FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
Outfit generation is a challenging task in the field of fashion technology,
in which the aim is to create a collocated set of fashion items that complement
a given set of items. Previous studies in this area have been limited to
generating a unique set of fashion items based on a given set of items, without
providing additional options to users. This lack of a diverse range of choices
necessitates the development of a more versatile framework. However, when the
task of generating collocated and diversified outfits is approached with
multimodal image-to-image translation methods, it poses a challenging problem
in terms of non-aligned image translation, which is hard to address with
existing methods. In this research, we present FCBoost-Net, a new framework for
outfit generation that leverages the power of pre-trained generative models to
produce multiple collocated and diversified outfits. Initially, FCBoost-Net
randomly synthesizes multiple sets of fashion items, and the compatibility of
the synthesized sets is then improved in several rounds using a novel fashion
compatibility booster. This approach was inspired by boosting algorithms and
allows the performance to be gradually improved in multiple steps. Empirical
evidence indicates that the proposed strategy can improve the fashion
compatibility of randomly synthesized fashion items as well as maintain their
diversity. Extensive experiments confirm the effectiveness of our proposed
framework with respect to visual authenticity, diversity, and fashion
compatibility.
