AI Assisted Apparel Design

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.

Image Segmentation of Multi-Shaped Overlapping Objects

In this work, we propose a new segmentation algorithm for images containing convex objects present in multiple shapes with a high degree of overlap. The proposed algorithm is carried out in two steps, first we identify the visible contours, segment them using concave points and finally group the segments belonging to the same object. The next step is to assign a shape identity to these grouped contour segments. For images containing objects in multiple shapes we begin first by identifying shape classes of the contours followed by assigning a shape entity to these classes. We provide a comprehensive experimentation of our algorithm on two crystal image datasets. One dataset comprises of images containing objects in multiple shapes overlapping each other and the other dataset contains standard images with objects present in a single shape. We test our algorithm against two baselines, with our proposed algorithm outperforming both the baselines.