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:

Sep. 30, 2020
Applicant:

Facebook Technologies, Llc, Menlo Park, CA (US);

Inventors:

Lei Xiao, Redmond, WA (US);

Salah Eddine Nouri, Bellevue, WA (US);

Douglas Robert Lanman, Bellevue, WA (US);

Anton S Kaplanyan, Redmond, WA (US);

Alexander Jobe Fix, Seattle, WA (US);

Matthew Steven Chapman, Redmond, WA (US);

Assignee:

Facebook Technologies, LLC., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 11/00 (2006.01); G06T 7/285 (2017.01); H04N 13/366 (2018.01); G06T 7/246 (2017.01); G06T 3/00 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4053 (2013.01); G06T 3/0093 (2013.01); G06T 3/4046 (2013.01); G06T 7/246 (2017.01); G06T 7/285 (2017.01); G06T 11/00 (2013.01); H04N 13/366 (2018.05); G06T 2207/10021 (2013.01); G06T 2210/36 (2013.01);
Abstract

In one embodiment, a method includes receiving a first frame associated with a first time and one or more second frames of a video having a resolution lower than a target resolution, wherein each second frame is associated with a second time prior to the first time, generating a first feature map for the first frame and one or more second feature maps for the one or more second frames, up-sampling the first feature map and the one or more second feature maps to the target resolution, warping each of the up-sampled second feature maps according to a motion estimation between the associated second time and the first time, and generating a reconstructed frame having the target resolution corresponding to the first frame by using a machine-learning model to process the up-sampled first feature map and the one or more up-sampled and warped second feature maps.


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