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公开(公告)号:US20240394889A1
公开(公告)日:2024-11-28
申请号:US18200908
申请日:2023-05-23
Applicant: Adobe Inc.
Inventor: He Zhang , Salil Tambe
IPC: G06T7/12 , G06F3/04845 , G06T5/50 , G06T7/13 , G06V10/56
Abstract: An image editing system accesses an input image displayed via a user interface and generates an instance-aware trimap for the input image by applying an instance-aware image segmentation model to input data including the input image and a segmented image defining a segment of the input image including a first set of pixel values. The trimap defines a modified segment using a second set of pixels different from the first set of pixels. Applying the model includes detecting boundaries of an object depicted in the input image. The second set of pixels is located within the boundaries of the object. Responsive to receiving a request via the user interface, the system generates a modified image by performing an editing operation on the input image including editing a portion of the second set of pixels of the modified segment of the trimap. The system transmits, for display, the modified image.
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公开(公告)号:US12165292B2
公开(公告)日:2024-12-10
申请号:US18317547
申请日:2023-05-15
Applicant: Adobe Inc.
Inventor: He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
IPC: G06T5/75 , G06N3/08 , G06T3/4046 , G06T7/194
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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3.
公开(公告)号:US20230281763A1
公开(公告)日:2023-09-07
申请号:US18317547
申请日:2023-05-15
Applicant: Adobe Inc.
Inventor: He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
CPC classification number: G06T5/004 , G06T7/194 , G06N3/08 , G06T3/4046 , G06T2207/20132 , G06T2207/20084 , G06T2207/20081
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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公开(公告)号:US11651477B2
公开(公告)日:2023-05-16
申请号:US16988055
申请日:2020-08-07
Applicant: Adobe Inc.
Inventor: He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
CPC classification number: G06T5/004 , G06N3/08 , G06T3/4046 , G06T7/194 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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公开(公告)号:US20230129341A1
公开(公告)日:2023-04-27
申请号:US17584233
申请日:2022-01-25
Applicant: Adobe Inc.
Inventor: Betty Leong , Hyunghwan Byun , Alan L Erickson , Chih-Yao Hsieh , Sarah Kong , Seyed Morteza Safdarnejad , Salil Tambe , Yilin Wang , Zijun Wei , Zhengyun Zhang
IPC: G06T7/10 , G06F3/04842 , G06T3/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate preliminary object masks for objects in an image, surface the preliminary object masks as object mask previews, and on-demand converts preliminary object masks into refined object masks. Indeed, in one or more implementations, an object mask preview and on-demand generation system automatically detects objects in an image. For the detected objects, the object mask preview and on-demand generation system generates preliminary object masks for the detected objects of a first lower resolution. The object mask preview and on-demand generation system surfaces a given preliminary object mask in response to detecting a first input. The object mask preview and on-demand generation system also generates a refined object mask of a second higher resolution in response to detecting a second input.
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