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arXiv · 1906.02222

Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser Smartphone AR Applications

Brendan Duke·Abdalla Ahmed·Edmund Phung·Irina Kezele·Parham Aarabi
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Abstract

We provide a system for semantic segmentation of small objects that enables nail polish try-on AR applications to run client-side in realtime in native and web mobile applications. By adjusting input resolution and neural network depth, our model design enables a smooth trade-off of performance and runtime, with the highest performance setting achieving~\num{94.5} mIoU at 29.8ms runtime in native applications on an iPad Pro. We also provide a postprocessing and rendering algorithm for nail polish try-on, which integrates with our semantic segmentation and fingernail base-tip direction predictions.

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Brendan Duke, Abdalla Ahmed, Edmund Phung, Irina Kezele, Parham Aarabi. 2019-06-05. Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser Smartphone AR Applications. https://arxiv.org/abs/1906.02222

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