公开(公告)号：US11748928B2

公开(公告)日：2023-09-05

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 ,  G06F21/62 ,  G06F16/56 ,  G06F16/532

CPC classification number: G06T11/60 ,  G06F16/532 ,  G06F16/56 ,  G06F21/6254 ,  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：US20230076196A1

公开(公告)日：2023-03-09

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 ,  G06N3/04 ,  G06T3/40 ,  H04L29/06

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：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.

公开(公告)号：US20210272253A1

公开(公告)日：2021-09-02

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: G06T5/50 ,  G06K9/00 ,  G06T7/11 ,  G06K9/62 ,  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.