Accurately estimating human body shape from photos can enable innovative applications in fashion, from mass customization, to size and fit recommendations and virtual try-on. Body silhouettes calculated from user pictures are effective representations of the body shape for downstream tasks. Smartphones provide a convenient way for users to capture images of their body, and on-device image processing allows predicting body segmentation while protecting users privacy. Existing off-the-shelf methods for human segmentation are closed source and cannot be specialized for our application of body shape and measurement estimation. Therefore, we create a new segmentation model by simplifying Semantic FPN with PointRend, an existing accurate model. We finetune this model on a high-quality dataset of humans in a restricted set of poses relevant for our application. We obtain our final model, ALiSNet, with a size of 4MB and 97.6±1.0% mIoU, compared to Apple Person Segmentation, which has an accuracy of 94.4±5.7% mIoU on our dataset.
@article{arxiv.2304.07533,
title = {ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce},
author = {Amrollah Seifoddini and Koen Vernooij and Timon Künzle and Alessandro Canopoli and Malte Alf and Anna Volokitin and Reza Shirvany},
journal= {arXiv preprint arXiv:2304.07533},
year = {2023}
}