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:
Jun. 21, 2022

Filed:

Feb. 19, 2020
Applicant:

Apple Inc., Cupertino, CA (US);

Inventors:

Francesco Rossi, Sunnyvale, CA (US);

Marco Zuliani, San Jose, CA (US);

Bartlomiej W. Rymkowski, San Jose, CA (US);

Albert Antony, Hayes, GB;

Brian P. Keene, Cupertino, CA (US);

Xiaojin Shi, Cupertino, CA (US);

Assignee:

Apple Inc., Cupertino, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 11/60 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4046 (2013.01); G06N 3/0454 (2013.01); G06T 3/4092 (2013.01); G06T 11/60 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Artistic styles extracted from source images may be applied to target images to generate stylized images and/or video sequences. The extracted artistic styles may be stored as a plurality of layers in one or more neural networks, which neural networks may be further optimized, e.g., via the fusion of various elements of the networks' architectures. The artistic style may be applied to the target images and/or video sequences using various optimization methods, such as the use of a first version of the neural network by a first processing device at a first resolution to generate one or more sets of parameters (e.g., scaling and/or biasing parameters), which parameters may then be mapped for use by a second version of the neural network by a second processing device at a second resolution. Analogous multi-processing device and/or multi-network solutions may also be applied to other complex image processing tasks for increased efficiency.


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