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公开(公告)号：US11625813B2
公开(公告)日：2023-04-11
申请号：US17085491
申请日：2020-10-30
Applicant: Adobe Inc.
Inventor： Sheng-Wei Huang , Wentian Zhao , Kun Wan , Zichuan Liu , Xin Lu , Jen-Chan Jeff Chien
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a 3D to 2D generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.
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公开(公告)号：US10795647B2
公开(公告)日：2020-10-06
申请号：US15785298
申请日：2017-10-16
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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公开(公告)号：US20190244327A1
公开(公告)日：2019-08-08
申请号：US16384593
申请日：2019-04-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
CPC classification number: G06T3/40 , G06K9/4671 , G06T3/0012 , G06T11/60 , G06T2210/22
Abstract: Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, indicative of visual characteristics established for visually-pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which a candidate image cropping is scored may be indicative of its composition quality, an extent to which it preserves content appearing in the scene, and a simplicity of its boundary. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that croppings in a group are different by less than a threshold amount and croppings in different groups are different by at least the threshold amount. Based on the clustering, croppings may then be chosen, e.g., to present them to a user for selection.
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公开(公告)号：US12154379B2
公开(公告)日：2024-11-26
申请号：US18306439
申请日：2023-04-25
Applicant: Adobe Inc.
Inventor： Jinoh Oh , Xin Lu , Gahye Park , Jen-Chan Jeff Chien , Yumin Jia
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.
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公开(公告)号：US20230274400A1
公开(公告)日：2023-08-31
申请号：US18298146
申请日：2023-04-10
Applicant: Adobe Inc.
Inventor： Sheng-Wei Huang , Wentian Zhao , Kun Wan , Zichuan Liu , Xin Lu , Jen-Chan Jeff Chien
IPC: G06T5/00 , G06T7/73 , G06F18/2134 , H04N23/63
CPC classification number: G06T5/005 , G06F18/2134 , G06T7/73 , H04N23/631 , G06T2207/10016 , G06T2207/20081
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.
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Patent Agency Ranking
公开(公告)号：US20220138913A1
公开(公告)日：2022-05-05
申请号：US17085491
申请日：2020-10-30
Applicant: Adobe Inc.
Inventor： Sheng-Wei Huang , Wentian Zhao , Kun Wan , Zichuan Liu , Xin Lu , Jen-Chan Jeff Chien
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a 3D to 2D generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.
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公开(公告)号：US11853723B2
公开(公告)日：2023-12-26
申请号：US17490748
申请日：2021-09-30
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
CPC classification number: G06F8/33 , G06F21/6245 , G06N5/02 , G06N20/00
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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公开(公告)号：US11677897B2
公开(公告)日：2023-06-13
申请号：US17073697
申请日：2020-10-19
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Kun Wan , Xin Lu , Jen-Chan Jeff Chien
CPC classification number: H04N5/2621 , G06T5/003 , G06V10/40 , G06V10/56 , G06V10/82 , H04N5/265 , H04N23/631 , H04N23/632
Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating artistic images by applying an artistic-effect to one or more frames of a video stream or digital images. In one or more embodiments, the disclosed system captures a video stream utilizing a camera of a computing device. The disclosed system deploys a distilled artistic-effect neural network on the computing device to generate an artistic version of the captured video stream at a first resolution in real time. The disclosed system can provide the artistic video stream for display via the computing device. Based on an indication of a capture event, the disclosed system utilizes the distilled artistic-effect neural network to generate an artistic image at a higher resolution than the artistic video stream. Furthermore, the disclosed system tunes and utilizes an artistic-effect patch generative adversarial neural network to modify parameters for the distilled artistic-effect neural network.
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公开(公告)号：US11544743B2
公开(公告)日：2023-01-03
申请号：US15785329
申请日：2017-10-16
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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公开(公告)号：US20220019412A1
公开(公告)日：2022-01-20
申请号：US17490748
申请日：2021-09-30
Applicant: Adobe Inc.
Inventor： Thomas William Randall Jacobs , Peter Raymond Fransen , Kevin Gary Smith , Kent Andrew Edmonds , Jen-Chan Jeff Chien , Gavin Stuart Peter Miller
Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.
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