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    PROCEDURAL MEDIA GENERATION
    1.
    发明公开
    PROCEDURAL MEDIA GENERATION 审中-公开

    公开(公告)号：US20230360310A1

    公开(公告)日：2023-11-09

    申请号：US17662287

    申请日：2022-05-06

    Applicant: ADOBE INC.

    Inventor： Paul Augusto Guerrero ,  Milos Hasan ,  Kalyan K. Sunkavalli ,  Radomir Mech ,  Tamy Boubekeur ,  Niloy Jyoti Mitra

    IPC: G06T15/04 ,  G06T17/00 ,  G06V10/44 ,  G06V10/426 ,  G06V10/774 ,  G06V10/776

    CPC classification number: G06T15/04 ,  G06T17/00 ,  G06V10/44 ,  G06V10/426 ,  G06V10/7747 ,  G06V10/776

    Abstract: Aspects of a system and method for procedural media generation include generating a sequence of operator types using a node generation network; generating a sequence of operator parameters for each operator type of the sequence of operator types using a parameter generation network; generating a sequence of directed edges based on the sequence of operator types using an edge generation network; combining the sequence of operator types, the sequence of operator parameters, and the sequence of directed edges to obtain a procedural media generator, wherein each node of the procedural media generator comprises an operator that includes an operator type from the sequence of operator types, a corresponding sequence of operator parameters, and an input connection or an output connection from the sequence of directed edges that connects the node to another node of the procedural media generator; and generating a media asset using the procedural media generator.

    Image lighting transfer via multi-dimensional histogram matching
    3.
    发明授权
    Image lighting transfer via multi-dimensional histogram matching 有权

    公开(公告)号：US10521892B2

    公开(公告)日：2019-12-31

    申请号：US15253655

    申请日：2016-08-31

    Applicant: ADOBE INC.

    Inventor： Kalyan K. Sunkavalli ,  Sunil Hadap ,  Elya Shechtman ,  Zhixin Shu

    IPC: G06T5/40 ,  G06K9/00 ,  G06T5/50 ,  G06T11/00

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at relighting a target image based on a lighting effect from a reference image. In one embodiment, a target image and a reference image are received, the reference image includes a lighting effect desired to be applied to the target image. A lighting transfer is performed using color data and geometrical data associated with the reference image and color data and geometrical data associated with the target image. The lighting transfer causes generation of a relit image that corresponds with the target image having a lighting effect of the reference image. The relit image is provided for display to a user via one or more output devices. Other embodiments may be described and/or claimed.

    Procedural media generation
    5.
    发明授权
    Procedural media generation 有权

    公开(公告)号：US11875446B2

    公开(公告)日：2024-01-16

    申请号：US17662287

    申请日：2022-05-06

    Applicant: ADOBE INC.

    Inventor： Paul Augusto Guerrero ,  Milos Hasan ,  Kalyan K. Sunkavalli ,  Radomir Mech ,  Tamy Boubekeur ,  Niloy Jyoti Mitra

    IPC: G06T15/04 ,  G06T17/00 ,  G06V10/776 ,  G06V10/426 ,  G06V10/774 ,  G06V10/44

    CPC classification number: G06T15/04 ,  G06T17/00 ,  G06V10/426 ,  G06V10/44 ,  G06V10/776 ,  G06V10/7747

    Abstract: Aspects of a system and method for procedural media generation include generating a sequence of operator types using a node generation network; generating a sequence of operator parameters for each operator type of the sequence of operator types using a parameter generation network; generating a sequence of directed edges based on the sequence of operator types using an edge generation network; combining the sequence of operator types, the sequence of operator parameters, and the sequence of directed edges to obtain a procedural media generator, wherein each node of the procedural media generator comprises an operator that includes an operator type from the sequence of operator types, a corresponding sequence of operator parameters, and an input connection or an output connection from the sequence of directed edges that connects the node to another node of the procedural media generator; and generating a media asset using the procedural media generator.

    NEURAL NETWORK-BASED CAMERA CALIBRATION
    6.
    发明申请
    NEURAL NETWORK-BASED CAMERA CALIBRATION 审中-公开

    公开(公告)号：US20190164312A1

    公开(公告)日：2019-05-30

    申请号：US15826331

    申请日：2017-11-29

    Applicant: ADOBE INC.

    Inventor： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC: G06T7/80 ,  G06T7/00 ,  G06N3/08

    CPC classification number: G06T7/80 ,  G06N3/0454 ,  G06N3/08 ,  G06T7/97 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.

    Editing neural radiance fields with neural basis decomposition
    7.
    发明授权
    Editing neural radiance fields with neural basis decomposition 有权

    公开(公告)号：US12211138B2

    公开(公告)日：2025-01-28

    申请号：US18065456

    申请日：2022-12-13

    Applicant: Adobe Inc.

    Inventor： Zhengfei Kuang ,  Fujun Luan ,  Sai Bi ,  Zhixin Shu ,  Kalyan K. Sunkavalli

    IPC: G06T15/08 ,  G06T15/06 ,  G06T15/50 ,  G06T15/80 ,  G06T19/20

    Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for generating editable synthesized views of scenes by inputting image rays into neural networks using neural basis decomposition. In embodiments, a set of input images of a scene depicting at least one object are collected and used to generate a plurality of rays of the scene. The rays each correspond to three-dimensional coordinates and viewing angles taken from the images. A volume density of the scene is determined by inputting the three-dimensional coordinates from the neural radiance fields into a first neural network to generate a 3D geometric representation of the object. An appearance decomposition is produced by inputting the three-dimensional coordinates and the viewing angles of the rays into a second neural network.

    Learning to estimate high-dynamic range outdoor lighting parameters
    8.
    发明授权
    Learning to estimate high-dynamic range outdoor lighting parameters 有权

    公开(公告)号：US10936909B2

    公开(公告)日：2021-03-02

    申请号：US16188130

    申请日：2018-11-12

    Applicant: ADOBE INC.

    Inventor： Kalyan K. Sunkavalli ,  Sunil Hadap ,  Jonathan Eisenmann ,  Jinsong Zhang ,  Emiliano Gambaretto

    IPC: G06K9/62 ,  G06K9/46 ,  G06T5/00

    Abstract: Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate lighting parameters for input images where the input images are synthetic and real low-dynamic range images. Such a neural network system can be trained using differences between a simple scene rendered using the estimated lighting parameters and the same simple scene rendered using known ground-truth lighting parameters. Such a neural network system can also be trained such that the synthetic and real low-dynamic range images are mapped in roughly the same distribution. Such a trained neural network system can be used to input a low-dynamic range image determine high-dynamic range lighting parameters.

    NEURAL NETWORK-BASED CAMERA CALIBRATION
    9.
    发明申请
    NEURAL NETWORK-BASED CAMERA CALIBRATION 审中-公开

    公开(公告)号：US20200074682A1

    公开(公告)日：2020-03-05

    申请号：US16675641

    申请日：2019-11-06

    Applicant: ADOBE INC.

    Inventor： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC: G06T7/80 ,  G06N3/08 ,  G06T7/00 ,  G06N3/04

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.

    Neural network-based camera calibration
    10.
    发明授权
    Neural network-based camera calibration 有权

    公开(公告)号：US10515460B2

    公开(公告)日：2019-12-24

    申请号：US15826331

    申请日：2017-11-29

    Applicant: ADOBE INC.

    Inventor： Kalyan K. Sunkavalli ,  Yannick Hold-Geoffroy ,  Sunil Hadap ,  Matthew David Fisher ,  Jonathan Eisenmann ,  Emiliano Gambaretto

    IPC: G06T7/80 ,  G06N3/08 ,  G06T7/00

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.

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