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    SEQUENCE-OF-SEQUENCES MODEL FOR 3D OBJECT RECOGNITION
    14.
    发明申请
    SEQUENCE-OF-SEQUENCES MODEL FOR 3D OBJECT RECOGNITION 有权

    公开(公告)号：US20230034794A1

    公开(公告)日：2023-02-02

    申请号：US17878591

    申请日：2022-08-01

    Applicant: Snap Inc.

    Inventor： Yuncheng Li ,  Zhou Ren ,  Ning Xu ,  Enxu Yan ,  Tan Yu

    IPC: G06V20/64 ,  G06T7/55 ,  G06T7/33 ,  G06K9/62 ,  G06N3/08

    Abstract: Systems and methods are disclosed for capturing multiple sequences of views of a three-dimensional object using a plurality of virtual cameras. The systems and methods generate aligned sequences from the multiple sequences based on an arrangement of the plurality of virtual cameras in relation to the three-dimensional object. Using a convolutional network, the systems and methods classify the three-dimensional object based on the aligned sequences and identify the three-dimensional object using the classification.

    3D hand shape and pose estimation
    15.
    发明授权
    3D hand shape and pose estimation 有权

    公开(公告)号：US11468636B2

    公开(公告)日：2022-10-11

    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.

    3D hand shape and pose estimation
    18.
    发明授权
    3D hand shape and pose estimation 有权

    公开(公告)号：US10796482B2

    公开(公告)日：2020-10-06

    申请号：US16210927

    申请日：2018-12-05

    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.

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