公开(公告)号：US20230137774A1

公开(公告)日：2023-05-04

申请号：US17453595

申请日：2021-11-04

Applicant: ADOBE INC.

Inventor： Baldo Faieta ,  Ajinkya Gorakhnath Kale ,  Pranav Vineet Aggarwal ,  Naveen Marri ,  Saeid Motiian ,  Tracy Holloway King ,  Alex Filipkowski ,  Shabnam Ghadar

IPC: G06F16/583 ,  G06F16/58 ,  G06F16/538 ,  G06F40/295 ,  G06F16/535 ,  G06N3/08

Abstract: Systems and methods for image retrieval are described. Embodiments of the present disclosure receive a search query from a user; extract an entity and a color phrase describing the entity from the search query; generate an entity color embedding in a color embedding space from the color phrase using a multi-modal color encoder; identify an image in a database based on metadata for the image including an object label corresponding to the extracted entity and an object color embedding in the color embedding space corresponding to the object label; and provide image information for the image to the user based on the metadata.

公开(公告)号：US20220391611A1

公开(公告)日：2022-12-08

申请号：US17341778

申请日：2021-06-08

Applicant: ADOBE INC.

Inventor： RATHEESH KALAROT ,  Siavash Khodadadeh ,  Baldo Faieta ,  Shabnam Ghadar ,  Saeid Motiian ,  Wei-An Lin ,  Zhe Lin

IPC: G06K9/00 ,  G06N3/08 ,  G06N3/04 ,  G06T11/60

Abstract: Systems and methods for image processing are described. One or more embodiments of the present disclosure identify a latent vector representing an image of a face, identify a target attribute vector representing a target attribute for the image, generate a modified latent vector using a mapping network that converts the latent vector and the target attribute vector into a hidden representation having fewer dimensions than the latent vector, wherein the modified latent vector is generated based on the hidden representation, and generate a modified image based on the modified latent vector, wherein the modified image represents the face with the target attribute.

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：US20240428482A1

公开(公告)日：2024-12-26

申请号：US18338964

申请日：2023-06-21

Applicant: Adobe Inc.

Inventor： Siavash Khodadadeh ,  Jinrong Xie ,  Ratheesh Kalarot ,  Shabnam Ghadar

IPC: G06T11/60 ,  G06T3/00 ,  G06T5/00 ,  G06T5/20 ,  G06T7/11

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and composting pixels of a digital image that depict hair of an individual using generative neural networks. In some embodiments, the disclosed systems receive a modification to a face crop enclosing a face depicted within a digital image. In some cases, the disclosed systems determine, from the modification, modified hair pixels within the face crop of the digital image and unmodified hair pixels outside of the face crop of the digital image. The disclosed systems generate, for the unmodified hair pixels outside of the face crop, replacement hair pixels that resemble the modified hair pixels utilizing a generative neural network. Additionally, the disclosed systems generate a modified digital image by replacing the unmodified hair pixels outside of the face crop with the replacement hair pixels.

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：US12153705B2

公开(公告)日：2024-11-26

申请号：US17217024

申请日：2021-03-30

Applicant: ADOBE INC.

Inventor： William Marino ,  Tim Converse ,  Sudharshan reddy Kakumanu ,  Shabnam Ghadar ,  Nico Becherer ,  Dhaval Shah ,  Ben Bowles ,  Alvin Ghouas ,  Alexander Riss

IPC: G06F21/62 ,  G06F21/31 ,  G06N20/00

Abstract: The present disclosure describes systems and methods for a privacy sensitive computing system. One or more embodiments provide a protected computing environment, a code authorization unit, and a data aggregation unit. For example, some embodiments of the privacy sensitive computing system may train unsupervised or self-supervised ML models on user-generated assets subject to privacy considerations that mandate those assets are not viewed directly by human eyes.

公开(公告)号：US11941727B2

公开(公告)日：2024-03-26

申请号：US17813987

申请日：2022-07-21

Applicant: ADOBE INC.

Inventor： Saeid Motiian ,  Wei-An Lin ,  Shabnam Ghadar

IPC: G06T11/00 ,  G06V40/16

CPC classification number: G06T11/00 ,  G06V40/168 ,  G06T2200/24

Abstract: Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.

公开(公告)号：US11928762B2

公开(公告)日：2024-03-12

申请号：US17466699

申请日：2021-09-03

Applicant: ADOBE INC.

Inventor： Akhilesh Kumar ,  Ratheesh Kalarot ,  Baldo Faieta ,  Shabnam Ghadar

IPC: G06T11/60 ,  G06N3/045 ,  H04L65/401

CPC classification number: G06T11/60 ,  G06N3/045 ,  G06T3/4046 ,  H04L65/4015

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for editing images using a web-based intermediary between a user interface on a client device and an image editing neural network(s) (e.g., a generative adversarial network) on a server(s). The present image editing system supports multiple users in the same software container, advanced concurrency of projection and transformation of the same image, clubbing transformation requests from several users hosted in the same software container, and smooth display updates during a progressive projection.

公开(公告)号：US11663265B2

公开(公告)日：2023-05-30

申请号：US17104745

申请日：2020-11-25

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Shabnam Ghadar ,  Saeid Motiian ,  Ratheesh Kalarot ,  Baldo Faieta ,  Alireza Zaeemzadeh

IPC: G06F16/583 ,  G06F16/532 ,  G06N20/00 ,  G06F16/54 ,  G06F16/56 ,  G06F16/538

CPC classification number: G06F16/583 ,  G06F16/532 ,  G06F16/538 ,  G06F16/54 ,  G06F16/56 ,  G06N20/00

Abstract: A query image is received, along with a query to initiate a search process to find other images based on the query image. The query includes a preference value associated with an attribute, the preference value indicative of a level of emphasis to be placed on the attribute during the search. A full query vector, which is within a first dimensional space and representative of the query image, is generated. The full query vector is projected to a reduced dimensional space having a dimensionality lower than the first dimensional space, to generate a query vector. An attribute direction corresponding to the attribute is identified. A plurality of candidate vectors of the reduced dimensional space is searched, based on the attribute direction, the query vector, and the preference value, to identify a target vector of the plurality of candidate vectors. A target image, representative of the target vector, is displayed.

公开(公告)号：US20230154088A1

公开(公告)日：2023-05-18

申请号：US17455318

申请日：2021-11-17

Applicant: ADOBE INC.

Inventor： Kevin Duarte ,  Wei-An Lin ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Jingwan Lu ,  Elya Shechtman ,  John Thomas Nack

IPC: G06T13/40 ,  G06T5/50 ,  G06N3/04

CPC classification number: G06T13/40 ,  G06N3/0454 ,  G06T5/50

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure encode features of a source image to obtain a source appearance encoding that represents inherent attributes of a face in the source image; encode features of a target image to obtain a target non-appearance encoding that represents contextual attributes of the target image; combine the source appearance encoding and the target non-appearance encoding to obtain combined image features; and generate a modified target image based on the combined image features, wherein the modified target image includes the inherent attributes of the face in the source image together with the contextual attributes of the target image.