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:
Dec. 10, 2024

Filed:

May. 04, 2022
Applicant:

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

Inventors:

Haitian Zheng, Rochester, NY (US);

Zhe Lin, Fremont, CA (US);

Jingwan Lu, Santa Clara, CA (US);

Scott Cohen, Sunnyvale, CA (US);

Elya Shechtman, Seattle, WA (US);

Connelly Barnes, Seattle, WA (US);

Jianming Zhang, Campbell, CA (US);

Ning Xu, Milpitas, CA (US);

Sohrab Amirghodsi, Seattle, WA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 5/77 (2024.01); G06T 3/4046 (2024.01); G06V 10/40 (2022.01);
U.S. Cl.
CPC ...
G06T 5/77 (2024.01); G06T 3/4046 (2013.01); G06V 10/40 (2022.01); G06T 2207/20084 (2013.01);
Abstract

The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.


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