公开(公告)号：US12271996B2

公开(公告)日：2025-04-08

申请号：US18166189

申请日：2023-02-08

Applicant: ADOBE INC.

Inventor： Sudeep Siddheshwar Katakol ,  Taesung Park ,  Aliakbar Darabi ,  Kevin Duarte ,  Ryan Joe Murdock

IPC: G06T15/50 ,  G06T11/60

Abstract: A method for training a GAN to transfer lighting from a reference image to a source image includes: receiving the source image and the reference image; generating a lighting vector from the reference image; applying features of the source image and the lighting vector to a generative network of the GAN to create a generated image; applying features of the reference image and the lighting vector to a discriminative network of the GAN to update weights of the discriminative network; and applying features of the generated image and the lighting vector to the discriminative network to update weights of the generative network.

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：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.

公开(公告)号：US20250069299A1

公开(公告)日：2025-02-27

申请号：US18452827

申请日：2023-08-21

Applicant: ADOBE INC.

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

IPC: G06T11/60

Abstract: One or more aspects of a method, apparatus, and non-transitory computer readable medium include obtaining an input latent vector for an image generation network and a target lighting representation. A modified latent vector is generated based on the input latent vector and the target lighting representation, and an image generation network generates an image based on the modified latent vector using.

公开(公告)号：US11900519B2

公开(公告)日：2024-02-13

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 ,  G06N3/045

CPC classification number: G06T13/40 ,  G06N3/045 ,  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.