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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.

Patent No.:

US 11978245 B1

PDFFull Text
Date of Patent:
May. 07, 2024

Filed:

Jul. 31, 2018

Method and apparatus for generating image

Applicant:

Baidu Online Network Technology (Beijing) Co., Ltd., Beijing, CN;

Inventors:

Tao He, Beijing, CN;

Gang Zhang, Beijing, CN;

Jingtuo Liu, Beijing, CN;

Assignee:

BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD., Beijing, CN;

Attorney:

Knobbe, Martens, Olson & Bear, LLP

Primary Examiner:

Chan S Park

Assistant Examiner:

Daniel C Chang

Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06N 3/084 (2023.01); G06N 7/00 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06T 7/00 (2017.01); G06T 7/246 (2017.01); G06T 11/00 (2006.01); G06V 10/764 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01); G06T 7/251 (2017.01); G06T 7/97 (2017.01); G06T 11/00 (2013.01); G06V 10/764 (2022.01); G06V 40/165 (2022.01); G06V 40/167 (2022.01); G06V 40/168 (2022.01); G06V 40/172 (2022.01); G06N 7/01 (2023.01); G06N 20/10 (2019.01); G06T 2207/20081 (2013.01); G06T 2207/30201 (2013.01);
Abstract

The present disclosure discloses a method and apparatus for generating an image. A specific embodiment of the method comprises: acquiring at least two frames of facial images extracted from a target video; and inputting the at least two frames of facial images into a pre-trained generative model to generate a single facial image. The generative model updates a model parameter using a loss function in a training process, and the loss function is determined based on a probability of the single facial generative image being a real facial image and a similarity between the single facial generative image and a standard facial image. According to this embodiment, authenticity of the single facial image generated by the generative model may be enhanced, and then a quality of a facial image obtained based on the video is improved.

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