Real-time, automatic digi-tailor mannequin robot adjustment based on human body classification through supervised learning

Abstract

Although mannequin robots have been in use in the context of fit advising, most of the modules involved in the process of online try-on still demand manual calculations, operations and adjustments. This article overcomes the latter deficiency, alleviates the time consumption and brings about significant enhancements to the efficiency and reliability of the foregoing service through coming up with a fully automatic solution. Notions and practices aimed at the classification of 3D scanning instances of human body using a laser scanner are explained, along with the subsequent automatic activation of the mannequin robots, upon presentation of the experimental results. The proposed methodology consists in scanning, classifying according to gender and size and performing analysis on the user's body, modelling and extracting measurements from the 3D visual data imported from the mannequins, and finally, photoshooting the garment being put on the user's body. In order to classify the data obtained by the 3D scanner, first, maximum likelihood function is used for selecting one of the digi-tailor mannequin robots, according to the presumed gender and size, to be activated, and then support vector machine is utilized so as to find out which shape template from the dictionary best matches the scanning instance being considered. The proposed automatic methodology is also compared with the currently used manual method, and the experimental results easily approve its accuracy and reliability.

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