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:
Apr. 23, 2024

Filed:

Jan. 11, 2023
Applicant:

Shanghai United Imaging Intelligence Co., Ltd., Shanghai, CN;

Inventors:

Ziyan Wu, Lexington, MA (US);

Srikrishna Karanam, Bangalore, IN;

Changjiang Cai, Secaucus, NJ (US);

Georgios Georgakis, Philadelphia, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/21 (2023.01); A61B 5/00 (2006.01); G06F 18/214 (2023.01); G06T 7/00 (2017.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06T 7/90 (2017.01); G06T 17/00 (2006.01); G06T 17/20 (2006.01); G06V 10/40 (2022.01); G06V 10/42 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/62 (2022.01); G06V 20/64 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
A61B 5/0077 (2013.01); A61B 5/0035 (2013.01); A61B 5/70 (2013.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2193 (2023.01); G06T 7/0012 (2013.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06T 7/90 (2017.01); G06T 17/00 (2013.01); G06T 17/20 (2013.01); G06V 10/40 (2022.01); G06V 10/42 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/7796 (2022.01); G06V 10/82 (2022.01); G06V 20/62 (2022.01); G06V 20/64 (2022.01); G06V 40/10 (2022.01); G06V 40/20 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06T 2200/08 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30196 (2013.01); G06V 2201/033 (2022.01);
Abstract

The pose and shape of a human body may be recovered based on joint location information associated with the human body. The joint location information may be derived based on an image of the human body or from an output of a human motion capture system. The recovery of the pose and shape of the human body may be performed by a computer-implemented artificial neural network (ANN) trained to perform the recovery task using training datasets that include paired joint location information and human model parameters. The training of the ANN may be conducted in accordance with multiple constraints designed to improve the accuracy of the recovery and by artificially manipulating the training data so that the ANN can learn to recover the pose and shape of the human body even with partially observed joint locations.


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