Machine-Learning Models Trained to Modify Image Illumination Without Ground-Truth Images

    公开(公告)号:US20230290108A1

    公开(公告)日:2023-09-14

    申请号:US18116686

    申请日:2023-03-02

    CPC classification number: G06V10/60 G06V10/761 G06V10/82

    Abstract: In one embodiment, a method of training a machine-learning model for modifying a facial illumination in an image includes accessing an initial image including a human face having an initial illumination and determining one or more illumination priors for the initial image. The method includes providing the initial image and the one or more illumination priors to the machine-learning model; receiving, from the machine-learning model, a set of correction operators identifying a modified illumination for the human face; creating, based at least on the set of correction operators and the initial image, a modified image having the modified illumination; creating, based on the modified image, a reconstructed initial image including the human face having a reconstructed illumination; and adjusting one or more parameters of the machine-learning model by minimizing a loss function based on a difference between the initial and the reconstructed initial images in their respective illumination.

    System and method for hybrid eye tracker

    公开(公告)号:US10795435B2

    公开(公告)日:2020-10-06

    申请号:US16216849

    申请日:2018-12-11

    Abstract: A system and method enable an electronic device to perform gaze tracking operations. The electronic device includes one or more cameras and at least one processor. The one or more cameras include a Dynamic Vision Sensor (DVS) camera. The one or more cameras are configured to capture features of an eye of a wearer of the electronic device The processor is configured to receive, from the one or more cameras, an image or pixel stream associated with the eye, determine an initial pose of the eye based on the image or pixel stream, receive DVS pixel data from the DVS camera, track one or more changes to the initial pose of the eye based on the DVS pixel data, and based on the one or more changes to the initial pose of the eye, determine a gaze of the wearer.

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