公开(公告)号：US20240062390A1

公开(公告)日：2024-02-22

申请号：US18460335

申请日：2023-09-01

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06V20/40 ,  G06V10/764 ,  G06V10/82 ,  G06V40/20

CPC classification number: G06T7/246 ,  G06T7/73 ,  G06V20/46 ,  G06V10/764 ,  G06V10/82 ,  G06V40/23 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06F3/04817

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US20210125342A1

公开(公告)日：2021-04-29

申请号：US16949594

申请日：2020-11-05

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06K9/00

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US20230010480A1

公开(公告)日：2023-01-12

申请号：US17660462

申请日：2022-04-25

Applicant: Snap, Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06V20/40

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US10861170B1

公开(公告)日：2020-12-08

申请号：US16206684

申请日：2018-11-30

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06K9/00 ,  H04L29/06 ,  G06F3/0481 ,  H04L12/58

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US12165335B2

公开(公告)日：2024-12-10

申请号：US18460335

申请日：2023-09-01

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06V10/764 ,  G06V10/82 ,  G06V20/40 ,  G06V40/20 ,  G06F3/04817 ,  H04L51/04 ,  H04L67/01

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US11783494B2

公开(公告)日：2023-10-10

申请号：US17660462

申请日：2022-04-25

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06V20/40 ,  G06V10/764 ,  G06V10/82 ,  G06V40/20 ,  G06F3/04817 ,  H04L51/04 ,  H04L67/01

CPC classification number: G06T7/246 ,  G06T7/73 ,  G06V10/764 ,  G06V10/82 ,  G06V20/46 ,  G06V40/23 ,  G06F3/04817 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196 ,  H04L51/04 ,  H04L67/01

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

公开(公告)号：US11315259B2

公开(公告)日：2022-04-26

申请号：US16949594

申请日：2020-11-05

Applicant: Snap Inc.

Inventor： Yuncheng Li ,  Linjie Luo ,  Xuecheng Nie ,  Ning Zhang

IPC: G06T7/246 ,  G06T7/73 ,  G06K9/00 ,  H04L67/01 ,  G06F3/04817 ,  H04L51/04

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.