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:
Jan. 20, 2026

Filed:

Apr. 28, 2021
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Chloe Legendre, Culver City, CA (US);

Paul Debevec, Culver City, CA (US);

Sean Ryan Francesco Fanello, San Francisco, CA (US);

Rohit Kumar Pandey, Mountain View, CA (US);

Sergio Orts Escolano, San Francisco, CA (US);

Christian Haene, Berkeley, CA (US);

Sofien Bouaziz, Los Gatos, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/50 (2006.01); G06T 7/194 (2017.01); G06V 10/56 (2022.01); G06V 10/60 (2022.01); H04N 5/272 (2006.01);
U.S. Cl.
CPC ...
G06T 5/50 (2013.01); G06T 7/194 (2017.01); G06V 10/56 (2022.01); G06V 10/60 (2022.01); H04N 5/272 (2013.01); G06T 2207/20221 (2013.01);
Abstract

Apparatus and methods related to applying lighting models to images are provided. An example method includes receiving, via a computing device, an image comprising a subject. The method further includes relighting, via a neural network, a foreground of the image to maintain a consistent lighting of the foreground with a target illumination. The relighting is based on a per-pixel light representation indicative of a surface geometry of the foreground. The light representation includes a specular component, and a diffuse component, of surface reflection. The method additionally includes predicting, via the neural network, an output image comprising the subject in the relit foreground. One or more neural networks can be trained to perform one or more of the aforementioned aspects.


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