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    REDUCING COLLISION-BASED DEFECTS IN MOTION-STYLIZATION OF VIDEO CONTENT DEPICTING CLOSELY SPACED FEATURES
    1.
    发明申请
    REDUCING COLLISION-BASED DEFECTS IN MOTION-STYLIZATION OF VIDEO CONTENT DEPICTING CLOSELY SPACED FEATURES 审中-公开

    公开(公告)号：US20190259214A1

    公开(公告)日：2019-08-22

    申请号：US15899503

    申请日：2018-02-20

    Applicant: Adobe Inc.

    Inventor： Rinat Abdrashitov ,  Jose Ignacio Echevarria Vallespi ,  Jingwan Lu ,  Elya Shectman ,  Duygu Ceylan Aksit ,  David Simons

    IPC: G06T19/20 ,  G06T13/40 ,  G06T7/246

    Abstract: Embodiments involve reducing collision-based defects in motion-stylizations. For example, a device obtains facial landmark data from video data. The facial landmark data includes a first trajectory traveled by a first point tracking one or more facial features, and a second trajectory traveled by a second point tracking one or more facial features. The device applies a motion-stylization to the facial landmark data that causes a first change to one or more of the first trajectory and the second trajectory. The device also identifies a new collision between the first and second points that is introduced by the first change. The device applies a modified stylization to the facial landmark data that causes a second change to one or more of the first trajectory and the second trajectory. If the new collision is removed by the second change, the device outputs the facial landmark data with the modified stylization applied.

    Using machine-learning models to determine movements of a mouth corresponding to live speech
    3.
    发明授权
    Using machine-learning models to determine movements of a mouth corresponding to live speech 有权

    公开(公告)号：US11211060B2

    公开(公告)日：2021-12-28

    申请号：US16887418

    申请日：2020-05-29

    Applicant: Adobe Inc.

    Inventor： Wilmot Li ,  Jovan Popovic ,  Deepali Aneja ,  David Simons

    IPC: G10L15/197 ,  G06N3/04 ,  G06N3/08 ,  G10L15/02 ,  G10L15/06 ,  G10L21/0316 ,  G10L25/21 ,  G10L25/24

    Abstract: Disclosed systems and methods predict visemes from an audio sequence. In an example, a viseme-generation application accesses a first audio sequence that is mapped to a sequence of visemes. The first audio sequence has a first length and represents phonemes. The application adjusts a second length of a second audio sequence such that the second length equals the first length and represents the phonemes. The application adjusts the sequence of visemes to the second audio sequence such that phonemes in the second audio sequence correspond to the phonemes in the first audio sequence. The application trains a machine-learning model with the second audio sequence and the sequence of visemes. The machine-learning model predicts an additional sequence of visemes based on an additional sequence of audio.

    USING MACHINE-LEARNING MODELS TO DETERMINE MOVEMENTS OF A MOUTH CORRESPONDING TO LIVE SPEECH
    5.
    发明申请
    USING MACHINE-LEARNING MODELS TO DETERMINE MOVEMENTS OF A MOUTH CORRESPONDING TO LIVE SPEECH 审中-公开

    公开(公告)号：US20190392823A1

    公开(公告)日：2019-12-26

    申请号：US16016418

    申请日：2018-06-22

    Applicant: Adobe Inc.

    Inventor： Wilmot Li ,  Jovan Popovic ,  Deepali Aneja ,  David Simons

    IPC: G10L15/197 ,  G06N3/08 ,  G06N3/04 ,  G10L15/06 ,  G10L15/02 ,  G10L25/24 ,  G10L25/21 ,  G10L21/0316

    Abstract: Disclosed systems and methods predict visemes from an audio sequence. A viseme-generation application accesses a first set of training data that includes a first audio sequence representing a sentence spoken by a first speaker and a sequence of visemes. Each viseme is mapped to a respective audio sample of the first audio sequence. The viseme-generation application creates a second set of training data adjusting a second audio sequence spoken by a second speaker speaking the sentence such that the second and first sequences have the same length and at least one phoneme occurs at the same time stamp in the first sequence and in the second sequence. The viseme-generation application maps the sequence of visemes to the second audio sequence and trains a viseme prediction model to predict a sequence of visemes from an audio sequence.

    Using machine-learning models to determine movements of a mouth corresponding to live speech
    7.
    发明授权
    Using machine-learning models to determine movements of a mouth corresponding to live speech 有权

    公开(公告)号：US10699705B2

    公开(公告)日：2020-06-30

    申请号：US16016418

    申请日：2018-06-22

    Applicant: Adobe Inc.

    Inventor： Wilmot Li ,  Jovan Popovic ,  Deepali Aneja ,  David Simons

    IPC: G10L15/197 ,  G06N3/04 ,  G06N3/08 ,  G10L15/02 ,  G10L15/06 ,  G10L21/0316 ,  G10L25/21 ,  G10L25/24

    Abstract: Disclosed systems and methods predict visemes from an audio sequence. A viseme-generation application accesses a first set of training data that includes a first audio sequence representing a sentence spoken by a first speaker and a sequence of visemes. Each viseme is mapped to a respective audio sample of the first audio sequence. The viseme-generation application creates a second set of training data adjusting a second audio sequence spoken by a second speaker speaking the sentence such that the second and first sequences have the same length and at least one phoneme occurs at the same time stamp in the first sequence and in the second sequence. The viseme-generation application maps the sequence of visemes to the second audio sequence and trains a viseme prediction model to predict a sequence of visemes from an audio sequence.

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