mlsanityData to 14 Oct 2026

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

arXiv 2304.07533 ↗First posted 15 Apr 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 15 Apr 2023.

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14 of 34 references.

References · 14

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2 more
  1. 2020BlazePose: On-device Real-time Body Pose trackingcited by 113
  2. 2019ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modulescited by 4

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