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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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2.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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公开(公告)号：US12056857B2
公开(公告)日：2024-08-06
申请号：US17520249
申请日：2021-11-05
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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公开(公告)号：US20240338869A1
公开(公告)日：2024-10-10
申请号：US18474536
申请日：2023-09-26
Applicant: ADOBE INC.
Inventor： Yuqian Zhou , Krishna Kumar Singh , Zhifei Zhang , Difan Liu , Zhe Lin , Jianming Zhang , Qing Liu , Jingwan Lu , Elya Shechtman , Sohrab Amirghodsi , Connelly Stuart Barnes
IPC: G06T11/60
CPC classification number: G06T11/60
Abstract: An image processing system obtains an input image (e.g., a user provided image, etc.) and a mask indicating an edit region of the image. A user selects an image editing mode for an image generation network from a plurality of image editing modes. The image generation network generates an output image using the input image, the mask, and the image editing mode.
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公开(公告)号：US20240169500A1
公开(公告)日：2024-05-23
申请号：US18058027
申请日：2022-11-22
Applicant: ADOBE INC.
Inventor： Haitian Zheng , Zhe Lin , Jianming Zhang , Connelly Stuart Barnes , Elya Shechtman , Jingwan Lu , Qing Liu , Sohrab Amirghodsi , Yuqian Zhou , Scott Cohen
IPC: G06T5/00
CPC classification number: G06T5/005 , G06T5/003 , G06T2207/20081 , G06T2207/20104
Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image comprising a first region that includes content and a second region to be inpainted. Noise is then added to the image to obtain a noisy image, and a plurality of intermediate output images are generated based on the noisy image using a diffusion model trained using a perceptual loss. The intermediate output images predict a final output image based on a corresponding intermediate noise level of the diffusion model. The diffusion model then generates the final output image based on the intermediate output image. The final output image includes inpainted content in the second region that is consistent with the content in the first region.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20240046429A1
公开(公告)日：2024-02-08
申请号：US17815418
申请日：2022-07-27
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Lingzhi Zhang , Zhe Lin , Elya Shechtman , Yuqian Zhou , Connelly Barnes
CPC classification number: G06T5/005 , G06T7/11 , G06T2207/20084
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.
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7.发明公开公开(公告)号：US20240037717A1
公开(公告)日：2024-02-01
申请号：US17815409
申请日：2022-07-27
Applicant: Adobe Inc.
Inventor： Sohrab Amirghodsi , Lingzhi Zhang , Zhe Lin , Elya Shechtman , Yuqian Zhou , Connelly Barnes
CPC classification number: G06T5/005 , G06T7/194 , G06T2207/20081 , G06T2207/20084
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating neural network based perceptual artifact segmentations in synthetic digital image content. The disclosed system utilizing neural networks to detect perceptual artifacts in digital images in connection with generating or modifying digital images. The disclosed system determines a digital image including one or more synthetically modified portions. The disclosed system utilizes an artifact segmentation machine-learning model to detect perceptual artifacts in the synthetically modified portion(s). The artifact segmentation machine-learning model is trained to detect perceptual artifacts based on labeled artifact regions of synthetic training digital images. Additionally, the disclosed system utilizes the artifact segmentation machine-learning model in an iterative inpainting process. The disclosed system utilizes one or more digital image inpainting models to inpaint in a digital image. The disclosed system utilizes the artifact segmentation machine-learning model detect perceptual artifacts in the inpainted portions for additional inpainting iterations.
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公开(公告)号：US11869173B2
公开(公告)日：2024-01-09
申请号：US18089218
申请日：2022-12-27
Applicant: Adobe Inc.
Inventor： Yuqian Zhou , Elya Shechtman , Connelly Stuart Barnes , Sohrab Amirghodsi
CPC classification number: G06T5/005 , G06N3/08 , G06T3/0093 , G06T5/50 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221 , G06T2207/20224
Abstract: Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.
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公开(公告)号：US20250061626A1
公开(公告)日：2025-02-20
申请号：US18674518
申请日：2024-05-24
Applicant: Adobe Inc.
Inventor： Xiaoyang Liu , Zhe Lin , Yuqian Zhou , Sohrab Amirghodsi , Sarah Jane Stuckey , Sakshi Gupta , Guotong Feng , Elya Schechtman , Connelly Stuart Barnes , Betty Leong
IPC: G06T11/60 , G06T5/77 , G06T11/20 , G06V10/764
Abstract: Techniques for performing a digital operation on a digital image are described along with methods and systems employing such techniques. According to the techniques, an input (e.g., an input stroke) is received by, for example, a processing system. Based upon the input, an area of the digital image upon which a digital operation (e.g., for removal of a distractor within the area) is to be performed is determined. In an implementation, one or more metrics of an input stroke are analyzed, typically in real time, to at least partially determine the area upon which the digital operation is to be performed. In an additional or alternative implementation, the input includes a first point, a second point and a connector, and the area is at least partially determined by a location of the first point relative to a location of the second point and/or by locations of the first point and/or second point relative to one or more edges of the digital image.
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公开(公告)号：US20240362791A1
公开(公告)日：2024-10-31
申请号：US18307353
申请日：2023-04-26
Applicant: Adobe Inc.
Inventor： Yuqian Zhou , Chuong Huynh , Connelly Barnes , Elya Shechtman , Sohrab Amirghodsi , Zhe Lin
IPC: G06T7/12 , G06F3/04883 , G06V10/44 , G06V10/74 , G06V10/80
CPC classification number: G06T7/12 , G06F3/04883 , G06V10/44 , G06V10/761 , G06V10/806 , G06T2207/20101 , G06V2201/07
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning to generate a mask for an object portrayed in a digital image. For example, in some embodiments, the disclosed systems utilize a neural network to generate an image feature representation from the digital image. The disclosed systems can receive a selection input identifying one or more pixels corresponding to the object. In addition, in some implementations, the disclosed systems generate a modified feature representation by integrating the selection input into the image feature representation. Moreover, in one or more embodiments, the disclosed systems utilize an additional neural network to generate a plurality of masking proposals for the object from the modified feature representation. Furthermore, in some embodiments, the disclosed systems utilize a further neural network to generate the mask for the object from the modified feature representation and/or the masking proposals.
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