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$\pm$1.0$\%$ mIoU, compared to Apple Person Segmentation, which has an accuracy of 94.4$\pm$5.7$\%$ mIoU on our dataset.

本文描述了使用智能手机拍摄人体照片并使用于保护用户隐私的图片处理技术，使用基于语义的FPN模型辅以PointRend技术，在特定姿势的高质量数据集上进行微调，从而创建了一个体积为4MB，精度为97.6±1.0%的新模型ALiSNet，以及它的主要应用如人体形状估计和衣衫搭配等。

ALiSNet：时尚电商精准轻量人像分割网络