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:
Sep. 16, 2025

Filed:

Dec. 20, 2021
Applicant:

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

Inventors:

Aaron Phillip Hertzmann, San Francisco, CA (US);

Manuel Rodriguez Ladron De Guevara, Pittsburgh, PA (US);

Matthew Fisher, San Francisco, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 11/00 (2006.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06V 10/40 (2022.01);
U.S. Cl.
CPC ...
G06T 11/00 (2013.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06V 10/40 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a multi-stroke neural network for modifying a digital image via a plurality of generated stroke parameters in a single pass of the neural network. Specifically, the disclosed system utilizes an encoder neural network to generate an encoding of a digital image. The disclosed system then utilizes a decoder neural network that generates a sequence of stroke parameters for digital drawing strokes from the encoding in a single pass of the encoder neural network and decoder neural network. Additionally, the disclosed system utilizes a renderer neural network to render the digital drawing strokes on a digital canvas according to the sequence of stroke parameters. In additional embodiments, the disclosed system utilizes a balance of loss functions to learn parameters of the multi-stroke neural network to generate stroke parameters according to various rendering styles.


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