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公开(公告)号：US20250069437A1
公开(公告)日：2025-02-27
申请号：US18948067
申请日：2024-11-14
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：US12026857B2
公开(公告)日：2024-07-02
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: G06T5/77 , G06F18/2134 , G06T7/73 , H04N23/63
CPC classification number: G06T5/77 , 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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公开(公告)号：US20230262189A1
公开(公告)日：2023-08-17
申请号：US18309410
申请日：2023-04-28
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Kun Wan , Xin Lu , Jen-Chan Jeff Chien
CPC classification number: H04N5/2621 , H04N5/265 , G06T5/003 , G06V10/82 , G06V10/40 , G06V10/56 , 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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4.发明公开公开(公告)号：US20230260324A1
公开(公告)日：2023-08-17
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
CPC classification number: G06V40/174 , G06T7/97 , G06V40/23 , G06F18/22 , G06T2207/20084
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：US11670114B2
公开(公告)日：2023-06-06
申请号：US17075207
申请日：2020-10-20
Applicant: Adobe Inc.
Inventor： Jinoh Oh , Xin Lu , Gahye Park , Jen-Chan Jeff Chien , Yumin Jia
CPC classification number: G06V40/174 , G06K9/6215 , G06T7/97 , G06V40/23 , G06T2207/20084
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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Patent Agency Ranking
公开(公告)号：US20220245824A1
公开(公告)日：2022-08-04
申请号：US17660361
申请日：2022-04-22
Applicant: Adobe Inc.
Inventor： Zichuan Liu , Wentian Zhao , Shitong Wang , He Qin , Yumin Jia , Yeojin Kim , Xin Lu , Jen-Chan Chien
IPC: G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.
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公开(公告)号：US11334971B2
公开(公告)日：2022-05-17
申请号：US16928340
申请日：2020-07-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
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公开(公告)号：US20210350504A1
公开(公告)日：2021-11-11
申请号：US17379622
申请日：2021-07-19
Applicant: ADOBE INC.
Inventor： Xiaohui Shen , Zhe Lin , Xin Lu , Sarah Aye Kong , I-Ming Pao , Yingcong Chen
Abstract: Methods and systems are provided for generating enhanced image. A neural network system is trained where the training includes training a first neural network that generates enhanced images conditioned on content of an image undergoing enhancement and training a second neural network that designates realism of the enhanced images generated by the first neural network. The neural network system is trained by determine loss and accordingly adjusting the appropriate neural network(s). The trained neural network system is used to generate an enhanced aesthetic image from a selected image where the output enhanced aesthetic image has increased aesthetics when compared to the selected image.
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9.发明授权公开(公告)号：US10915798B1
公开(公告)日：2021-02-09
申请号：US15980636
申请日：2018-05-15
Applicant: ADOBE INC.
Inventor： Jianming Zhang , Rameswar Panda , Haoxiang Li , Joon-Young Lee , Xin Lu
Abstract: Disclosed herein are embodiments of systems, methods, and products for a webly supervised training of a convolutional neural network (CNN) to predict emotion in images. A computer may query one or more image repositories using search keywords generated based on the tertiary emotion classes of Parrott's emotion wheel. The computer may filter images received in response to the query to generate a weakly labeled training dataset labels associated with the images that are noisy or wrong may be cleaned prior to training of the CNN. The computer may iteratively train the CNN leveraging the hierarchy of emotion classes by increasing the complexity of the labels (tags) for each iteration. Such curriculum guided training may generate a trained CNN that is more accurate than the conventionally trained neural networks.
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公开(公告)号：US20200327675A1
公开(公告)日：2020-10-15
申请号：US16384039
申请日：2019-04-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Wei Xiong , Connelly Barnes , Jimei Yang , Xin Lu
Abstract: In some embodiments, an image manipulation application receives an incomplete image that includes a hole area lacking image content. The image manipulation application applies a contour detection operation to the incomplete image to detect an incomplete contour of a foreground object in the incomplete image. The hole area prevents the contour detection operation from detecting a completed contour of the foreground object. The image manipulation application further applies a contour completion model to the incomplete contour and the incomplete image to generate the completed contour for the foreground object. Based on the completed contour and the incomplete image, the image manipulation application generates image content for the hole area to generate a completed image.
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