公开(公告)号：US20240037805A1

公开(公告)日：2024-02-01

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：US20230386114A1

公开(公告)日：2023-11-30

申请号：US18449604

申请日：2023-08-14

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：US11823490B2

公开(公告)日：2023-11-21

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: G06V40/16 ,  G06N3/084 ,  G06T11/60 ,  G06N3/045

CPC classification number: G06V40/169 ,  G06N3/045 ,  G06N3/084 ,  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：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.

公开(公告)号：US20220122306A1

公开(公告)日：2022-04-21

申请号：US17468487

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: G06T11/60 ,  G06F3/0484 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods dynamically adjust an available range for editing an attribute in an image. An image editing system computes a metric for an attribute in an input image as a function of a latent space representation of the input image and a filtering vector for editing the input image. The image editing system compares the metric to a threshold. If the metric exceeds the threshold, then the image editing system selects a first range for editing the attribute in the input image. If the metric does not exceed the threshold, a second range is selected. The image editing system causes display of a user interface for editing the input image comprising an interface element for editing the attribute within the selected range.

公开(公告)号：US20220122221A1

公开(公告)日：2022-04-21

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: G06T3/40 ,  G06F3/0484 ,  G06N3/08 ,  G06N3/04

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：US20220121931A1

公开(公告)日：2022-04-21

申请号：US17384371

申请日：2021-07-23

Applicant: Adobe Inc.

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

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector to the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation. The encoder is trained using specialized loss functions including a segmentation loss and a mean latent loss.