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    HIGH-PRECISION SEMANTIC IMAGE EDITING USING NEURAL NETWORKS FOR SYNTHETIC DATA GENERATION SYSTEMS AND APPLICATIONS
    5.
    发明申请
    HIGH-PRECISION SEMANTIC IMAGE EDITING USING NEURAL NETWORKS FOR SYNTHETIC DATA GENERATION SYSTEMS AND APPLICATIONS 有权

    公开(公告)号：US20220383570A1

    公开(公告)日：2022-12-01

    申请号：US17827394

    申请日：2022-05-27

    Applicant: NVIDIA Corporation

    Inventor： Huan Ling ,  Karsten Kreis ,  Daiqing Li ,  Seung Wook Kim ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06T11/60 ,  G06T7/10 ,  G06V10/776 ,  G06V10/774

    Abstract: In various examples, high-precision semantic image editing for machine learning systems and applications are described. For example, a generative adversarial network (GAN) may be used to jointly model images and their semantic segmentations based on a same underlying latent code. Image editing may be achieved by using segmentation mask modifications (e.g., provided by a user, or otherwise) to optimize the latent code to be consistent with the updated segmentation, thus effectively changing the original, e.g., RGB image. To improve efficiency of the system, and to not require optimizations for each edit on each image, editing vectors may be learned in latent space that realize the edits, and that can be directly applied on other images with or without additional optimizations. As a result, a GAN in combination with the optimization approaches described herein may simultaneously allow for high precision editing in real-time with straightforward compositionality of multiple edits.

    High-precision semantic image editing using neural networks for synthetic data generation systems and applications
    9.
    发明授权
    High-precision semantic image editing using neural networks for synthetic data generation systems and applications 有权

    公开(公告)号：US12288277B2

    公开(公告)日：2025-04-29

    申请号：US17827394

    申请日：2022-05-27

    Applicant: NVIDIA Corporation

    Inventor： Huan Ling ,  Karsten Kreis ,  Daiqing Li ,  Seung Wook Kim ,  Antonio Torralba Barriuso ,  Sanja Fidler

    IPC: G06T7/10 ,  G06T11/60 ,  G06V10/774 ,  G06V10/776

    Abstract: In various examples, high-precision semantic image editing for machine learning systems and applications are described. For example, a generative adversarial network (GAN) may be used to jointly model images and their semantic segmentations based on a same underlying latent code. Image editing may be achieved by using segmentation mask modifications (e.g., provided by a user, or otherwise) to optimize the latent code to be consistent with the updated segmentation, thus effectively changing the original, e.g., RGB image. To improve efficiency of the system, and to not require optimizations for each edit on each image, editing vectors may be learned in latent space that realize the edits, and that can be directly applied on other images with or without additional optimizations. As a result, a GAN in combination with the optimization approaches described herein may simultaneously allow for high precision editing in real-time with straightforward compositionality of multiple edits.

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