ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce

• URL: http://arxiv.org/abs/2304.07533v1
• Date: Sat, 15 Apr 2023 11:06:32 GMT
• Title: ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce
• Authors: Amrollah Seifoddini, Koen Vernooij, Timon K\"unzle, Alessandro Canopoli, Malte Alf, Anna Volokitin, Reza Shirvany
• Abstract summary: Smartphones provide a convenient way for users to capture images of their body. We create a new segmentation model by simplifying Semantic FPN with PointRend. We finetune this model on a high-quality dataset of humans in a restricted set of poses relevant for our application.
• Score: 57.876602177247534
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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