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    ATTRIBUTE DECORRELATION TECHNIQUES FOR IMAGE EDITING
    11.
    发明申请
    ATTRIBUTE DECORRELATION TECHNIQUES FOR IMAGE EDITING 有权

    公开(公告)号：US20220122232A1

    公开(公告)日：2022-04-21

    申请号：US17468476

    申请日：2021-09-07

    Applicant: Adobe Inc.

    Inventor： Wei-An Lin ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Jun-Yan Zhu ,  Niloy Mitra ,  Ratheesh Kalarot ,  Richard Zhang ,  Shabnam Ghadar ,  Zhixin Shu

    IPC: G06T5/00 ,  G06T5/20 ,  G06N3/08

    Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

    ANIMATED FACIAL EXPRESSION AND POSE TRANSFER UTILIZING AN END-TO-END MACHINE LEARNING MODEL
    12.
    发明公开
    ANIMATED FACIAL EXPRESSION AND POSE TRANSFER UTILIZING AN END-TO-END MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240331247A1

    公开(公告)日：2024-10-03

    申请号：US18190684

    申请日：2023-03-27

    Applicant: Adobe Inc.

    Inventor： Cameron Smith

    IPC: G06T13/40 ,  G06T7/246 ,  G06V10/94 ,  G06V40/16

    CPC classification number: G06T13/40 ,  G06T7/251 ,  G06V10/95 ,  G06V40/176 ,  G06T2200/24 ,  G06T2207/20084 ,  G06T2207/30201

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

    DIRECT REGRESSION ENCODER ARCHITECTURE AND TRAINING
    20.
    发明申请
    DIRECT REGRESSION ENCODER ARCHITECTURE AND TRAINING 有权

    公开(公告)号：US20220121931A1

    公开(公告)日：2022-04-21

    申请号：US17384371

    申请日：2021-07-23

    Applicant: Adobe Inc.

    Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Cameron Smith ,  Zhixin Shu ,  Baldo Faieta ,  Shabnam Ghadar ,  Jingwan Lu ,  Aliakbar Darabi ,  Jun-Yan Zhu ,  Niloy Mitra ,  Richard Zhang ,  Elya Shechtman

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

    Abstract: Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector to the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation. The encoder is trained using specialized loss functions including a segmentation loss and a mean latent loss.

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