公开(公告)号：US20230177824A1

公开(公告)日：2023-06-08

申请号：US18161666

申请日：2023-01-30

Applicant: Adobe Inc.

Inventor： Brian Price ,  Scott Cohen ,  Mai Long ,  Jun Hao Liew

IPC: G06V10/82 ,  G06N3/084 ,  G06T7/11 ,  G06V10/26 ,  G06V10/44 ,  G06F18/40 ,  G06N3/045 ,  G06N5/01 ,  G06V10/94 ,  G06V10/20

CPC classification number: G06V10/82 ,  G06N3/084 ,  G06T7/11 ,  G06V10/26 ,  G06V10/454 ,  G06F18/40 ,  G06N3/045 ,  G06N5/01 ,  G06V10/945 ,  G06V10/255 ,  G06N3/044

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

公开(公告)号：US10963759B2

公开(公告)日：2021-03-30

申请号：US16417115

申请日：2019-05-20

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Mai Long ,  Jonathan Brandt ,  Hailin Jin ,  Chen Fang

IPC: G06K9/66 ,  G06F16/532 ,  G06K9/46 ,  G06K9/62 ,  G06K9/72 ,  G06N3/04 ,  G06F16/583 ,  G06K9/52 ,  G06N3/08

Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.

公开(公告)号：US11798180B2

公开(公告)日：2023-10-24

申请号：US17186436

申请日：2021-02-26

Applicant: Adobe Inc.

Inventor： Wei Yin ,  Jianming Zhang ,  Oliver Wang ,  Simon Niklaus ,  Mai Long ,  Su Chen

IPC: G06T7/50 ,  G06T7/593 ,  G06T7/30 ,  G06T7/13 ,  G06T7/143 ,  G06T7/521

CPC classification number: G06T7/50 ,  G06T7/13 ,  G06T7/143 ,  G06T7/30 ,  G06T7/521 ,  G06T7/593 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: This disclosure describes one or more implementations of a depth prediction system that generates accurate depth images from single input digital images. In one or more implementations, the depth prediction system enforces different sets of loss functions across mix-data sources to generate a multi-branch architecture depth prediction model. For instance, in one or more implementations, the depth prediction model utilizes different data sources having different granularities of ground truth depth data to robustly train a depth prediction model. Further, given the different ground truth depth data granularities from the different data sources, the depth prediction model enforces different combinations of loss functions including an image-level normalized regression loss function and/or a pair-wise normal loss among other loss functions.

公开(公告)号：US20220277514A1

公开(公告)日：2022-09-01

申请号：US17186522

申请日：2021-02-26

Applicant: Adobe Inc.

Inventor： Wei Yin ,  Jianming Zhang ,  Oliver Wang ,  Simon Niklaus ,  Mai Long ,  Su Chen

IPC: G06T17/00 ,  G06K9/00 ,  G06N3/04 ,  G06T7/80

Abstract: This disclosure describes implementations of a three-dimensional (3D) scene recovery system that reconstructs a 3D scene representation of a scene portrayed in a single digital image. For instance, the 3D scene recovery system trains and utilizes a 3D point cloud model to recover accurate intrinsic camera parameters from a depth map of the digital image. Additionally, the 3D point cloud model may include multiple neural networks that target specific intrinsic camera parameters. For example, the 3D point cloud model may include a depth 3D point cloud neural network that recovers the depth shift as well as include a focal length 3D point cloud neural network that recovers the camera focal length. Further, the 3D scene recovery system may utilize the recovered intrinsic camera parameters to transform the single digital image into an accurate and realistic 3D scene representation, such as a 3D point cloud.

公开(公告)号：US11568627B2

公开(公告)日：2023-01-31

申请号：US16376704

申请日：2019-04-05

Applicant: Adobe Inc.

Inventor： Brian Price ,  Scott Cohen ,  Mai Long ,  Jun Hao Liew

IPC: G06V10/20 ,  G06N3/08 ,  G06K9/62 ,  G06N3/04 ,  G06T7/11 ,  G06N5/00 ,  G06V10/26 ,  G06V10/44 ,  G06V10/24

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

公开(公告)号：US20220284613A1

公开(公告)日：2022-09-08

申请号：US17186436

申请日：2021-02-26

Applicant: Adobe Inc.

Inventor： Wei Yin ,  Jianming Zhang ,  Oliver Wang ,  Simon Niklaus ,  Mai Long ,  Su Chen

IPC: G06T7/593 ,  G06T7/30 ,  G06T7/13 ,  G06T7/143 ,  G01S17/894 ,  G01S17/42

Abstract: This disclosure describes one or more implementations of a depth prediction system that generates accurate depth images from single input digital images. In one or more implementations, the depth prediction system enforces different sets of loss functions across mix-data sources to generate a multi-branch architecture depth prediction model. For instance, in one or more implementations, the depth prediction model utilizes different data sources having different granularities of ground truth depth data to robustly train a depth prediction model. Further, given the different ground truth depth data granularities from the different data sources, the depth prediction model enforces different combinations of loss functions including an image-level normalized regression loss function and/or a pair-wise normal loss among other loss functions.

公开(公告)号：US20190236394A1

公开(公告)日：2019-08-01

申请号：US16376704

申请日：2019-04-05

Applicant: Adobe Inc.

Inventor： Brian Price ,  Scott Cohen ,  Mai Long ,  Jun Hao Liew

IPC: G06K9/32 ,  G06K9/46

CPC classification number: G06K9/3241 ,  G06K9/4628 ,  G06K2009/366

Abstract: Systems and methods are disclosed for selecting target objects within digital images utilizing a multi-modal object selection neural network trained to accommodate multiple input modalities. In particular, in one or more embodiments, the disclosed systems and methods generate a trained neural network based on training digital images and training indicators corresponding to various input modalities. Moreover, one or more embodiments of the disclosed systems and methods utilize a trained neural network and iterative user inputs corresponding to different input modalities to select target objects in digital images. Specifically, the disclosed systems and methods can transform user inputs into distance maps that can be utilized in conjunction with color channels and a trained neural network to identify pixels that reflect the target object.

公开(公告)号：US20210256717A1

公开(公告)日：2021-08-19

申请号：US16790056

申请日：2020-02-13

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Oliver Wang ,  Mai Long ,  Ke Xian ,  Jianming Zhang

IPC: G06T7/50 ,  G06T7/13 ,  G06N3/08

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.

公开(公告)号：US20190272451A1

公开(公告)日：2019-09-05

申请号：US16417115

申请日：2019-05-20

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Mai Long ,  Jonathan Brandt ,  Hailin Jin ,  Chen Fang

IPC: G06K9/66 ,  G06F16/532 ,  G06K9/72 ,  G06K9/62 ,  G06K9/46 ,  G06N3/08 ,  G06K9/52

Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.

公开(公告)号：US11443481B1

公开(公告)日：2022-09-13

申请号：US17186522

申请日：2021-02-26

Applicant: Adobe Inc.

Inventor： Wei Yin ,  Jianming Zhang ,  Oliver Wang ,  Simon Niklaus ,  Mai Long ,  Su Chen

IPC: G06T17/00 ,  G06T7/80 ,  G06N3/04 ,  G06V20/00

Abstract: This disclosure describes implementations of a three-dimensional (3D) scene recovery system that reconstructs a 3D scene representation of a scene portrayed in a single digital image. For instance, the 3D scene recovery system trains and utilizes a 3D point cloud model to recover accurate intrinsic camera parameters from a depth map of the digital image. Additionally, the 3D point cloud model may include multiple neural networks that target specific intrinsic camera parameters. For example, the 3D point cloud model may include a depth 3D point cloud neural network that recovers the depth shift as well as include a focal length 3D point cloud neural network that recovers the camera focal length. Further, the 3D scene recovery system may utilize the recovered intrinsic camera parameters to transform the single digital image into an accurate and realistic 3D scene representation, such as a 3D point cloud.