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- W4366342296 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." @default.
- W4366342296 created "2023-04-20" @default.
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- W4366342296 date "2023-04-15" @default.
- W4366342296 modified "2023-09-27" @default.
- W4366342296 title "ALiSNet: Accurate and Lightweight Human Segmentation Network for Fashion E-Commerce" @default.
- W4366342296 doi "https://doi.org/10.48550/arxiv.2304.07533" @default.
- W4366342296 hasPublicationYear "2023" @default.
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