  Fashion is a fast-changing industry where designs are refreshed at large
scale every season. Moreover, it faces huge challenge of unsold inventory as
not all designs appeal to customers. This puts designers under significant
pressure. Firstly, they need to create innumerous fresh designs. Secondly, they
need to create designs that appeal to customers. Although we see advancements
in approaches to help designers analyzing consumers, often such insights are
too many. Creating all possible designs with those insights is time consuming.
In this paper, we propose a system of AI assistants that assists designers in
their design journey. The proposed system assists designers in analyzing
different selling/trending attributes of apparels. We propose two design
generation assistants namely Apparel-Style-Merge and Apparel-Style-Transfer.
Apparel-Style-Merge generates new designs by combining high level components of
apparels whereas Apparel-Style-Transfer generates multiple customization of
apparels by applying different styles, colors and patterns. We compose a new
dataset, named DeepAttributeStyle, with fine-grained annotation of landmarks of
different apparel components such as neck, sleeve etc. The proposed system is
evaluated on a user group consisting of people with and without design
background. Our evaluation result demonstrates that our approach generates high
quality designs that can be easily used in fabrication. Moreover, the suggested
designs aid to the designers creativity.
