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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：US20210082124A1
公开(公告)日：2021-03-18
申请号：US17103119
申请日：2020-11-24
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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公开(公告)号：US20200342576A1
公开(公告)日：2020-10-29
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：US10783622B2
公开(公告)日：2020-09-22
申请号：US15962735
申请日：2018-04-25
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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公开(公告)号：US20200184610A1
公开(公告)日：2020-06-11
申请号：US16791939
申请日：2020-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion using deep learning 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 framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling content in place of the holey digital image's holes.
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Patent Agency Ranking
公开(公告)号：US10515275B2
公开(公告)日：2019-12-24
申请号：US15816704
申请日：2017-11-17
Applicant: Adobe Inc.
Inventor： Xin Lu
Abstract: The present disclosure includes systems, methods, and computer readable media, that identify one or more scene categories that correspond to digital images. In one or more embodiments, disclosed systems analyze a digital image to determine, for each of a plurality of object tags, a probability that the object tag associates with the digital image. The systems further determine, for each of the plurality of object tags, a similarity score for each of a plurality of scene categories (e.g., a similarity between each object tag and each scene category). Using the object tag probabilities and the similarity scores, the disclosed systems determine a probability, for each scene category, that the digital image pertains to the scene category. Based on the determined probabilities, the disclosed systems are able to identify an appropriate scene category for the digital image.
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公开(公告)号：US12282987B2
公开(公告)日：2025-04-22
申请号：US18053646
申请日：2022-11-08
Applicant: Adobe Inc.
Inventor： Zichuan Liu , Xin Lu , Ke Wang
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.
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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：US11436775B2
公开(公告)日：2022-09-06
申请号：US16806344
申请日：2020-03-02
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
IPC: G06T11/60 , G06K9/62 , G06T5/00 , G06T5/30 , G06N3/04 , G06N3/08 , G06V10/44 , G06V10/75 , G06V30/194
Abstract: Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.
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公开(公告)号：US11321847B2
公开(公告)日：2022-05-03
申请号：US17103119
申请日：2020-11-24
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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