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    SYNTHETIC BRACKETING FOR EXPOSURE CORRECTION
    103.
    发明申请
    SYNTHETIC BRACKETING FOR EXPOSURE CORRECTION 有权

    公开(公告)号：US20250069191A1

    公开(公告)日：2025-02-27

    申请号：US18452634

    申请日：2023-08-21

    Applicant: NVIDIA Corporation

    Inventor： Iuri Frosio ,  Mayoore Selvarasa Jaiswal ,  Jan Kautz ,  Jianyuan Min

    IPC: G06T5/50 ,  H04N23/743

    Abstract: Systems and methods are disclosed related to synthetic bracketing for exposure correction. A deep learning based method and system produces a set of differently exposed images from a single input image. The images in the set may be combined to produce an output image with improved global and local exposure compared with the input image. An image encoder applies learned parameters to each input image to generate a set of image features including local exposure estimates for each of two or more regions of the input image and a low resolution latent representation of the input image. A decoder receives the local exposure estimates, the latent representation, and target enhancements that are processed to generate synthesized transformations. When applied to the input image, the synthesized transformations produce the set of transformed images. Each transformed image is a version of the input image synthesized to correspond to a respective target enhancement.

    VARIATIONAL INFERENCING BY A DIFFUSION MODEL
    104.
    发明申请
    VARIATIONAL INFERENCING BY A DIFFUSION MODEL 有权

    公开(公告)号：US20250045892A1

    公开(公告)日：2025-02-06

    申请号：US18593742

    申请日：2024-03-01

    Applicant: NVIDIA Corporation

    Inventor： Morteza Mardani ,  Jiaming Song ,  Jan Kautz ,  Arash Vahdat

    IPC: G06T7/00 ,  G06T3/40 ,  G06T5/70 ,  G06T5/73 ,  G06T5/77

    Abstract: Diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. For example, they can be trained in the image domain, for example, to perform specific image restoration tasks, such as inpainting (e.g. completing an incomplete image), deblurring (e.g. removing blurring from an image), and super-resolution (e.g. increasing a resolution of an image), or they can be trained to perform image rendering tasks, including 2D-to-3D image generation tasks. However, current approaches to training diffusion models only allow the models to be optimized for a specific task such that they will not achieve high-quality results when used for other tasks. The present disclosure provides a diffusion model that uses variational inferencing to approximate a distribution of data, which allows the diffusion model to universally solve different tasks without having to be re-trained specifically for each task.

    SYNTHETIC DATA GENERATION USING MORPHABLE MODELS WITH IDENTITY AND EXPRESSION EMBEDDINGS
    105.
    发明申请
    SYNTHETIC DATA GENERATION USING MORPHABLE MODELS WITH IDENTITY AND EXPRESSION EMBEDDINGS 有权

    公开(公告)号：US20240371096A1

    公开(公告)日：2024-11-07

    申请号：US18312102

    申请日：2023-05-04

    Applicant: Nvidia Corporation

    Inventor： Sameh Khamis ,  Koki Nagano ,  Jan Kautz ,  Sanja Fidler

    IPC: G06T17/20 ,  G06T7/70 ,  G06T13/40 ,  G06V10/56 ,  G06V10/82

    Abstract: Approaches presented herein provide systems and methods for disentangling identity from expression input models. One or more machine learning systems may be trained directly from three-dimensional (3D) points to develop unique latent codes for expressions associated with different identities. These codes may then be mapped to different identities to independently model an object, such as a face, to generate a new mesh including an expression for an independent identity. A pipeline may include a set of machine learning systems to determine model parameters and also adjust input expression codes using gradient backpropagation in order train models for incorporation into a content development pipeline.

    LEARNING DENSE CORRESPONDENCES FOR IMAGES
    110.
    发明公开
    LEARNING DENSE CORRESPONDENCES FOR IMAGES 审中-公开

    公开(公告)号：US20230252692A1

    公开(公告)日：2023-08-10

    申请号：US17929182

    申请日：2022-09-01

    Applicant: NVIDIA Corporation

    Inventor： Sifei Liu ,  Jiteng Mu ,  Shalini De Mello ,  Zhiding Yu ,  Jan Kautz

    IPC: G06T11/00 ,  G06T3/00

    CPC classification number: G06T11/001 ,  G06T3/0093

    Abstract: Embodiments of the present disclosure relate to learning dense correspondences for images. Systems and methods are disclosed that disentangle structure and texture (or style) representations of GAN synthesized images by learning a dense pixel-level correspondence map for each image during image synthesis. A canonical coordinate frame is defined and a structure latent code for each generated image is warped to align with the canonical coordinate frame. In sum, the structure associated with the latent code is mapped into a shared coordinate space (canonical coordinate space), thereby establishing correspondences in the shared coordinate space. A correspondence generation system receives the warped coordinate correspondences as an encoded image structure. The encoded image structure and a texture latent code are used to synthesize an image. The shared coordinate space enables propagation of semantic labels from reference images to synthesized images.

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