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. 25, 2025

Filed:

Jul. 19, 2019
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Mikael Pierre Bonnevie, Walnut Creek, CA (US);

Aaron Maschinot, Somerville, MA (US);

Aaron Sarna, Cambridge, MA (US);

Shuchao Bi, Mountain View, CA (US);

Jingbin Wang, Mountain View, CA (US);

Michael Spencer Krainin, Arlington, MA (US);

Wenchao Tong, San Jose, CA (US);

Dilip Krishnan, Arlington, MA (US);

Haifeng Gong, Fremont, CA (US);

Ce Liu, Cambridge, MA (US);

Hossein Talebi, San Jose, CA (US);

Raanan Sayag, San Jose, CA (US);

Piotr Teterwak, Boston, MA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06N 3/045 (2023.01); G06T 7/10 (2017.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/045 (2023.01); G06T 7/10 (2017.01); G06T 2207/20132 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.


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