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    Automatic image warping for warped image generation
    12.
    发明授权
    Automatic image warping for warped image generation 有权

    公开(公告)号：US11328385B2

    公开(公告)日：2022-05-10

    申请号：US16848741

    申请日：2020-04-14

    Applicant: Adobe Inc.

    Inventor： Julia Gong ,  Yannick Hold-Geoffroy ,  Jingwan Lu

    IPC: G06T3/00 ,  G06T7/00 ,  G06T11/60

    Abstract: Techniques and systems are provided for configuring neural networks to perform warping of an object represented in an image to create a caricature of the object. For instance, in response to obtaining an image of an object, a warped image generator generates a warping field using the image as input. The warping field is generated using a model trained with pairings of training images and known warped images using supervised learning techniques and one or more losses. The warped image generator determines, based on the warping field, a set of displacements associated with pixels of the input image. These displacements indicate pixel displacement directions for the pixels of the input image. These displacements are applied to the digital image to generate a warped image of the object.

    Neural network-based camera calibration
    13.
    发明授权
    Neural network-based camera calibration 有权

    公开(公告)号：US10964060B2

    公开(公告)日：2021-03-30

    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.

    Machine learning based image calibration using dense fields
    15.
    发明授权
    Machine learning based image calibration using dense fields 有权

    公开(公告)号：US12236640B2

    公开(公告)日：2025-02-25

    申请号：US17656796

    申请日：2022-03-28

    Applicant: ADOBE INC.

    Inventor： Jianming Zhang ,  Linyi Jin ,  Kevin Matzen ,  Oliver Wang ,  Yannick Hold-Geoffroy

    IPC: G06T7/00 ,  G06N3/045 ,  G06T7/80 ,  G06T9/00 ,  G06T11/00 ,  G06V10/764

    Abstract: Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.

    Generating physically-based material maps
    17.
    发明授权
    Generating physically-based material maps 有权

    公开(公告)号：US11663775B2

    公开(公告)日：2023-05-30

    申请号：US17233861

    申请日：2021-04-19

    Applicant: ADOBE INC.

    Inventor： Akshat Dave ,  Kalyan Krishna Sunkavalli ,  Yannick Hold-Geoffroy ,  Milos Hasan

    IPC: G06T15/50 ,  G06T15/00 ,  G06N3/08 ,  G06T15/04

    CPC classification number: G06T15/506 ,  G06N3/08 ,  G06T15/005 ,  G06T15/04

    Abstract: Methods, system, and computer storage media are provided for generating physical-based materials for rendering digital objects with an appearance of a real-world material. Images depicted the real-world material, including diffuse component images and specular component images, are captured using different lighting patterns, which may include area lights. From the captured images, approximations of one or more material maps are determined using a photometric stereo technique. Based on the approximations and the captured images, a neural network system generates a set of material maps, such as a diffuse albedo material map, a normal material map, a specular albedo material map, and a roughness material map. The material maps from the neural network may be optimized based on a comparison of the input images of the real-world material and images rendered from the material maps.

    Large-scale outdoor augmented reality scenes using camera pose based on learned descriptors
    19.
    发明授权
    Large-scale outdoor augmented reality scenes using camera pose based on learned descriptors 有权

    公开(公告)号：US11568642B2

    公开(公告)日：2023-01-31

    申请号：US17068429

    申请日：2020-10-12

    Applicant: ADOBE INC.

    Inventor： Michal Lukác ,  Oliver Wang ,  Jan Brejcha ,  Yannick Hold-Geoffroy ,  Martin {hacek over (C)}adík

    IPC: G06V20/20 ,  G06N20/00 ,  G06T7/70

    Abstract: Methods and systems are provided for facilitating large-scale augmented reality in relation to outdoor scenes using estimated camera pose information. In particular, camera pose information for an image can be estimated by matching the image to a rendered ground-truth terrain model with known camera pose information. To match images with such renders, data driven cross-domain feature embedding can be learned using a neural network. Cross-domain feature descriptors can be used for efficient and accurate feature matching between the image and the terrain model renders. This feature matching allows images to be localized in relation to the terrain model, which has known camera pose information. This known camera pose information can then be used to estimate camera pose information in relation to the image.

    GENERATING PHYSICALLY-BASED MATERIAL MAPS
    20.
    发明申请
    GENERATING PHYSICALLY-BASED MATERIAL MAPS 有权

    公开(公告)号：US20220335682A1

    公开(公告)日：2022-10-20

    申请号：US17233861

    申请日：2021-04-19

    Applicant: ADOBE INC.

    Inventor： Akshat Dave ,  Kalyan Krishna Sunkavalli ,  Yannick Hold-Geoffroy ,  Milos Hasan

    IPC: G06T15/50 ,  G06T15/04 ,  G06N3/08 ,  G06T15/00

    Abstract: Methods, system, and computer storage media are provided for generating physical-based materials for rendering digital objects with an appearance of a real-world material. Images depicted the real-world material, including diffuse component images and specular component images, are captured using different lighting patterns, which may include area lights. From the captured images, approximations of one or more material maps are determined using a photometric stereo technique. Based on the approximations and the captured images, a neural network system generates a set of material maps, such as a diffuse albedo material map, a normal material map, a specular albedo material map, and a roughness material map. The material maps from the neural network may be optimized based on a comparison of the input images of the real-world material and images rendered from the material maps.

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