公开(公告)号：US20240338869A1

公开(公告)日：2024-10-10

申请号：US18474536

申请日：2023-09-26

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Krishna Kumar Singh ,  Zhifei Zhang ,  Difan Liu ,  Zhe Lin ,  Jianming Zhang ,  Qing Liu ,  Jingwan Lu ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Connelly Stuart Barnes

IPC: G06T11/60

CPC classification number: G06T11/60

Abstract: An image processing system obtains an input image (e.g., a user provided image, etc.) and a mask indicating an edit region of the image. A user selects an image editing mode for an image generation network from a plurality of image editing modes. The image generation network generates an output image using the input image, the mask, and the image editing mode.

公开(公告)号：US20240265505A1

公开(公告)日：2024-08-08

申请号：US18165141

申请日：2023-02-06

Applicant: ADOBE INC.

Inventor： Cusuh Ham ,  Tobias Hinz ,  Jingwan Lu ,  Krishna Kumar Singh ,  Zhifei Zhang

IPC: G06T5/00

CPC classification number: G06T5/70 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a noise image and guidance information for generating an image. A diffusion model generates an intermediate noise prediction for the image based on the noise image. A conditioning network generates noise modulation parameters. The intermediate noise prediction and the noise modulation parameters are combined to obtain a modified intermediate noise prediction. The diffusion model generates the image based on the modified intermediate noise prediction, wherein the image depicts a scene based on the guidance information.

公开(公告)号：US12014452B2

公开(公告)日：2024-06-18

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 ,  G06N3/04 ,  G06T11/20

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：US20240169500A1

公开(公告)日：2024-05-23

申请号：US18058027

申请日：2022-11-22

Applicant: ADOBE INC.

Inventor： Haitian Zheng ,  Zhe Lin ,  Jianming Zhang ,  Connelly Stuart Barnes ,  Elya Shechtman ,  Jingwan Lu ,  Qing Liu ,  Sohrab Amirghodsi ,  Yuqian Zhou ,  Scott Cohen

IPC: G06T5/00

CPC classification number: G06T5/005 ,  G06T5/003 ,  G06T2207/20081 ,  G06T2207/20104

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive an image comprising a first region that includes content and a second region to be inpainted. Noise is then added to the image to obtain a noisy image, and a plurality of intermediate output images are generated based on the noisy image using a diffusion model trained using a perceptual loss. The intermediate output images predict a final output image based on a corresponding intermediate noise level of the diffusion model. The diffusion model then generates the final output image based on the intermediate output image. The final output image includes inpainted content in the second region that is consistent with the content in the first region.

公开(公告)号：US20240135572A1

公开(公告)日：2024-04-25

申请号：US18190636

申请日：2023-03-27

Applicant: Adobe Inc.

Inventor： Krishna Kumar Singh ,  Yijun Li ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Yangtuanfeng Wang ,  Jimei Yang ,  Tobias Hinz

IPC: G06T7/70 ,  G06T7/40 ,  G06V10/44 ,  G06V10/771 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06T7/70 ,  G06T7/40 ,  G06V10/44 ,  G06V10/771 ,  G06V10/806 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/30196

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

公开(公告)号：US20240127412A1

公开(公告)日：2024-04-18

申请号：US17937708

申请日：2022-10-03

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Haitian Zheng ,  Elya Shechtman ,  Jianming Zhang ,  Jingwan Lu ,  Ning Xu ,  Qing Liu ,  Scott Cohen ,  Sohrab Amirghodsi

IPC: G06T5/00 ,  G06T7/11

CPC classification number: G06T5/005 ,  G06T7/11 ,  G06T2207/20084 ,  G06T2207/20092

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

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.

公开(公告)号：US11887216B2

公开(公告)日：2024-01-30

申请号：US17455796

申请日：2021-11-19

Applicant: ADOBE INC.

Inventor： Ratheesh Kalarot ,  Timothy M. Converse ,  Shabnam Ghadar ,  John Thomas Nack ,  Jingwan Lu ,  Elya Shechtman ,  Baldo Faieta ,  Akhilesh Kumar

IPC: G06T5/00 ,  G06T11/00 ,  G06N3/08 ,  G06V40/16

CPC classification number: G06T11/00 ,  G06N3/08 ,  G06V40/168 ,  G06V40/172

Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).

公开(公告)号：US11869125B2

公开(公告)日：2024-01-09

申请号：US17038866

申请日：2020-09-30

Applicant: Adobe Inc.

Inventor： Ajay Bedi ,  Ajay Jain ,  Jingwan Lu ,  Anugrah Prakash ,  Prasenjit Mondal ,  Sachin Soni ,  Sanjeev Tagra

IPC: G06F3/04842 ,  G06F3/04845 ,  G06T5/00 ,  G06T5/50 ,  G06T7/11 ,  G06T7/60 ,  G06T11/60

CPC classification number: G06T11/60 ,  G06F3/04842 ,  G06F3/04845 ,  G06T5/002 ,  G06T5/50 ,  G06T7/11 ,  G06T2200/24 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating a composite image comprising objects in positions from two or more different digital images. In one or more embodiments, the disclosed system receives a sequence of images and identifies objects within the sequence of images. In one example, the disclosed system determines a target position for a first object based on detecting user selection of the first object in the target position from a first image. The disclosed system can generate a fixed object image comprising the first object in the target position. The disclosed system can generate preview images comprising the fixed object image with the second object sequencing through a plurality of positions as seen in the sequence of images. Based on a second user selection of a desired preview image, the disclosed system can generate the composite image.

公开(公告)号：US11842468B2

公开(公告)日：2023-12-12

申请号：US17178681

申请日：2021-02-18

Applicant: Adobe Inc.

Inventor： Pei Wang ,  Yijun Li ,  Jingwan Lu ,  Krishna Kumar Singh

IPC: G06K9/00 ,  G06T5/50 ,  G06N3/04 ,  G06V10/75 ,  G06F18/22 ,  G06F18/24

CPC classification number: G06T5/50 ,  G06F18/22 ,  G06F18/24 ,  G06N3/04 ,  G06V10/751 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06V10/759

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize image-guided model inversion of an image classifier with a discriminator. The disclosed systems utilize a neural network image classifier to encode features of an initial image and a target image. The disclosed system also reduces a feature distance between the features of the initial image and the features of the target image at a plurality of layers of the neural network image classifier by utilizing a feature distance regularizer. Additionally, the disclosed system reduces a patch difference between image patches of the initial image and image patches of the target image by utilizing a patch-based discriminator with a patch consistency regularizer. The disclosed system then generates a synthesized digital image based on the constrained feature set and constrained image patches of the initial image.