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1.发明授权公开(公告)号：US12190484B2
公开(公告)日：2025-01-07
申请号：US17202019
申请日：2021-03-15
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Lingzhi Zhang , Zhe Lin , Connelly Barnes , Elya Shechtman
IPC: G06T5/77 , G06N3/08 , G06T3/4053 , G06T7/11 , G06T7/50
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating modified digital images utilizing a guided inpainting approach that implements a patch match model informed by a deep visual guide. In particular, the disclosed systems can utilize a visual guide algorithm to automatically generate guidance maps to help identify replacement pixels for inpainting regions of digital images utilizing a patch match model. For example, the disclosed systems can generate guidance maps in the form of structure maps, depth maps, or segmentation maps that respectively indicate the structure, depth, or segmentation of different portions of digital images. Additionally, the disclosed systems can implement a patch match model to identify replacement pixels for filling regions of digital images according to the structure, depth, and/or segmentation of the digital images.
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公开(公告)号：US12165295B2
公开(公告)日：2024-12-10
申请号：US17661985
申请日：2022-05-04
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
IPC: G06T5/77 , G06T3/4046 , G06V10/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.
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公开(公告)号：US12159380B2
公开(公告)日：2024-12-03
申请号：US17664991
申请日：2022-05-25
Applicant: Adobe Inc.
Inventor： Connelly Barnes , Elya Shechtman , Sohrab Amirghodsi , Zhe Lin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that implement an inpainting framework having computer-implemented machine learning models to generate high-resolution inpainting results. For instance, in one or more embodiments, the disclosed systems generate an inpainted digital image utilizing a deep inpainting neural network from a digital image having a replacement region. The disclosed systems further generate, utilizing a visual guide algorithm, at least one deep visual guide from the inpainted digital image. Using a patch match model and the at least one deep visual guide, the disclosed systems generate a plurality of modified digital images from the digital image by replacing the region of pixels of the digital image with replacement pixels. Additionally, the disclosed systems select, utilizing an inpainting curation model, a modified digital image from the plurality of modified digital images to provide to a client device.
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公开(公告)号：US12086965B2
公开(公告)日：2024-09-10
申请号：US17520361
申请日：2021-11-05
Applicant: Adobe Inc.
Inventor： Yunhan Zhao , Connelly Barnes , Yuqian Zhou , Sohrab Amirghodsi , Elya Shechtman
CPC classification number: G06T5/77 , G06T3/18 , G06T3/4046 , G06T5/50 , G06T7/30 , G06T7/50 , G06T7/90 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately restoring missing pixels within a hole region of a target image utilizing multi-image inpainting techniques based on incorporating geometric depth information. For example, in various implementations, the disclosed systems utilize a depth prediction of a source image as well as camera relative pose parameters. Additionally, in some implementations, the disclosed systems jointly optimize the depth rescaling and camera pose parameters before generating the reprojected image to further increase the accuracy of the reprojected image. Further, in various implementations, the disclosed systems utilize the reprojected image in connection with a content-aware fill model to generate a refined composite image that includes the target image having a hole, where the hole is filled in based on the reprojected image of the source image.
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5.发明公开公开(公告)号：US20230259587A1
公开(公告)日：2023-08-17
申请号：US17650967
申请日：2022-02-14
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Jingwan Lu , Scott Cohen , Jianming Zhang , Ning Xu , Elya Shechtman , Connelly Barnes , Sohrab Amirghodsi
CPC classification number: G06K9/6257 , G06T5/005 , G06T7/11 , G06N3/08 , G06T2207/20084 , G06T2207/20081
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a generative inpainting neural network to accurately generate inpainted digital images via object-aware training and/or masked regularization. For example, the disclosed systems utilize an object-aware training technique to learn parameters for a generative inpainting neural network based on masking individual object instances depicted within sample digital images of a training dataset. In some embodiments, the disclosed systems also (or alternatively) utilize a masked regularization technique as part of training to prevent overfitting by penalizing a discriminator neural network utilizing a regularization term that is based on an object mask. In certain cases, the disclosed systems further generate an inpainted digital image utilizing a trained generative inpainting model with parameters learned via the object-aware training and/or the masked regularization
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Patent Agency Ranking
公开(公告)号：US20230145498A1
公开(公告)日：2023-05-11
申请号：US17520361
申请日：2021-11-05
Applicant: Adobe Inc.
Inventor： Yunhan Zhao , Connelly Barnes , Yuqian Zhou , Sohrab Amirghodsi , Elya Shechtman
CPC classification number: G06T5/005 , G06T3/0093 , G06T3/4046 , G06T5/50 , G06T7/30 , G06T7/50 , G06T7/90 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately restoring missing pixels within a hole region of a target image utilizing multi-image inpainting techniques based on incorporating geometric depth information. For example, in various implementations, the disclosed systems utilize a depth prediction of a source image as well as camera relative pose parameters. Additionally, in some implementations, the disclosed systems jointly optimize the depth rescaling and camera pose parameters before generating the reprojected image to further increase the accuracy of the reprojected image. Further, in various implementations, the disclosed systems utilize the reprojected image in connection with a content-aware fill model to generate a refined composite image that includes the target image having a hole, where the hole is filled in based on the reprojected image of the source image.
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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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公开(公告)号：US20210158495A1
公开(公告)日：2021-05-27
申请号：US16692843
申请日：2019-11-22
Applicant: Adobe Inc.
Inventor： Connelly Barnes , Utkarsh Singhal , Elya Shechtman , Michael Gharbi
Abstract: A method for manipulating a target image includes generating a query of the target image and keys and values of a first reference image. The method also includes generating matching costs by comparing the query of the target image with each key of the reference image and generating a set of weights from the matching costs. Further, the method includes generating a set of weighted values by applying each weight of the set of weights to a corresponding value of the values of the reference image and generating a weighted patch by adding each weighted value of the set of weighted values together. Additionally, the method includes generating a combined weighted patch by combining the weighted patch with additional weighted patches associated with additional queries of the target image and generating a manipulated image by applying the combined weighted patch to an image processing algorithm.
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公开(公告)号：US12271804B2
公开(公告)日：2025-04-08
申请号：US17870496
申请日：2022-07-21
Applicant: Adobe Inc.
Inventor： Mang Tik Chiu , Connelly Barnes , Zijun Wei , Zhe Lin , Yuqian Zhou , Xuaner Zhang , Sohrab Amirghodsi , Florian Kainz , Elya Shechtman
Abstract: Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.
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10.发明公开公开(公告)号：US20240331114A1
公开(公告)日：2024-10-03
申请号：US18743497
申请日：2024-06-14
Applicant: Adobe Inc.
Inventor： Yuqian Zhou , Connelly Barnes , Sohrab Amirghodsi , Elya Shechtman
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately generating inpainted digital images utilizing a guided inpainting model guided by both plane panoptic segmentation and plane grouping. For example, the disclosed systems utilize a guided inpainting model to fill holes of missing pixels of a digital image as informed or guided by an appearance guide and a geometric guide. Specifically, the disclosed systems generate an appearance guide utilizing plane panoptic segmentation and generate a geometric guide by grouping plane panoptic segments. In some embodiments, the disclosed systems generate a modified digital image by implementing an inpainting model guided by both the appearance guide (e.g., a plane panoptic segmentation map) and the geometric guide (e.g., a plane grouping map).
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