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    Generating data in a messaging system for a machine learning model
    14.
    发明授权
    Generating data in a messaging system for a machine learning model 有权

    公开(公告)号：US11599741B1

    公开(公告)日：2023-03-07

    申请号：US16774796

    申请日：2020-01-28

    Applicant: Snap Inc.

    Inventor： Zehao Xue ,  Zhou Ren

    IPC: G06K9/62 ,  G06N20/00 ,  G06V20/68

    Abstract: Systems and methods are provided for analyzing, by a computing device, location data associated with a location of the computing device to determine that an image or video captured using a messaging application on the computing device is captured near a food-related venue or event, receiving input related to food associated with the food-related venue or event, sending the image or video and the input related to food associated with the food-related venue or event to a computing system to train a machine learning model for food detection, and updating the messaging application to comprise the trained machine learning model for food detection.

    3D HAND SHAPE AND POSE ESTIMATION
    15.
    发明申请
    3D HAND SHAPE AND POSE ESTIMATION 有权

    公开(公告)号：US20220414985A1

    公开(公告)日：2022-12-29

    申请号：US17823764

    申请日：2022-08-31

    Applicant: Snap Inc.

    Inventor： Liuhao Ge ,  Zhou Ren ,  Yuncheng Li ,  Zehao Xue ,  Yingying Wang

    IPC: G06T17/20 ,  G06T7/73 ,  G06N3/08 ,  G06N3/04

    Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for receiving a monocular image that includes a depiction of a hand and extracting features of the monocular image using a plurality of machine learning techniques. The program and method further include modeling, based on the extracted features, a pose of the hand depicted in the monocular image by adjusting skeletal joint positions of a three-dimensional (3D) hand mesh using a trained graph convolutional neural network (CNN); modeling, based on the extracted features, a shape of the hand in the monocular image by adjusting blend shape values of the 3D hand mesh representing surface features of the hand depicted in the monocular image using the trained graph CNN; and generating, for display, the 3D hand mesh adjusted to model the pose and shape of the hand depicted in the monocular image.

    3D HAND SHAPE AND POSE ESTIMATION
    16.
    发明申请
    3D HAND SHAPE AND POSE ESTIMATION 有权

    公开(公告)号：US20210225077A1

    公开(公告)日：2021-07-22

    申请号：US17222176

    申请日：2021-04-05

    Applicant: Snap Inc.

    Inventor： Liuhao Ge ,  Zhou Ren ,  Yuncheng LI ,  Zehao Xue ,  Yingying Wang

    IPC: G06T17/20 ,  G06T7/73 ,  G06N3/08 ,  G06N3/04

    Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for receiving a monocular image that includes a depiction of a hand and extracting features of the monocular image using a plurality of machine learning techniques. The program and method further include modeling, based on the extracted features, a pose of the hand depicted in the monocular image by adjusting skeletal joint positions of a three-dimensional (3D) hand mesh using a trained graph convolutional neural network (CNN); modeling, based on the extracted features, a shape of the hand in the monocular image by adjusting blend shape values of the 3D hand mesh representing surface features of the hand depicted in the monocular image using the trained graph CNN; and generating, for display, the 3D hand mesh adjusted to model the pose and shape of the hand depicted in the monocular image.

    Virtual object machine learning
    17.
    发明授权
    Virtual object machine learning 有权

    公开(公告)号：US11030454B1

    公开(公告)日：2021-06-08

    申请号：US16777098

    申请日：2020-01-30

    Applicant: Snap Inc.

    Inventor： Xuehan Xiong ,  Zehao Xue

    IPC: G06K9/00 ,  G06N20/00 ,  G06T17/20

    Abstract: A machine learning scheme can be trained on a set of labeled training images of a subject in different poses, with different textures, and with different background environments. The label or marker data of the subject may be stored as metadata to a 3D model of the subject or rendered images of the subject. The machine learning scheme may be implemented as a supervised learning scheme that can automatically identify the labeled data to create a classification model. The classification model can classify a depicted subject in many different environments and arrangements (e.g., poses).

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