of the notion of complimentary relationship between lifestyle items. Quite recently, some works have realised that style plays a vital role in fashion, especially in the understanding of compatibility learning and outfit creation. In this paper, we would like to present the end-to-end design of a methodology in which we aim to generate outfits guided by styles or themes using a novel style encoder network. We present an extensive analysis of different aspects of our method through various experiments. We also provide a demonstration api to showcase the ability of our work in generating outfits based on an anchor item and styles.
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References
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Published In
January 2022
357 pages
ISBN:9781450385824
DOI:10.1145/3493700
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Published: 08 January 2022
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CODS-COMAD 2022
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CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)
January 8 - 10, 2022
Bangalore, India
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