公开(公告)号：US11531697B2

公开(公告)日：2022-12-20

申请号：US17087982

申请日：2020-11-03

Applicant: Adobe Inc.

Inventor： Jinrong Xie ,  Shabnam Ghadar ,  Jun Saito ,  Jimei Yang ,  Elnaz Morad ,  Duygu Ceylan Aksit ,  Baldo Faieta ,  Alex Filipkowski

IPC: G06F16/55 ,  G06F16/538 ,  G06F16/583 ,  G06F16/56 ,  G06F16/535 ,  G06N3/08 ,  G06T7/73

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly identifying and providing digital images of human figures in poses corresponding to a query pose. In particular, the disclosed systems can provide multiple approaches to searching for and providing pose images, including identifying a digital image depicting a human figure in a particular pose based on a query digital image that depicts the pose or identifying a digital image depicting a human figure in a particular pose based on a virtual mannequin. Indeed, the disclosed systems can provide a manipulable virtual mannequin that defines a query pose for searching a repository of digital images. Additionally, the disclosed systems can generate and provide digital pose image groups by clustering digital images together according to poses of human figures within a pose feature space.

公开(公告)号：US11380033B2

公开(公告)日：2022-07-05

申请号：US16738359

申请日：2020-01-09

Applicant: Adobe Inc.

Inventor： Kate Sousa ,  Zhe Lin ,  Saeid Motiian ,  Pramod Srinivasan ,  Baldo Faieta ,  Alex Filipkowski

IPC: G06T11/60 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/25

Abstract: Based on a received digital image and text, a neural network trained to identify candidate text placement areas within images may be used to generate a mask for the digital image that includes a candidate text placement area. A bounding box for the digital image may be defined for the text and based on the candidate text placement area, and the text may be superimposed onto the digital image within the bounding box.

公开(公告)号：US20220138249A1

公开(公告)日：2022-05-05

申请号：US17087982

申请日：2020-11-03

Applicant: Adobe Inc.

Inventor： Jinrong Xie ,  Shabnam Ghadar ,  Jun Saito ,  Jimei Yang ,  Elnaz Morad ,  Duygu Ceylan Aksit ,  Baldo Faieta ,  Alex Filipkowski

IPC: G06F16/55 ,  G06F16/538 ,  G06N3/08 ,  G06F16/535 ,  G06T7/73 ,  G06F16/56 ,  G06F16/583

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly identifying and providing digital images of human figures in poses corresponding to a query pose. In particular, the disclosed systems can provide multiple approaches to searching for and providing pose images, including identifying a digital image depicting a human figure in a particular pose based on a query digital image that depicts the pose or identifying a digital image depicting a human figure in a particular pose based on a virtual mannequin. Indeed, the disclosed systems can provide a manipulable virtual mannequin that defines a query pose for searching a repository of digital images. Additionally, the disclosed systems can generate and provide digital pose image groups by clustering digital images together according to poses of human figures within a pose feature space.

公开(公告)号：US20210216824A1

公开(公告)日：2021-07-15

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 ,  G06N3/04 ,  G06T7/143 ,  G06T7/174 ,  G06T7/136

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.