公开(公告)号：US20240169499A1

公开(公告)日：2024-05-23

申请号：US18057930

申请日：2022-11-22

Applicant: ADOBE INC.

Inventor： Anjali Agarwal ,  Siavash Khodadadeh ,  Ratheesh Kalarot ,  Hui Qu ,  Sven C. Olsen ,  Shabnam Ghadar

IPC: G06T5/00 ,  G06N3/04 ,  G06T3/40

CPC classification number: G06T5/005 ,  G06N3/0454 ,  G06T3/4046 ,  G06T3/4053 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: Systems and methods for image processing are provided. Embodiments include identifying an image of a face that includes an artifact in a part of the face. A machine learning model generates an intermediate image based on the original image. The intermediate image depicts the part of the face in a closed position. Then the model generates a corrected image based on the intermediate image. The corrected image depicts the face with the part of the face in an open position and without the artifact.

公开(公告)号：US12254597B2

公开(公告)日：2025-03-18

申请号：US17709221

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Wei-An Lin ,  Timothy M. Converse ,  Shabnam Ghadar ,  Ratheesh Kalarot ,  John Nack ,  Jingwan Lu ,  Hui Qu ,  Elya Shechtman ,  Baldo Faieta

IPC: G06T5/50 ,  G06N3/045

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.

公开(公告)号：US20230316475A1

公开(公告)日：2023-10-05

申请号：US17709221

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Wei-An Lin ,  Timothy M. Converse ,  Shabnam Ghadar ,  Ratheesh Kalarot ,  John Nack ,  Jingwan Lu ,  Hui Qu ,  Elya Shechtman ,  Baldo Faieta

IPC: G06T5/50 ,  G06N3/04

CPC classification number: G06T5/50 ,  G06N3/0454 ,  G06T2207/20221 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.

公开(公告)号：US20240412429A1

公开(公告)日：2024-12-12

申请号：US18332163

申请日：2023-06-09

Applicant: ADOBE INC.

Inventor： Wei-An Lin ,  Hui Qu ,  Siavash Khodadadeh ,  Kevin Duarte ,  Surabhi Sinha ,  Ratheesh Kalarot ,  Shabnam Ghadar

IPC: G06T11/60 ,  G06V10/82 ,  G06V40/16

Abstract: Systems and methods for editing multiple attributes of an image are described. Embodiments are configured to receive input comprising an image of a face and a target value of an attribute of the face to be modified; encode the image using an encoder of an image generation neural network to obtain an image embedding; and generate a modified image of the face having the target value of the attribute based on the image embedding using a decoder of the image generation neural network. The image generation neural network is trained using a plurality of training images generated by a separate training image generation neural network, and the plurality of training images include a first synthetic image having a first value of the attribute and a second synthetic image depicting a same face as the first synthetic image with a second value of the attribute.

公开(公告)号：US12254594B2

公开(公告)日：2025-03-18

申请号：US17657691

申请日：2022-04-01

Applicant: Adobe Inc.

Inventor： Hui Qu ,  Jingwan Lu ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin ,  Elya Shechtman

IPC: G06T5/50 ,  G06N3/04 ,  G06N3/045 ,  G06T7/11

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently enhancing details in edited images. The disclosed system iteratively updates residual detail latent code for segments in edited images where detail has been lost through the editing process. More particularly, the disclosed system enhances an edited segment in an edited image based on details in a detailed segment of an image. Additionally, the disclosed system may utilize a detail neural network encoder to project the detailed segment and a corresponding segment of the edited image into a residual detail latent code. In some embodiments, the disclosed system generates a refined edited image based on the residual detail latent code and a latent vector of the edited image.

公开(公告)号：US20230316606A1

公开(公告)日：2023-10-05

申请号：US17655739

申请日：2022-03-21

Applicant: Adobe Inc.

Inventor： Hui Qu ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Ratheesh Kalarot ,  Richard Zhang ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin

IPC: G06N3/04 ,  G06T11/60

CPC classification number: G06T11/60 ,  G06N3/0454

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for latent-based editing of digital images using a generative neural network. In particular, in one or more embodiments, the disclosed systems perform latent-based editing of a digital image by mapping a feature tensor and a set of style vectors for the digital image into a joint feature style space. In one or more implementations, the disclosed systems apply a joint feature style perturbation and/or modification vectors within the joint feature style space to determine modified style vectors and a modified feature tensor. Moreover, in one or more embodiments the disclosed systems generate a modified digital image utilizing a generative neural network from the modified style vectors and the modified feature tensor.

公开(公告)号：US20230316474A1

公开(公告)日：2023-10-05

申请号：US17657691

申请日：2022-04-01

Applicant: Adobe Inc.

Inventor： Hui Qu ,  Jingwan Lu ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin ,  Elya Shechtman

IPC: G06T5/50 ,  G06T7/11 ,  G06N3/04

CPC classification number: G06T5/50 ,  G06T7/11 ,  G06N3/0454 ,  G06T2207/20172 ,  G06T2207/20084

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently enhancing details in edited images. The disclosed system iteratively updates residual detail latent code for segments in edited images where detail has been lost through the editing process. More particularly, the disclosed system enhances an edited segment in an edited image based on details in a detailed segment of an image. Additionally, the disclosed system may utilize a detail neural network encoder to project the detailed segment and a corresponding segment of the edited image into a residual detail latent code. In some embodiments, the disclosed system generates a refined edited image based on the residual detail latent code and a latent vector of the edited image.