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:
Aug. 19, 2025

Filed:

Jun. 22, 2023
Applicant:

Snap Inc., Santa Monica, CA (US);

Inventors:

Riza Alp Guler, London, GB;

Frank Lu, London, GB;

Georgios Papandreou, London, GB;

Haoyang Wang, London, GB;

Assignee:

SNAP INC., Santa Monica, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 17/20 (2006.01); G06T 7/73 (2017.01); G06T 13/40 (2011.01); G06T 19/00 (2011.01);
U.S. Cl.
CPC ...
G06T 17/20 (2013.01); G06T 7/73 (2017.01); G06T 13/40 (2013.01); G06T 19/006 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30201 (2013.01);
Abstract

Methods and systems are disclosed for generating a body mesh from a single image. The system predicts both a volumetric reconstruction tensor of the monocular image and a pose of an object by applying a first machine learning model to a monocular image. The system identifies a portion of the pose of the object that corresponds to a point in a canonical space associated with a set of position encoding information. The system obtains a point of the volumetric reconstruction tensor corresponding to the identified portion of the pose. The system classifies the obtained point as being inside or outside of a canonical volume by applying a second machine learning model to the obtained point of the volumetric reconstruction tensor together with the set of position encoding information. The system generates a three-dimensional (3D) mesh representing the object in the canonical space.


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