DIGITAL IMAGE REPOSING BASED ON MULTIPLE INPUT VIEWS

    公开(公告)号:US20250005812A1

    公开(公告)日:2025-01-02

    申请号:US18215484

    申请日:2023-06-28

    Applicant: Adobe Inc.

    Abstract: In implementations of systems for human reposing based on multiple input views, a computing device implements a reposing system to receive input data describing: input digital images; pluralities of keypoints corresponding to the input digital images, the pluralities of keypoints representing poses of a person depicted in the input digital images; and a plurality of keypoints representing a target pose. The reposing system generates selection masks corresponding to the input digital images by processing the input data using a machine learning model. The selection masks represent likelihoods of spatial correspondence between pixels of an output digital image and portions of the input digital images. The reposing system generates the output digital image depicting the person in the target pose for display in a user interface based on the selection masks and the input data.

    DIGITAL IMAGE REPOSING TECHNIQUES
    63.
    发明申请

    公开(公告)号:US20240428564A1

    公开(公告)日:2024-12-26

    申请号:US18213118

    申请日:2023-06-22

    Applicant: Adobe Inc.

    Abstract: In implementations of systems for generating images for human reposing, a computing device implements a reposing system to receive input data describing an input digital image depicting a person in a first pose, a first plurality of keypoints representing the first pose, and a second plurality of keypoints representing a second pose. The reposing system generates a mapping by processing the input data using a first machine learning model. The mapping indicates a plurality of first portions of the person in the second pose that are visible in the input digital image and a plurality of second portions of the person in the second pose that are invisible in the input digital image. The reposing system generates an output digital image depicting the person in the second pose by processing the mapping, the first plurality of keypoints, and the second plurality of keypoints using a second machine learning model.

    Interactive search experience using machine learning

    公开(公告)号:US11971884B2

    公开(公告)日:2024-04-30

    申请号:US17656772

    申请日:2022-03-28

    Applicant: Adobe Inc.

    Abstract: An interactive search session is implemented using an artificial intelligence model. For example, when the artificial intelligence model receives a search query from a user, the model selects an action from a plurality of actions based on the search query. The selected action queries the user for more contextual cues about the search query (e.g., may enquire about use of the search results, may request to refine the search query, or otherwise engage the user in conversation to better understand the intent of the search). The interactive search session may be in the form, for example, of a chat session between the user and the system, and the chat session may be displayed along with the search results (e.g., in a separate section of display). The interactive search session may enable the system to better understand the user's search needs, and accordingly may help provide more focused search results.

    Generating images for virtual try-on and pose transfer

    公开(公告)号:US11861772B2

    公开(公告)日:2024-01-02

    申请号:US17678237

    申请日:2022-02-23

    Applicant: Adobe Inc.

    CPC classification number: G06T11/60 G06N3/045 G06T7/11 G06T7/70

    Abstract: In implementations of systems for generating images for virtual try-on and pose transfer, a computing device implements a generator system to receive input data describing a first digital image that depicts a person in a pose and a second digital image that depicts a garment. Candidate appearance flow maps are computed that warp the garment based on the pose at different pixel-block sizes using a first machine learning model. The generator system generates a warped garment image by combining the candidate appearance flow maps as an aggregate per-pixel displacement map using a convolutional gated recurrent network. A conditional segment mask is predicted that segments portions of a geometry of the person using a second machine learning model. The generator system outputs a digital image that depicts the person in the pose wearing the garment based on the warped garment image and the conditional segmentation mask using a third machine learning model.

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