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11.发明授权公开(公告)号：US10962404B2
公开(公告)日：2021-03-30
申请号：US16830497
申请日：2020-03-26
Applicant: Bodygram, Inc.
Inventor： Kyohei Kamiyama , Chong Jin Koh , Yu Sato
Abstract: Disclosed are systems and methods for body weight prediction from one or more images. The method includes the steps of receiving one or more subject parameters; receiving one or more images containing a subject; identifying one or more annotation key points for one or more body features underneath a clothing of the subject from the one or more images utilizing one or more annotation deep-learning networks; calculating one or more geometric features of the subject based on the one or more annotation key points; and generating a prediction of the body weight of the subject utilizing a weight machine-learning module based on the one or more geometric features of the subject and the one or more subject parameters.
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公开(公告)号：US11507781B2
公开(公告)日：2022-11-22
申请号：US17309656
申请日：2019-12-17
Applicant: Bodygram, Inc.
Inventor： Chong Jin Koh , Kyohei Kamiyama
Abstract: Disclosed are systems and methods for generating large data sets for training deep learning networks (DLNs) for 3D measurements extraction from 2D images taken using a mobile device camera. The method includes the steps of receiving a 3D model of a 3D object; extracting spatial features from the 3D model; generating a first type of augmentation data for the 3D model, such as but not limited to skin color, face contour, hair style, virtual clothing, and/or lighting conditions; augmenting the 3D model with the first type of augmentation data to generate an augmented 3D model; generating at least one 2D image from the augmented 3D model by performing a projection of the augmented 3D model onto at least one plane; and generating a training data set to train the deep learning network (DLN) for spatial feature extraction by aggregating the spatial features and the at least one 2D image.
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13.发明申请公开(公告)号：US20220351378A1
公开(公告)日：2022-11-03
申请号：US17773661
申请日：2020-11-02
Applicant: Bodygram, Inc.
Inventor： Kyohei Kamiyama , Chong Jin Koh
Abstract: Disclosed are systems and methods for generating data sets for training deep learning networks for key point annotations and measurements extraction from photos taken using a mobile device camera. The method includes the steps of receiving a 3D scan model of a 3D object or subject captured from a 3D scanner and a 2D photograph of the same 3D object or subject at a virtual workspace. The 3D scan model is rigged with one or more key points. A superimposed image of a pose-adjusted and aligned 3D scan model superimposed over the 2D photograph is captured by a virtual camera in the virtual workspace. Training data for a key point annotation DLN is generated by repeating the steps for a plurality of objects belonging to a plurality of object categories. The key point annotation DLN learns from the training data to produce key point annotations of objects from 2D photographs captured using any mobile device camera.
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14.发明申请公开(公告)号：US20200319015A1
公开(公告)日：2020-10-08
申请号：US16830497
申请日：2020-03-26
Applicant: Bodygram, Inc.
Inventor： Kyohei Kamiyama , Chong Jin Koh , Yu Sato
Abstract: Disclosed are systems and methods for body weight prediction from one or more images. The method includes the steps of receiving one or more subject parameters; receiving one or more images containing a subject; identifying one or more annotation key points for one or more body features underneath a clothing of the subject from the one or more images utilizing one or more annotation deep-learning networks; calculating one or more geometric features of the subject based on the one or more annotation key points; and generating a prediction of the body weight of the subject utilizing a weight machine-learning module based on the one or more geometric features of the subject and the one or more subject parameters.
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