公开(公告)号：US20220122308A1

公开(公告)日：2022-04-21

申请号：US17468546

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Kevin Wampler ,  Jingwan Lu ,  Jakub Fiser ,  Elya Shechtman ,  Aliakbar Darabi ,  Alexandru Vasile Costin

IPC: G06T11/60 ,  G06T3/40 ,  G06T3/00 ,  G06T11/00 ,  G06T5/00

Abstract: Systems and methods seamlessly blend edited and unedited regions of an image. A computing system crops an input image around a region to be edited. The system applies an affine transformation to rotate the cropped input image. The system provides the rotated cropped input image as input to a machine learning model to generate a latent space representation of the rotated cropped input image. The system edits the latent space representation and provides the edited latent space representation to a generator neural network to generate a generated edited image. The system applies an inverse affine transformation to rotate the generated edited image and aligns an identified segment of the rotated generated edited image with an identified corresponding segment of the input image to produce an aligned rotated generated edited image. The system blends the aligned rotated generated edited image with the input image to generate an edited output image.

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：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.

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：US11727614B2

公开(公告)日：2023-08-15

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 ,  G06T11/20 ,  G06N3/04

CPC classification number: G06T11/60 ,  G06N3/04 ,  G06T11/203

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：US20220164380A1

公开(公告)日：2022-05-26

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 ,  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：US20220122232A1

公开(公告)日：2022-04-21

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: G06T5/00 ,  G06T5/20 ,  G06N3/08

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：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：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.