公开(公告)号：US20240153259A1

公开(公告)日：2024-05-09

申请号：US18053450

申请日：2022-11-08

Applicant: ADOBE INC.

Inventor： Saeid Motiian ,  Shabnam Ghadar

IPC: G06V10/82 ,  G06V10/75 ,  G06V10/771

CPC classification number: G06V10/82 ,  G06V10/751 ,  G06V10/771

Abstract: Systems and methods for image processing are provided. One aspect of the systems and methods includes identifying a style image including a target style. A style encoder network generates a style vector representing the target style based on the style image. The style encoder can be trained based on a style loss that encourages the network to match a desired style. A a diffusion model generates a synthetic image that includes the target style based on the style vector. The diffusion model is trained independently of the style encoder network.

公开(公告)号：US20230342893A1

公开(公告)日：2023-10-26

申请号：US17660090

申请日：2022-04-21

Applicant: Adobe Inc.

Inventor： Tobias Hinz ,  Shabnam Ghadar ,  Richard Zhang ,  Ratheesh Kalarot ,  Jingwan Lu ,  Elya Shechtman

IPC: G06T5/50 ,  G06T11/60 ,  G06V10/82

CPC classification number: G06T5/50 ,  G06T11/60 ,  G06V10/82 ,  G06T2207/20221 ,  G06T2207/30201

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for combining digital images. In particular, in one or more embodiments, the disclosed systems combine latent codes of a source digital image and a target digital image utilizing a blending network to determine a combined latent encoding and generate a combined digital image from the combined latent encoding utilizing a generative neural network. In some embodiments, the disclosed systems determine an intersection face mask between the source digital image and the combined digital image utilizing a face segmentation network and combine the source digital image and the combined digital image utilizing the intersection face mask to generate a blended digital image.

公开(公告)号：US11574392B2

公开(公告)日：2023-02-07

申请号：US16803332

申请日：2020-02-27

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Vipul Dalal ,  Vera Lychagina ,  Shabnam Ghadar ,  Saeid Motiian ,  Rohith mohan Dodle ,  Prethebha Chandrasegaran ,  Mina Doroudi ,  Midhun Harikumar ,  Kannan Iyer ,  Jayant Kumar ,  Gaurav Kukal ,  Daniel Miranda ,  Charles R McKinney ,  Archit Kalra

IPC: G06K9/62 ,  G06V40/16 ,  G06T5/50 ,  G06T7/11 ,  G06T7/70

Abstract: The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.

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：US20220270310A1

公开(公告)日：2022-08-25

申请号：US17182492

申请日：2021-02-23

Applicant: Adobe Inc.

Inventor： Akhilesh Kumar ,  Baldo Faieta ,  Piotr Walczyszyn ,  Ratheesh Kalarot ,  Archie Bagnall ,  Shabnam Ghadar ,  Wei-An Lin ,  Cameron Smith ,  Christian Cantrell ,  Patrick Hebron ,  Wilson Chan ,  Jingwan Lu ,  Holger Winnemoeller ,  Sven Olsen

IPC: G06T11/60 ,  G06N3/04 ,  G06T11/20

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.

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：US11915520B2

公开(公告)日：2024-02-27

申请号：US17902349

申请日：2022-09-02

Applicant: Adobe Inc.

Inventor： Saeid Motiian ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

IPC: G06V40/16 ,  G06V30/194 ,  G06V40/10 ,  G06F18/00 ,  G06F18/20

CPC classification number: G06V40/172 ,  G06F18/00 ,  G06F18/29 ,  G06V30/194 ,  G06V40/10

Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

公开(公告)号：US11875221B2

公开(公告)日：2024-01-16

申请号：US17468476

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Wei-An Lin ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Jun-Yan Zhu ,  Niloy Mitra ,  Ratheesh Kalarot ,  Richard Zhang ,  Shabnam Ghadar ,  Zhixin Shu

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: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

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：US20230162407A1

公开(公告)日：2023-05-25

申请号：US17455796

申请日：2021-11-19

Applicant: ADOBE INC.

Inventor： Ratheesh Kalarot ,  Timothy M. Converse ,  Shabnam Ghadar ,  John Thomas Nack ,  Jingwan Lu ,  Elya Shechtman ,  Baldo Faieta ,  Akhilesh Kumar

IPC: G06T11/00 ,  G06K9/00 ,  G06N3/08

CPC classification number: G06T11/00 ,  G06K9/00288 ,  G06K9/00268 ,  G06N3/08

Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).