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公开(公告)号：US20190197670A1
公开(公告)日：2019-06-27
申请号：US15855583
申请日：2017-12-27
Applicant: Facebook, Inc.
Inventor： Cristian Canton Ferrer , Brian Dolhansky , Thomas Ward Meyer , Jonathan Morton
CPC classification number: G06T5/005 , G06K9/00268 , G06K9/6256 , G06T2207/30201
Abstract: In one embodiment, a computing system may access a training image and a reference image of a person and an incomplete image. A generate may generate an in-painted image based on the incomplete image, and a discriminator may be used to determine whether each of the in-painted image, the training image, and the reference image is likely generated by the generator. The system may compute losses based on the determinations and update the discriminator accordingly. Using the updated discriminator, the system may determine whether a second in-painted image generated by the generator is likely generated by the generator. The system may compute a loss based on the determination and update the generator accordingly. Once training is complete, the generator may be used to generate a modified version of a given image, such as making the eyes of a person appear open even if they were closed in the input image.
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Patent Agency Ranking
公开(公告)号：US10388002B2
公开(公告)日：2019-08-20
申请号：US15855583
申请日：2017-12-27
Applicant: Facebook, Inc.
Inventor： Cristian Canton Ferrer , Brian Dolhansky , Thomas Ward Meyer , Jonathan Morton
Abstract: In one embodiment, a computing system may access a training image and a reference image of a person and an incomplete image. A generate may generate an in-painted image based on the incomplete image, and a discriminator may be used to determine whether each of the in-painted image, the training image, and the reference image is likely generated by the generator. The system may compute losses based on the determinations and update the discriminator accordingly. Using the updated discriminator, the system may determine whether a second in-painted image generated by the generator is likely generated by the generator. The system may compute a loss based on the determination and update the generator accordingly. Once training is complete, the generator may be used to generate a modified version of a given image, such as making the eyes of a person appear open even if they were closed in the input image.
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公开(公告)号：US10915663B1
公开(公告)日：2021-02-09
申请号：US16261112
申请日：2019-01-29
Applicant: Facebook, Inc.
Inventor： Cristian Canton Ferrer , Brian Dolhansky , Phong Dinh , Bryan Wu , Zhen Ling Tsai , Eric Erkon Hsin
Abstract: Systems, methods, and non-transitory computer-readable media can be configured to train a featurizer based at least in part on a set of training data. The featurizer can be applied to at least one input to generate at least one tensor. The at least one tensor obfuscates or excludes at least one feature in the at least one input.
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公开(公告)号：US20210141926A1
公开(公告)日：2021-05-13
申请号：US16790437
申请日：2020-02-13
Applicant: Facebook, Inc.
Inventor： Cristian Canton Ferrer , Brian Dolhansky , Hao Guo , Eric Erkon Hsin , Phong Dinh
Abstract: In one embodiment, a method includes accessing a first machine-learning model trained to generate a feature representation of an input data, a second machine-learning model trained to generate a desired result based on the feature representation, and a third machine-learning model trained to generate an undesired result based on the feature representation, and training a fourth machine-learning model by generating a secured feature representation by processing a first output of the first machine-learning model using the fourth machine-learning model, generating a second output and a third output by processing the secured feature representation using, respectively, the second and third machine-learning models, and updating the fourth machine-learning model according to an optimization function configured to optimize a correctness of the second output and an incorrectness of the third output.
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公开(公告)号：US10810725B1
公开(公告)日：2020-10-20
申请号：US16213667
申请日：2018-12-07
Applicant: Facebook, Inc.
Inventor： Brian Dolhansky , Cristian Canton Ferrer , Eric Erkon Hsin
Abstract: A content analyzer determines whether various types of modification have been made to images. The content analyzer computes JPEG ghosts from the images that are concatenated with the image channels to generate a feature vector. The feature vector is provided as input to a neural network that determines whether the types of modification have been made to the image. The neural network may include a constrained convolution layer and several unconstrained convolution layers. An image fake model may also be applied to determine whether the image was generated using a computer model or algorithm.
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公开(公告)号：US10789723B1
公开(公告)日：2020-09-29
申请号：US15956177
申请日：2018-04-18
Applicant: Facebook, Inc.
Inventor： Johannes Peter Kopf , Brian Dolhansky , Suhib Fakhri Mahmod Alsisan
Abstract: In one embodiment, a method includes generating depth map for a reference image and generating a three-dimensional (3D) model for a plurality of objects in the reference image based on the depth map. The method additionally includes determining, out of the objects in the 3D model, a background object having a boundary adjacent to a foreground object. The method also includes determining that at least a portion of a surface of the background object is hidden by the foreground object and extending, in the 3D model, the surface of the background object to include the portion hidden by the foreground object. The method further includes in-paint pixels of the extended surface of the background object with pixels that approximate the portion of the surface of the background object hidden by the foreground object.
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