Towards Automated Garment Measurements In the Wild Using Landmark and Depth Estimation

Abstract

This research introduces an innovative approach to automate garment measurements from photos, combining depth estimation and landmark detection to address the high return rates in the fashion industry due to inaccurate sizing. Utilizing the DeepFashion2 dataset and a custom set of images, we employ DepthAnything for depth estimation and Keypoint R-CNN for landmark estimation, advancing previous methodologies by offering a scalable and accurate solution for the fashion industry. Initial findings suggest promising avenues for reducing returns and enhancing the garment fitting processes.