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. 20, 2024

Filed:

Sep. 07, 2021
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Ratheesh Kalarot, San Jose, CA (US);

Kevin Wampler, Seattle, WA (US);

Jingwan Lu, Santa Clara, CA (US);

Jakub Fiser, Milton Keynes, GB;

Elya Shechtman, Seattle, WA (US);

Aliakbar Darabi, Seattle, WA (US);

Alexandru Vasile Costin, Monte Sereno, CA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 3/04845 (2022.01); G06T 11/60 (2006.01); G06T 3/40 (2006.01); G06T 3/00 (2006.01); G06F 3/04847 (2022.01); G06N 20/20 (2019.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); G06T 11/00 (2006.01); G06F 18/40 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 3/04845 (2013.01); G06F 3/04847 (2013.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06T 3/0006 (2013.01); G06T 3/0093 (2013.01); G06T 3/40 (2013.01); G06T 3/4038 (2013.01); G06T 3/4046 (2013.01); G06T 5/005 (2013.01); G06T 5/20 (2013.01); G06T 11/001 (2013.01); G06T 11/60 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2210/22 (2013.01);
Abstract

Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.


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