The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 20, 2024

Filed:

May. 25, 2021
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Jingmin Luo, Shenzhen, CN;

Xiaolong Zhu, Shenzhen, CN;

Yitong Wang, Shenzhen, CN;

Xing Ji, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06T 7/73 (2017.01); G06V 40/10 (2022.01); G06V 20/64 (2022.01); G06V 10/764 (2022.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06T 7/74 (2017.01); G06V 10/764 (2022.01); G06V 20/647 (2022.01); G06V 40/10 (2022.01); G06V 40/103 (2022.01); G06V 40/23 (2022.01);
Abstract

This application provides a method for training a pose recognition model performed at a computer device. The method includes: inputting a sample image labeled with human body key points into a feature map model included in a pose recognition model, to output a feature map of the sample image; inputting the feature map into a two-dimensional (2D) model included in the pose recognition model, to output 2D key point parameters used for representing a 2D human body pose; input a target human body feature map cropped from the feature map and the 2D key point parameter into a three-dimensional (3D) model included in the pose recognition model, to output 3D pose parameters used for representing a 3D human body pose; constructing a target loss function based on the 2D key point parameters and the 3D pose parameters; and updating the pose recognition model based on the target loss function.


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