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    Curve Generation for Sketch Vectorization
    1.
    发明申请
    Curve Generation for Sketch Vectorization 有权

    公开(公告)号：US20230137233A1

    公开(公告)日：2023-05-04

    申请号：US17519357

    申请日：2021-11-04

    Applicant: Adobe Inc.

    Inventor： Ashwani Chandil ,  Vineet Batra ,  Matthew David Fisher ,  Deepali Aneja ,  Ankit Phogat

    IPC: G06T11/20 ,  G06K9/46 ,  G06T7/12 ,  G06T7/194 ,  G06T7/181 ,  G06T3/40

    Abstract: Generating a vector representation of a hand-drawn sketch is described. To do so, the sketch is segmented into different superpixel regions. Superpixels are grown by distributing superpixel seeds throughout an image of the sketch and assigning unassigned pixels to a neighboring superpixel based on pixel value differences. The border between each pair of adjacent superpixels is then classified as either an active or an inactive boundary, with active boundaries indicating that the border corresponds to a salient sketch stroke. Vector paths are generated by traversing edges between pixel vertices along the active boundaries. To minimize vector paths included in the vector representation, vector paths are greedily generated first for longer curves along active boundaries until each edge is assigned to a vector path. Regions encompassed by vector paths corresponding to a foreground superpixel are filled to produce a high-fidelity vector representation of the sketch.

    Using machine-learning models to determine movements of a mouth corresponding to live speech
    2.
    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
    3.
    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.

    Stroke-Guided Sketch Vectorization
    9.
    发明公开
    Stroke-Guided Sketch Vectorization 审中-公开

    公开(公告)号：US20230162413A1

    公开(公告)日：2023-05-25

    申请号：US17530760

    申请日：2021-11-19

    Applicant: Adobe Inc.

    Inventor： Vineet Batra ,  Matthew David Fisher ,  Deepali Aneja ,  Ashwani Chandil ,  Ankit Phogat

    IPC: G06T11/20 ,  G06T7/30 ,  G06T5/00 ,  G06V10/75

    CPC classification number: G06T11/203 ,  G06T7/30 ,  G06T5/002 ,  G06V10/751 ,  G06T2207/20081

    Abstract: A stroke-guided vectorization system is described that generates, from an input sketch and guide image depicting an approximate vector representation of the sketch, an aligned guide image depicting an improved vector representation of the sketch. To do so, the stroke-guided vectorization system determines black levels representing a vector stroke in the input sketch and white levels representing a background in the input sketch. The stroke-guided vectorization system determines a black threshold value and a white threshold value for discrete portions of the aligned guide image using subsets of the black levels and subsets of the white levels determined using the input sketch. Each discrete portion of the aligned guide image is then mapped to a vector stroke or a background based on the black threshold value and the white threshold value of the portion.

    Using machine-learning models to determine movements of a mouth corresponding to live speech
    10.
    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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