TECHNIQUES FOR USING MACHINE LEARNING MODELS TO GENERATE SCENES BASED UPON IMAGE TILES

    公开(公告)号:US20250054211A1

    公开(公告)日:2025-02-13

    申请号:US18649891

    申请日:2024-04-29

    Applicant: AUTODESK, INC.

    Abstract: In various embodiments, examples of the disclosure provide systems and methods for generating a scene image. A plurality of input image tiles based upon at least one user input are obtained. Spatial positioning of each input image tile included in the plurality of input image tiles relative to at least one other input image tile included in the plurality of input image tiles is detected, and then a scene composition of the scene determined based upon the spatial positioning of each input image tile included in the plurality of image tiles. A scene prompt associated with the scene is obtained, and a machine learning model blends the plurality of image tiles based upon the scene prompt to generate the scene.

    PROVIDING AWARENESS OF PRIVACY-RELATED ACTIVITIES IN VIRTUAL AND REAL-WORLD ENVIRONMENTS

    公开(公告)号:US20240331281A1

    公开(公告)日:2024-10-03

    申请号:US18458924

    申请日:2023-08-30

    Applicant: AUTODESK, INC.

    CPC classification number: G06T17/00 G02B27/0172

    Abstract: One embodiment of the present invention sets forth a technique for providing awareness of privacy-related activities. The technique includes determining a privacy level associated with a user of an extended reality environment. The technique also includes presenting, using an internal display of a headset, one or more internal indicators identifying a location of a bystander located in a real-world environment, wherein a level of detail of each internal indicator is based on the privacy level associated with the user. The technique further includes presenting, using an external display, one or more external indicators that include a monitoring indicator representing being captured by the headset and presented to the user via the headset, and further include a user activity indicator representing one or more activities of the user, wherein a level of detail of the user activity indicator is based on the privacy level associated with the user.

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