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：US20250078406A1

公开(公告)日：2025-03-06

申请号：US18242380

申请日：2023-09-05

Applicant: Adobe Inc.

Inventor： Jae Shin Yoon ,  Yangtuanfeng Wang ,  Krishna Kumar Singh ,  Junying Wang ,  Jingwan Lu

IPC: G06T17/10 ,  G06T5/00 ,  G06T7/70

Abstract: A modeling system accesses a two-dimensional (2D) input image displayed via a user interface, the 2D input image depicting, at a first view, a first object. At least one region of the first object is not represented by pixel values of the 2D input image. The modeling system generates, by applying a 3D representation generation model to the 2D input image, a three-dimensional (3D) representation of the first object that depicts an entirety of the first object including the first region. The modeling system displays, via the user interface, the 3D representation, wherein the 3D representation is viewable via the user interface from a plurality of views including the first view.

公开(公告)号：US12230014B2

公开(公告)日：2025-02-18

申请号：US17680906

申请日：2022-02-25

Applicant: ADOBE INC.

Inventor： Yijun Li ,  Utkarsh Ojha ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman ,  Alexei A. Efros

IPC: G06V10/774 ,  G06F3/04842

Abstract: An image generation system enables user input during the process of training a generative model to influence the model's ability to generate new images with desired visual features. A source generative model for a source domain is fine-tuned using training images in a target domain to provide an adapted generative model for the target domain. Interpretable factors are determined for the source generative model and the adapted generative model. A user interface is provided that enables a user to select one or more interpretable factors. The user-selected interpretable factor(s) are used to generate a user-adapted generative model, for instance, by using a loss function based on the user-selected interpretable factor(s). The user-adapted generative model can be used to create new images in the target domain.

公开(公告)号：US20250005824A1

公开(公告)日：2025-01-02

申请号：US18341982

申请日：2023-06-27

Applicant: ADOBE INC.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Duygu Ceylan Aksit ,  Krishna Kumar Singh ,  Jingwan Lu ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06T11/60 ,  G06T3/00 ,  G06T7/11 ,  G06T11/00

Abstract: Systems and methods for image processing are described. One aspect of the systems and methods includes receiving a plurality of images comprising a first image depicting a first body part and a second image depicting a second body part and encoding, using a texture encoder, the first image and the second image to obtain a first texture embedding and a second texture embedding, respectively. Then, a composite image is generated using a generative decoder, the composite image depicting the first body part and the second body part based on the first texture embedding and the second texture embedding.

公开(公告)号：US20250005812A1

公开(公告)日：2025-01-02

申请号：US18215484

申请日：2023-06-28

Applicant: Adobe Inc.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Mausoom Sarkar ,  Krishna Kumar Singh ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Balaji Krishnamurthy

IPC: G06T11/00 ,  G06T7/12 ,  G06T7/70

Abstract: In implementations of systems for human reposing based on multiple input views, a computing device implements a reposing system to receive input data describing: input digital images; pluralities of keypoints corresponding to the input digital images, the pluralities of keypoints representing poses of a person depicted in the input digital images; and a plurality of keypoints representing a target pose. The reposing system generates selection masks corresponding to the input digital images by processing the input data using a machine learning model. The selection masks represent likelihoods of spatial correspondence between pixels of an output digital image and portions of the input digital images. The reposing system generates the output digital image depicting the person in the target pose for display in a user interface based on the selection masks and the input data.

公开(公告)号：US20240428564A1

公开(公告)日：2024-12-26

申请号：US18213118

申请日：2023-06-22

Applicant: Adobe Inc.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Mausoom Sarkar ,  Krishna Kumar Singh ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Balaji Krishnamurthy

IPC: G06V10/77 ,  G06T3/00 ,  G06T3/40

Abstract: In implementations of systems for generating images for human reposing, a computing device implements a reposing system to receive input data describing an input digital image depicting a person in a first pose, a first plurality of keypoints representing the first pose, and a second plurality of keypoints representing a second pose. The reposing system generates a mapping by processing the input data using a first machine learning model. The mapping indicates a plurality of first portions of the person in the second pose that are visible in the input digital image and a plurality of second portions of the person in the second pose that are invisible in the input digital image. The reposing system generates an output digital image depicting the person in the second pose by processing the mapping, the first plurality of keypoints, and the second plurality of keypoints using a second machine learning model.

公开(公告)号：US20240428491A1

公开(公告)日：2024-12-26

申请号：US18340445

申请日：2023-06-23

Applicant: Adobe Inc.

Inventor： Jae Shin Yoon ,  Duygu Ceylan Aksit ,  Yangtuanfeng Wang ,  Jingwan Lu ,  Jimei Yang ,  Zhixin Shu ,  Chengan He ,  Yi Zhou ,  Jun Saito ,  James Zachary

IPC: G06T13/40

Abstract: The present disclosure relates to a system that utilizes neural networks to generate looping animations from still images. The system fits a 3D model to a pose of a person in a digital image. The system receives a 3D animation sequence that transitions between a starting pose and an ending pose. The system generates, utilizing an animation transition neural network, first and second 3D animation transition sequences that respectively transition between the pose of the person and the starting pose and between the ending pose and the pose of the person. The system modifies each of the 3D animation sequence, the first 3D animation transition sequence, and the second 3D animation transition sequence by applying a texture map. The system generates a looping 3D animation by combining the modified 3D animation sequence, the modified first 3D animation transition sequence, and the modified second 3D animation transition sequence.

公开(公告)号：US20240320789A1

公开(公告)日：2024-09-26

申请号：US18585957

申请日：2024-02-23

Applicant: ADOBE INC.

Inventor： Tobias Hinz ,  Taesung Park ,  Jingwan Lu ,  Elya Shechtman ,  Richard Zhang ,  Oliver Wang

IPC: G06T3/4053 ,  G06T3/4046 ,  G06T11/00

CPC classification number: G06T3/4053 ,  G06T3/4046 ,  G06T11/00

Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation include obtaining an input image having a first resolution, where the input image includes random noise, and generating a low-resolution image based on the input image, where the low-resolution image has the first resolution. The method, non-transitory computer readable medium, apparatus, and system further include generating a high-resolution image based on the low-resolution image, where the high-resolution image has a second resolution that is greater than the first resolution.

公开(公告)号：US11880957B2

公开(公告)日：2024-01-23

申请号：US17013332

申请日：2020-09-04

Applicant: Adobe Inc.

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

IPC: G06T3/00 ,  G06N20/00 ,  G06T11/00

CPC classification number: G06T3/0056 ,  G06N20/00 ,  G06T11/00 ,  G06T2207/20081

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.

公开(公告)号：US11880766B2

公开(公告)日：2024-01-23

申请号：US17384357

申请日：2021-07-23

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Ratheesh Kalarot ,  Wei-An Lin ,  Richard Zhang ,  Niloy Mitra ,  Elya Shechtman ,  Shabnam Ghadar ,  Zhixin Shu ,  Yannick Hold-Geoffrey ,  Nathan Carr ,  Jingwan Lu ,  Oliver Wang ,  Jun-Yan Zhu

IPC: G06N3/08 ,  G06F3/04845 ,  G06F3/04847 ,  G06T11/60 ,  G06T3/40 ,  G06N20/20 ,  G06T5/00 ,  G06T5/20 ,  G06T3/00 ,  G06T11/00 ,  G06F18/40 ,  G06F18/211 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F3/04845 ,  G06F3/04847 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2163 ,  G06F18/40 ,  G06N3/045 ,  G06N20/20 ,  G06T3/0006 ,  G06T3/0093 ,  G06T3/40 ,  G06T3/4038 ,  G06T3/4046 ,  G06T5/005 ,  G06T5/20 ,  G06T11/001 ,  G06T11/60 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2210/22

Abstract: An improved system architecture uses a pipeline including a Generative Adversarial Network (GAN) including a generator neural network and a discriminator neural network to generate an image. An input image in a first domain and information about a target domain are obtained. The domains correspond to image styles. An initial latent space representation of the input image is produced by encoding the input image. An initial output image is generated by processing the initial latent space representation with the generator neural network. Using the discriminator neural network, a score is computed indicating whether the initial output image is in the target domain. A loss is computed based on the computed score. The loss is minimized to compute an updated latent space representation. The updated latent space representation is processed with the generator neural network to generate an output image in the target domain.