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1.发明授权公开(公告)号：US11615292B2
公开(公告)日：2023-03-28
申请号：US17899936
申请日：2022-08-31
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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2.发明授权公开(公告)号：US11468294B2
公开(公告)日：2022-10-11
申请号：US16798271
申请日：2020-02-21
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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公开(公告)号：US12165034B2
公开(公告)日：2024-12-10
申请号：US18301887
申请日：2023-04-17
Applicant: Adobe Inc.
Abstract: A generative neural network control system controls a generative neural network by modifying the intermediate latent space in the generative neural network. The generative neural network includes multiple layers each generating a set of activation values. An initial layer (and optionally additional layers) receives an input latent vector, and a final layer outputs an image generated based on the input latent vector. The data that is input to each layer (other than the initial layer) is referred to as data in an intermediate latent space. The data in the intermediate latent space includes activation values (e.g., generated by the previous layer or modified using various techniques) and optionally a latent vector. The generative neural network control system modifies the intermediate latent space to achieve various different effects when generating a new image.
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公开(公告)号：US10650490B2
公开(公告)日：2020-05-12
申请号：US16379496
申请日：2019-04-09
Applicant: Adobe Inc.
Inventor： Xue Bai , Elya Shechtman , Sylvain Philippe Paris
Abstract: Environmental map generation techniques and systems are described. A digital image is scaled to achieve a target aspect ratio using a content aware scaling technique. A canvas is generated that is dimensionally larger than the scaled digital image and the scaled digital image is inserted within the canvas thereby resulting in an unfilled portion of the canvas. An initially filled canvas is then generated by filling the unfilled portion using a content aware fill technique based on the inserted digital image. A plurality of polar coordinate canvases is formed by transforming original coordinates of the canvas into polar coordinates. The unfilled portions of the polar coordinate canvases are filled using a content-aware fill technique that is initialized based on the initially filled canvas. An environmental map of the digital image is generated by combining a plurality of original coordinate canvas portions formed from the polar coordinate canvases.
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公开(公告)号：US20230342592A1
公开(公告)日：2023-10-26
申请号：US18301887
申请日：2023-04-17
Applicant: Adobe Inc.
Abstract: A generative neural network control system controls a generative neural network by modifying the intermediate latent space in the generative neural network. The generative neural network includes multiple layers each generating a set of activation values. An initial layer (and optionally additional layers) receives an input latent vector, and a final layer outputs an image generated based on the input latent vector. The data that is input to each layer (other than the initial layer) is referred to as data in an intermediate latent space. The data in the intermediate latent space includes activation values (e.g., generated by the previous layer or modified using various techniques) and optionally a latent vector. The generative neural network control system modifies the intermediate latent space to achieve various different effects when generating a new image.
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Patent Agency Ranking
公开(公告)号：US11657255B2
公开(公告)日：2023-05-23
申请号：US16798263
申请日：2020-02-21
Applicant: Adobe Inc.
CPC classification number: G06N3/0454 , G06N3/08
Abstract: A generative neural network control system controls a generative neural network by modifying the intermediate latent space in the generative neural network. The generative neural network includes multiple layers each generating a set of activation values. An initial layer (and optionally additional layers) receives an input latent vector, and a final layer outputs an image generated based on the input latent vector. The data that is input to each layer (other than the initial layer) is referred to as data in an intermediate latent space. The data in the intermediate latent space includes activation values (e.g., generated by the previous layer or modified using various techniques) and optionally a latent vector. The generative neural network control system modifies the intermediate latent space to achieve various different effects when generating a new image.
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7.发明申请公开(公告)号：US20210264235A1
公开(公告)日：2021-08-26
申请号：US16798271
申请日：2020-02-21
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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公开(公告)号：US20210264234A1
公开(公告)日：2021-08-26
申请号：US16798263
申请日：2020-02-21
Applicant: Adobe Inc.
Abstract: A generative neural network control system controls a generative neural network by modifying the intermediate latent space in the generative neural network. The generative neural network includes multiple layers each generating a set of activation values. An initial layer (and optionally additional layers) receives an input latent vector, and a final layer outputs an image generated based on the input latent vector. The data that is input to each layer (other than the initial layer) is referred to as data in an intermediate latent space. The data in the intermediate latent space includes activation values (e.g., generated by the previous layer or modified using various techniques) and optionally a latent vector. The generative neural network control system modifies the intermediate latent space to achieve various different effects when generating a new image.
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9.发明申请公开(公告)号：US20220414431A1
公开(公告)日：2022-12-29
申请号：US17899936
申请日：2022-08-31
Applicant: Adobe Inc.
Inventor： Richard Zhang , Sylvain Philippe Paris , Junyan Zhu , Aaron Phillip Hertzmann , Jacob Minyoung Huh
Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.
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公开(公告)号：US10706512B2
公开(公告)日：2020-07-07
申请号：US15452112
申请日：2017-03-07
Applicant: ADOBE INC.
Inventor： Yinglan Ma , Sylvain Philippe Paris , Chih-Yao Hsieh
Abstract: Methods and systems are provided for adjusting the brightness of images. In some implementations, an exposure bracketed set of input images produced by a camera is received. A brightness adjustment is determined for at least one input image from the set of input images. The determined brightness adjustment is applied to the input image. An output image is produced by exposure fusion from the set of input images, using the input image having the determined brightness adjustment. The output image is transmitted where, the transmitting causes display of the output image on a user device.
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