Apparel Generation via Cluster-Indexed Global and Local Feature Representations.

Apparel design requires expertise in aesthetics, which is challenging for non-professional general users. Inspired by the recent advances in data science, in this paper we address the task of apparel generation in a simple way by leveraging a deep neural network model. We propose to generate clothes through three selection steps from the big picture (e.g., type of clothes) to the details (e.g., color) by varying the cluster ID and latent variables. Users can go through these steps to achieve an ideal design. Experiments on a publicly available dateset demonstrate the effectiveness of our method.