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1.发明公开公开(公告)号：US20240127411A1
公开(公告)日：2024-04-18
申请号：US17937706
申请日：2022-10-03
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Elya Shechtman , Jianming Zhang , Jingwan Lu , Ning Xu , Qing Liu , Scott Cohen , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06T7/11 , G06T2200/24 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.
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公开(公告)号：US20240127410A1
公开(公告)日：2024-04-18
申请号：US17937695
申请日：2022-10-03
Applicant: Adobe Inc.
Inventor： Zhe Lin , Haitian Zheng , Elya Shechtman , Jianming Zhang , Jingwan Lu , Ning Xu , Qing Liu , Scott Cohen , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06T7/11 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.
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公开(公告)号：US11880977B2
公开(公告)日：2024-01-23
申请号：US17313158
申请日：2021-05-06
Applicant: Adobe Inc.
Inventor： Brian Lynn Price , Scott Cohen , Marco Forte , Ning Xu
IPC: G06T7/11 , G06T7/136 , G06T7/194 , G06T7/90 , G06T3/40 , G06N3/088 , G06N3/045 , G06N3/02 , G06N20/00
CPC classification number: G06T7/11 , G06N3/045 , G06N3/088 , G06T3/40 , G06T7/136 , G06T7/194 , G06T7/90 , G06N3/02 , G06N20/00 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/20096 , G06T2207/20104
Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.
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4.发明公开公开(公告)号：US20230368339A1
公开(公告)日：2023-11-16
申请号：US17663317
申请日：2022-05-13
Applicant: Adobe Inc.
Inventor： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06T7/11 , G06N3/04 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing class-specific cascaded modulation inpainting neural network. For example, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network that includes cascaded modulation decoder layers to generate replacement pixels portraying a particular target object class. To illustrate, in response to user selection of a replacement region and target object class, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network corresponding to the target object class to generate an inpainted digital image that portrays an instance of the target object class within the replacement region. Moreover, in one or more embodiments the disclosed systems train class-specific cascaded modulation inpainting neural networks corresponding to a variety of target object classes, such as a sky object class, a water object class, a ground object class, or a human object class.
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公开(公告)号：US20230206462A1
公开(公告)日：2023-06-29
申请号：US18175481
申请日：2023-02-27
Applicant: Adobe Inc.
Inventor： Qihang Yu , Jianming Zhang , He Zhang , Yilin Wang , Zhe Lin , Ning Xu
CPC classification number: G06T7/194 , G06T7/136 , G06T7/11 , G06T3/4053 , G06T3/4046 , G06T5/009 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.
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Patent Agency Ranking
公开(公告)号：US20230126177A1
公开(公告)日：2023-04-27
申请号：US17452529
申请日：2021-10-27
Applicant: ADOBE INC.
Inventor： Ning Xu , Zhe Lin , Franck Dernoncourt
Abstract: The present disclosure relates to systems and methods for automatically processing images based on a user request. In some examples, a request is divided into a retouching command (e.g., a global edit) and an inpainting command (e.g., a local edit). A retouching mask and an inpainting mask are generated to indicate areas where the edits will be applied. A photo-request attention and a multi-modal modulation process are applied to features representing the image, and a modified image that incorporates the user's request is generated using the modified features.
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7.发明申请公开(公告)号：US20220262009A1
公开(公告)日：2022-08-18
申请号：US17177595
申请日：2021-02-17
Applicant: Adobe Inc.
Inventor： Qihang Yu , Jianming Zhang , He Zhang , Yilin Wang , Zhe Lin , Ning Xu
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.
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公开(公告)号：US11314982B2
公开(公告)日：2022-04-26
申请号：US16216739
申请日：2018-12-11
Applicant: Adobe Inc.
Inventor： Brian Price , Scott Cohen , Ning Xu
Abstract: Systems and methods are disclosed for selecting target objects within digital images. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user indicators to select targeted objects in digital images. Specifically, the disclosed systems and methods can transform user indicators into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.
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9.发明申请公开(公告)号：US20220122357A1
公开(公告)日：2022-04-21
申请号：US17563901
申请日：2021-12-28
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Seokhwan Kim , Ning Xu , Hailin Jin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating a response to a question received from a user during display or playback of a video segment by utilizing a query-response-neural network. The disclosed systems can extract a query vector from a question corresponding to the video segment using the query-response-neural network. The disclosed systems further generate context vectors representing both visual cues and transcript cues corresponding to the video segment using context encoders or other layers from the query-response-neural network. By utilizing additional layers from the query-response-neural network, the disclosed systems generate (i) a query-context vector based on the query vector and the context vectors, and (ii) candidate-response vectors representing candidate responses to the question from a domain-knowledge base or other source. To respond to a user's question, the disclosed systems further select a response from the candidate responses based on a comparison of the query-context vector and the candidate-response vectors.
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公开(公告)号：US11113578B1
公开(公告)日：2021-09-07
申请号：US16847270
申请日：2020-04-13
Applicant: Adobe Inc.
Inventor： Jonathan Brandt , Radomir Mech , Ning Xu , Byungmoon Kim , Biao Jia
Abstract: A non-photorealistic image rendering system and related techniques are described herein that train and implement machine learning models to reproduce digital images in accordance with various painting styles and constraints. The image rendering system can include a machine learning system that utilizes actor-critic based reinforcement learning techniques to train painting agents (e.g., models that include one or more neural networks) how to transform images into various artistic styles with minimal loss between the original images and the transformed images. The image rendering system can generate constrained painting agents, which correspond to painting agents that are further trained to reproduce images in accordance with one or more constraints. The constraints may include limitations of the color, width, size, and/or position of brushstrokes within reproduced images. These constrained painting agents may provide users with robust, flexible, and customizable non-photorealistic painting systems.
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