公开(公告)号：US11748928B2

公开(公告)日：2023-09-05

申请号：US17094093

申请日：2020-11-10

Applicant: Adobe Inc.

Inventor： Yang Yang ,  Zhixin Shu ,  Shabnam Ghadar ,  Jingwan Lu ,  Jakub Fiser ,  Elya Schechtman ,  Cameron Y. Smith ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06T11/60 ,  G06F21/62 ,  G06F16/56 ,  G06F16/532

CPC classification number: G06T11/60 ,  G06F16/532 ,  G06F16/56 ,  G06F21/6254 ,  G06T2200/24

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

公开(公告)号：US20250061626A1

公开(公告)日：2025-02-20

申请号：US18674518

申请日：2024-05-24

Applicant: Adobe Inc.

Inventor： Xiaoyang Liu ,  Zhe Lin ,  Yuqian Zhou ,  Sohrab Amirghodsi ,  Sarah Jane Stuckey ,  Sakshi Gupta ,  Guotong Feng ,  Elya Schechtman ,  Connelly Stuart Barnes ,  Betty Leong

IPC: G06T11/60 ,  G06T5/77 ,  G06T11/20 ,  G06V10/764

Abstract: Techniques for performing a digital operation on a digital image are described along with methods and systems employing such techniques. According to the techniques, an input (e.g., an input stroke) is received by, for example, a processing system. Based upon the input, an area of the digital image upon which a digital operation (e.g., for removal of a distractor within the area) is to be performed is determined. In an implementation, one or more metrics of an input stroke are analyzed, typically in real time, to at least partially determine the area upon which the digital operation is to be performed. In an additional or alternative implementation, the input includes a first point, a second point and a connector, and the area is at least partially determined by a location of the first point relative to a location of the second point and/or by locations of the first point and/or second point relative to one or more edges of the digital image.

公开(公告)号：US20240087265A1

公开(公告)日：2024-03-14

申请号：US17942101

申请日：2022-09-09

Applicant: ADOBE INC.

Inventor： Taesung Park ,  Richard Zhang ,  Elya Schechtman

IPC: G06T19/20 ,  G06F3/04847 ,  G06F40/289 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06T19/20 ,  G06F3/04847 ,  G06F40/289 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06F40/284 ,  G06T2200/24 ,  G06T2210/61

Abstract: Various disclosed embodiments are directed to changing parameters of an input image or multidimensional representation of the input image based on a user request to change such parameters. An input image is first received. A multidimensional image that represents the input image in multiple dimensions is generated via a model. A request to change at least a first parameter to a second parameter is received via user input at a user device. Such request is a request to edit or generate the multidimensional image in some way. For instance, the request may be to change the light source position or camera position from a first set of coordinates to a second set of coordinates.

公开(公告)号：US20220156893A1

公开(公告)日：2022-05-19

申请号：US17098055

申请日：2020-11-13

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Elya Schechtman ,  Connelly Stuart Barnes ,  Sohrab Amirghodsi

IPC: G06T5/00 ,  G06T5/50 ,  G06T3/00 ,  G06N3/08

Abstract: Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.

公开(公告)号：US20240428384A1

公开(公告)日：2024-12-26

申请号：US18212992

申请日：2023-06-22

Applicant: Adobe Inc.

Inventor： Yuqian Zhou ,  Zhe Lin ,  Xiaoyang Liu ,  Sohrab Amirghodsi ,  Qing Liu ,  Lingzhi Zhang ,  Elya Schechtman ,  Connelly Stuart Barnes

IPC: G06T5/00 ,  G06T11/60

Abstract: Inpainting dispatch techniques for digital images are described. In one or more examples, an inpainting system includes a plurality of inpainting modules. The inpainting modules are configured to employ a variety of different techniques, respectively, as part of performing an inpainting operation. An inpainting dispatch module is also included as part of the inpainting system that is configured to select which of the plurality of inpainting modules are to be used to perform an inpainting operation for one or more regions in a digital image, automatically and without user intervention.

公开(公告)号：US20230360299A1

公开(公告)日：2023-11-09

申请号：US18224916

申请日：2023-07-21

Applicant: Adobe Inc.

Inventor： Yang Yang ,  Zhixin Shu ,  Shabnam Ghadar ,  Jingwan Lu ,  Jakub Fiser ,  Elya Schechtman ,  Cameron Y. Smith ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06T11/60 ,  G06F21/62 ,  G06F16/56 ,  G06F16/532

CPC classification number: G06T11/60 ,  G06F21/6254 ,  G06F16/56 ,  G06F16/532 ,  G06T2200/24

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

公开(公告)号：US20220148243A1

公开(公告)日：2022-05-12

申请号：US17094093

申请日：2020-11-10

Applicant: Adobe Inc.

Inventor： Yang Yang ,  Zhixin Shu ,  Shabnam Ghadar ,  Jingwan Lu ,  Jakub Fiser ,  Elya Schechtman ,  Cameron Y. Smith ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06T11/60 ,  G06T9/00 ,  G06F3/0484 ,  G06F16/532 ,  G06F21/62 ,  G06F16/56 ,  G06N20/00

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

公开(公告)号：US20220076374A1

公开(公告)日：2022-03-10

申请号：US17013332

申请日：2020-09-04

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Richard Zhang ,  Jingwan Lu ,  Elya Schechtman

IPC: G06T3/00 ,  G06N20/00

Abstract: One example method involves operations for receiving a request to transform an input image into a target image. Operations further include providing the input image to a machine learning model trained to adapt images. Training the machine learning model includes accessing training data having a source domain of images and a target domain of images with a target style. Training further includes using a pre-trained generative model to generate an adapted source domain of adapted images having the target style. The adapted source domain is generated by determining a rate of change for parameters of the target style, generating weighted parameters by applying a weight to each of the parameters based on their respective rate of change, and applying the weighted parameters to the source domain. Additionally, operations include using the machine learning model to generate the target image by modifying parameters of the input image using the target style.