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    System for automatic video reframing
    72.
    发明授权
    System for automatic video reframing 有权

    公开(公告)号：US11758082B2

    公开(公告)日：2023-09-12

    申请号：US17526853

    申请日：2021-11-15

    Applicant: Adobe Inc.

    Inventor： Lu Zhang ,  Jianming Zhang ,  Zhe Lin ,  Radomir Meeh

    IPC: H04N5/262 ,  G11B27/031 ,  G06V20/40 ,  G06V10/20 ,  G06V40/18

    CPC classification number: H04N5/2628 ,  G06V10/255 ,  G06V20/40 ,  G06V20/41 ,  G11B27/031 ,  G06V40/193

    Abstract: Systems and methods provide reframing operations in a smart editing system that may generate a focal point within a mask of an object for each frame of a video segment and perform editing effects on the frames of the video segment to quickly provide users with natural video editing effects. A reframing engine may processes video clips using a segmentation and hotspot module to determine a salient region of an object, generate a mask of the object, and track the trajectory of an object in the video clips. The reframing engine may then receive reframing parameters from a crop suggestion module and a user interface. Based on the determined trajectory of an object in a video clip and reframing parameters, the reframing engine may use reframing logic to produce temporally consistent reframing effects relative to an object for the video clip.

    End-to-end relighting of a foreground object technical
    74.
    发明授权
    End-to-end relighting of a foreground object technical 有权

    公开(公告)号：US11657546B2

    公开(公告)日：2023-05-23

    申请号：US17664800

    申请日：2022-05-24

    Applicant: Adobe Inc.

    Inventor： Xin Sun ,  Ruben Villegas ,  Manuel Lagunas Arto ,  Jimei Yang ,  Jianming Zhang

    IPC: G06T7/11 ,  G06T7/194 ,  G06T7/90 ,  G06T11/00

    CPC classification number: G06T11/001 ,  G06T7/11 ,  G06T7/194 ,  G06T7/90 ,  G06T2207/20084 ,  G06T2207/30196

    Abstract: Introduced here are techniques for relighting an image by automatically segmenting a human object in an image. The segmented image is input to an encoder that transforms it into a feature space. The feature space is concatenated with coefficients of a target illumination for the image and input to an albedo decoder and a light transport detector to predict an albedo map and a light transport matrix, respectively. In addition, the output of the encoder is concatenated with outputs of residual parts of each decoder and fed to a light coefficients block, which predicts coefficients of the illumination for the image. The light transport matrix and predicted illumination coefficients are multiplied to obtain a shading map that can sharpen details of the image. Scaling the resulting image by the albedo map to produce the relight image. The relight image can be refined to denoise the relight image.

    Learning copy space using regression and segmentation neural networks
    76.
    发明授权
    Learning copy space using regression and segmentation neural networks 有权

    公开(公告)号：US11605168B2

    公开(公告)日：2023-03-14

    申请号：US17215067

    申请日：2021-03-29

    Applicant: Adobe Inc.

    Inventor： Mingyang Ling ,  Alex Filipkowski ,  Zhe Lin ,  Jianming Zhang ,  Samarth Gulati

    IPC: G06K9/62 ,  G06T7/11 ,  G06T7/136 ,  G06T7/143 ,  G06T7/174 ,  G06F18/214 ,  G06N3/045 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V10/26

    Abstract: Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.

    Utilizing a two-stream encoder neural network to generate composite digital images
    77.
    发明授权
    Utilizing a two-stream encoder neural network to generate composite digital images 有权

    公开(公告)号：US11568544B2

    公开(公告)日：2023-01-31

    申请号：US17483280

    申请日：2021-09-23

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Jianming Zhang ,  He Zhang ,  Federico Perazzi

    IPC: G06K9/62 ,  G06T7/10 ,  G06N3/04 ,  G06N3/08 ,  G06T11/60

    Abstract: The present disclosure relates to utilizing a neural network having a two-stream encoder architecture to accurately generate composite digital images that realistically portray a foreground object from one digital image against a scene from another digital image. For example, the disclosed systems can utilize a foreground encoder of the neural network to identify features from a foreground image and further utilize a background encoder to identify features from a background image. The disclosed systems can then utilize a decoder to fuse the features together and generate a composite digital image. The disclosed systems can train the neural network utilizing an easy-to-hard data augmentation scheme implemented via self-teaching. The disclosed systems can further incorporate the neural network within an end-to-end framework for automation of the image composition process.

    SKY REPLACEMENT COLOR HARMONIZATION
    78.
    发明申请
    SKY REPLACEMENT COLOR HARMONIZATION 有权

    公开(公告)号：US20220335671A1

    公开(公告)日：2022-10-20

    申请号：US17232890

    申请日：2021-04-16

    Applicant: ADOBE INC

    Inventor： Alan Erickson ,  Kalyan Sunkavalli ,  I-Ming Pao ,  Guotong Feng ,  Jianming Zhang ,  Frederick Mandia

    IPC: G06T11/60 ,  G06T11/40 ,  G06N3/02

    Abstract: Systems and methods for image editing are described. Embodiments of the present disclosure provide an image editing system for performing image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. According to some embodiments described herein, real-time color harmonization based on the visible sky region may be used to produce more natural colorization. In some examples, horizon-aware sky alignment and placement with advanced padding may also be used. For example, the horizons of the original image and the replacement image may be automatically detected and aligned, and color harmonization may be performed based on the aligned images.

    GENERATING DEEP HARMONIZED DIGITAL IMAGES
    80.
    发明申请
    GENERATING DEEP HARMONIZED DIGITAL IMAGES 有权

    公开(公告)号：US20220292654A1

    公开(公告)日：2022-09-15

    申请号：US17200338

    申请日：2021-03-12

    Applicant: Adobe Inc.

    Inventor： He Zhang ,  Yifan Jiang ,  Yilin Wang ,  Jianming Zhang ,  Kalyan Sunkavalli ,  Sarah Kong ,  Su Chen ,  Sohrab Amirghodsi ,  Zhe Lin

    IPC: G06T5/50 ,  G06T7/194 ,  G06T11/60 ,  G06T11/00 ,  G06N3/04 ,  G06N3/08

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.

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