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    SEMANTIC IMAGE SYNTHESIS
    1.
    发明申请
    SEMANTIC IMAGE SYNTHESIS 有权

    公开(公告)号：US20250086849A1

    公开(公告)日：2025-03-13

    申请号：US18463333

    申请日：2023-09-08

    Applicant: ADOBE INC.

    Inventor： Yu Zeng ,  Zhe Lin ,  Jianming Zhang ,  Qing Liu ,  Jason Wen Yong Kuen ,  John Philip Collomosse

    IPC: G06T11/00 ,  G06F40/295 ,  G06F40/30 ,  G06V10/774 ,  G06V10/776 ,  G06V20/70

    Abstract: Embodiments of the present disclosure include obtaining a text prompt describing an element, layout information indicating a target region for the element, and a precision level corresponding to the element. Some embodiments generate a text feature pyramid based on the text prompt, the layout information, and the precision level, wherein the text feature pyramid comprises a plurality of text feature maps at a plurality of scales, respectively. Then, an image is generated based on the text feature pyramid. In some cases, the image includes an object corresponding to the element of the text prompt at the target region. Additionally, a shape of the object corresponds to a shape of the target region based on the precision level.

    Iterative image inpainting with confidence feedback
    2.
    发明授权
    Iterative image inpainting with confidence feedback 有权

    公开(公告)号：US11605156B2

    公开(公告)日：2023-03-14

    申请号：US17812639

    申请日：2022-07-14

    Applicant: ADOBE INC.

    Inventor： Zhe Lin ,  Yu Zeng ,  Jimei Yang ,  Jianming Zhang ,  Elya Shechtman

    IPC: G06T5/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.

    Guided up-sampling for image inpainting
    3.
    发明授权
    Guided up-sampling for image inpainting 有权

    公开(公告)号：US11948281B2

    公开(公告)日：2024-04-02

    申请号：US16864388

    申请日：2020-05-01

    Applicant: ADOBE INC.

    Inventor： Zhe Lin ,  Yu Zeng ,  Jimei Yang ,  Jianming Zhang ,  Elya Shechtman

    IPC: G06T5/00 ,  G06T3/40 ,  G06T3/4046 ,  G06T3/4076

    CPC classification number: G06T5/005 ,  G06T3/4046 ,  G06T3/4076

    Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of high-resolution images using guided upsampling during image inpainting. For instance, an image inpainting system can apply guided upsampling to an inpainted image result to enable generation of a high-resolution inpainting result from a lower-resolution image that has undergone inpainting. To allow for guided upsampling during image inpainting, one or more neural networks can be used. For instance, a low-resolution result neural network (e.g., comprised of an encoder and a decoder) and a high-resolution input neural network (e.g., comprised of an encoder and a decoder). The image inpainting system can use such networks to generate a high-resolution inpainting image result that fills the hole, region, and/or portion of the image.

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