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    Projecting images to a generative model based on gradient-free latent vector determination
    67.
    发明授权
    Projecting images to a generative model based on gradient-free latent vector determination 有权

    公开(公告)号：US11615292B2

    公开(公告)日：2023-03-28

    申请号：US17899936

    申请日：2022-08-31

    Applicant: Adobe Inc.

    Inventor： Richard Zhang ,  Sylvain Philippe Paris ,  Junyan Zhu ,  Aaron Phillip Hertzmann ,  Jacob Minyoung Huh

    IPC: G06N3/04 ,  G06T11/60 ,  G06F17/18

    Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.

    Automatic makeup transfer using semi-supervised learning
    68.
    发明授权
    Automatic makeup transfer using semi-supervised learning 有权

    公开(公告)号：US11508148B2

    公开(公告)日：2022-11-22

    申请号：US16822878

    申请日：2020-03-18

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Zhifei Zhang ,  Richard Zhang ,  Jingwan Lu

    IPC: G09G5/00 ,  G06V20/20 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/56 ,  G06V40/50 ,  G06V40/16

    Abstract: The present disclosure relates to systems, computer-implemented methods, and non-transitory computer readable medium for automatically transferring makeup from a reference face image to a target face image using a neural network trained using semi-supervised learning. For example, the disclosed systems can receive, at a neural network, a target face image and a reference face image, where the target face image is selected by a user via a graphical user interface (GUI) and the reference face image has makeup. The systems transfer, by the neural network, the makeup from the reference face image to the target face image, where the neural network is trained to transfer the makeup from the reference face image to the target face image using semi-supervised learning. The systems output for display the makeup on the target face image.

    FEW-SHOT DIGITAL IMAGE GENERATION USING GAN-TO-GAN TRANSLATION
    69.
    发明申请
    FEW-SHOT DIGITAL IMAGE GENERATION USING GAN-TO-GAN TRANSLATION 有权

    公开(公告)号：US20220254071A1

    公开(公告)日：2022-08-11

    申请号：US17163284

    申请日：2021-01-29

    Applicant: Adobe Inc.

    Inventor： Utkarsh Ojha ,  Yijun Li ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman ,  Alexei A. Efros

    IPC: G06T11/00 ,  G06K9/62 ,  G06N3/02

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently modifying a generative adversarial neural network using few-shot adaptation to generate digital images corresponding to a target domain while maintaining diversity of a source domain and realism of the target domain. In particular, the disclosed systems utilize a generative adversarial neural network with parameters learned from a large source domain. The disclosed systems preserve relative similarities and differences between digital images in the source domain using a cross-domain distance consistency loss. In addition, the disclosed systems utilize an anchor-based strategy to encourage different levels or measures of realism over digital images generated from latent vectors in different regions of a latent space.

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