Contextual action bar
    131.
    发明授权

    公开(公告)号:US12147643B2

    公开(公告)日:2024-11-19

    申请号:US17344339

    申请日:2021-06-10

    Applicant: Snap Inc.

    Abstract: Method of generating a contextual action bar starts with processor causing an application icon associated with an application to be displayed by a display screen of a client device. Processor receives selection of application icon from a user and determines a context of client device. Context can comprise identification of application, type associated with application, or type of interface including application icon. Processor generates action bar based on the context of the client device, causes a first portion of display screen to display an application interface associated with the application, and causes a second portion to display the action bar that is associated with a messaging system. Other embodiments are also disclosed herein.

    Graph-based prediction for contact suggestion in a location sharing system

    公开(公告)号:US12141215B2

    公开(公告)日:2024-11-12

    申请号:US18450110

    申请日:2023-08-15

    Applicant: Snap Inc.

    Abstract: Methods, systems, and devices for generating contact suggestions for a user of a social network. A first score is computed for each one of the plurality of users, the first score being computed using an edge-weighted ranking algorithm based on the user graph. A second score is computed, using a machine learning model, for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of said user, with the second score of each user being representative of a probability of a first user sending a connection request to said user. A ranked contact suggestion list of one or more users of the plurality of users is generated, the one or more users being ranked based on their respective second score.

    TECHNIQUES FOR USING 3-D AVATARS IN AUGMENTED REALITY MESSAGING

    公开(公告)号:US20240372822A1

    公开(公告)日:2024-11-07

    申请号:US18141851

    申请日:2023-05-01

    Applicant: Snap Inc.

    Abstract: Described herein is a messaging application that executes on a wearable augmented reality device. The messaging application facilitates the anchoring or pinning of a 3-D avatar representing another end-user. An end-user wearing the AR device facilitates messaging with the other end-user via interactions with the 3-D avatar representing the other end-user. As such, the AR device processes various sensor inputs to detect when the end-user wearing the AR device is “targeting” the 3-D avatar, and enables an audio recording device to record an audible message for communicating to the other end-user.

    LIGHT ESTIMATION USING NEURAL NETWORKS

    公开(公告)号:US20240371085A1

    公开(公告)日:2024-11-07

    申请号:US18770353

    申请日:2024-07-11

    Applicant: Snap Inc.

    Abstract: A messaging system performs image processing to estimate lighting properties with neural networks for images provided by users of the messaging system. A method of estimating light properties includes receiving an input image with first lighting properties and processing the input image using a convolutional neural network to generate an estimate of the first lighting properties. The method may further include modifying the input image with an augmentation to generate a modified input image, where the augmentation has second lighting properties, and changing the second lighting properties of the augmentation in the modified input image to the estimate of the first lighting properties.

    Augmented reality experience power usage prediction

    公开(公告)号:US12136160B2

    公开(公告)日:2024-11-05

    申请号:US17661001

    申请日:2022-04-27

    Applicant: Snap Inc.

    Abstract: Methods and systems are disclosed for performing operations for estimating power usage of an AR experience. The operations include: accessing resource utilization data associated with execution of an augmented reality (AR) experience; applying a machine learning technique to the resource utilization data to estimate power consumption of the AR experience, the machine learning technique being trained to establish a relationship between a plurality of training resource utilization data associated with training AR experiences and corresponding ground-truth power consumption of the training AR experiences; and adjusting one or more operations of the AR experience to reduce power consumption based on the estimated power consumption of the AR experience.

    Body pose estimation
    138.
    发明授权

    公开(公告)号:US12136158B2

    公开(公告)日:2024-11-05

    申请号:US18060449

    申请日:2022-11-30

    Applicant: Snap Inc.

    Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for detecting a pose of a user. The program and method include receiving a monocular image that includes a depiction of a body of a user; detecting a plurality of skeletal joints of the body depicted in the monocular image; and determining a pose represented by the body depicted in the monocular image based on the detected plurality of skeletal joints of the body. A pose of an avatar is modified to match the pose represented by the body depicted in the monocular image by adjusting a set of skeletal joints of a rig of an avatar based on the detected plurality of skeletal joints of the body; and the avatar having the modified pose that matches the pose represented by the body depicted in the monocular image is generated for display.

    Compact neural networks using condensed filters

    公开(公告)号:US12136026B2

    公开(公告)日:2024-11-05

    申请号:US18222649

    申请日:2023-07-17

    Applicant: Snap Inc.

    Abstract: A compact neural network system can generate multiple individual filters from a compound filter. Each convolutional layer of a convolutional neural network can include a compound filters used to generate individual filters for that layer. The individual filters overlap in the compound filter and can be extracted using a sampling operation. The extracted individual filters can share weights with nearby filters thereby reducing the overall size of the convolutional neural network.

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