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:
Nov. 25, 2025

Filed:

Sep. 29, 2023
Applicant:

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

Inventors:

Songfang Han, Cupertino, CA (US);

Sergei Korolev, Marina del Rey, CA (US);

Hsin-Ying Lee, San Jose, CA (US);

Aleksei Stoliar, Marina del Rey, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 19/20 (2011.01); G06T 7/90 (2017.01);
U.S. Cl.
CPC ...
G06T 19/20 (2013.01); G06T 7/90 (2017.01); G06T 2207/10024 (2013.01); G06T 2219/2012 (2013.01);
Abstract

An artificial intelligence (AI) network or neural network is trained to generate three-dimensional (3D) models or shapes with color from two-dimensional (2D) input images and input text describing the 3D model with color. Example methods include converting a first three-dimensional (3D) model from a first representation to a second representation, the second representation including color information for the 3D model and inputting the second representation into an encoder to generate a third representation having a lower dimension than the second representation. The method further includes inputting the third representation into a decoder to generate a fourth representation having a same dimension as the second representation and generating a second 3D model from the fourth representation. The method further includes determining losses between the first 3D model and the second 3D model and updating weights of the encoder and the decoder based on the losses.


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