公开(公告)号：US20240135509A1

公开(公告)日：2024-04-25

申请号：US18190500

申请日：2023-03-27

Applicant: Adobe Inc.

Inventor： Qing Liu ,  Jianming Zhang ,  Krishna Kumar Singh ,  Scott Cohen ,  Zhe Lin

IPC: G06T5/00 ,  G06T7/11 ,  G06T11/60 ,  G06V10/764 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06T5/005 ,  G06T5/002 ,  G06T7/11 ,  G06T11/60 ,  G06V10/764 ,  G06V10/82 ,  G06V20/70 ,  G06T2200/24 ,  G06T2207/20021 ,  G06T2207/20084

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：US20240127411A1

公开(公告)日：2024-04-18

申请号：US17937706

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 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084

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：US20240127410A1

公开(公告)日：2024-04-18

申请号：US17937695

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

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：US11880977B2

公开(公告)日：2024-01-23

申请号：US17313158

申请日：2021-05-06

Applicant: Adobe Inc.

Inventor： Brian Lynn Price ,  Scott Cohen ,  Marco Forte ,  Ning Xu

IPC: G06T7/11 ,  G06T7/136 ,  G06T7/194 ,  G06T7/90 ,  G06T3/40 ,  G06N3/088 ,  G06N3/045 ,  G06N3/02 ,  G06N20/00

CPC classification number: G06T7/11 ,  G06N3/045 ,  G06N3/088 ,  G06T3/40 ,  G06T7/136 ,  G06T7/194 ,  G06T7/90 ,  G06N3/02 ,  G06N20/00 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20096 ,  G06T2207/20104

Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.

公开(公告)号：US20240005574A1

公开(公告)日：2024-01-04

申请号：US17810392

申请日：2022-07-01

Applicant: Adobe Inc.

Inventor： Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Darshan Prasad ,  Zhihong Ding

IPC: G06T11/40 ,  G06T5/00 ,  G06T7/12

CPC classification number: G06T11/40 ,  G06T5/005 ,  G06T7/12 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

公开(公告)号：US20230368339A1

公开(公告)日：2023-11-16

申请号：US17663317

申请日：2022-05-13

Applicant: Adobe Inc.

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

IPC: G06T5/00 ,  G06T7/11 ,  G06N3/04

CPC classification number: G06T5/005 ,  G06T7/11 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing class-specific cascaded modulation inpainting neural network. For example, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network that includes cascaded modulation decoder layers to generate replacement pixels portraying a particular target object class. To illustrate, in response to user selection of a replacement region and target object class, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network corresponding to the target object class to generate an inpainted digital image that portrays an instance of the target object class within the replacement region. Moreover, in one or more embodiments the disclosed systems train class-specific cascaded modulation inpainting neural networks corresponding to a variety of target object classes, such as a sky object class, a water object class, a ground object class, or a human object class.

公开(公告)号：US11631234B2

公开(公告)日：2023-04-18

申请号：US16518810

申请日：2019-07-22

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Zhe Lin ,  Mingyang Ling

IPC: G06K9/00 ,  G06V10/20 ,  G06F16/535 ,  G06K9/62

Abstract: The present disclosure relates to an object selection system that accurately detects and optionally automatically selects user-requested objects (e.g., query objects) in digital images. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analyzing the object class of a query object. In particular, the object selection system can identify both known object classes as well as objects corresponding to unknown object classes.

公开(公告)号：US20220414142A1

公开(公告)日：2022-12-29

申请号：US17929206

申请日：2022-09-01

Applicant: Adobe Inc.

Inventor： Walter Wei Tuh Chang ,  Khoi Pham ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding

IPC: G06F16/532 ,  G06T11/60 ,  G06K9/62 ,  G06F40/279 ,  G06F40/247 ,  G06N20/00 ,  G06F16/583 ,  G06F16/242 ,  G06F16/28 ,  G06F16/538 ,  G06F40/30 ,  G06F16/33

Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image based on natural language-based inputs. For instance, the object selection system can utilize natural language processing tools to detect objects and their corresponding relationships within natural language object selection queries. For example, the object selection system can determine alternative object terms for unrecognized objects in a natural language object selection query. As another example, the object selection system can determine multiple types of relationships between objects in a natural language object selection query and utilize different object relationship models to select the requested query object.

公开(公告)号：US20220392046A1

公开(公告)日：2022-12-08

申请号：US17819845

申请日：2022-08-15

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Zhe Lin ,  Mingyang Ling

IPC: G06T7/00 ,  G06T7/70 ,  G06T7/90 ,  G06T11/60 ,  G06F16/583 ,  G06F40/205

Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects target instances of user-requested objects (e.g., a query object instance) in a digital image. In one or more embodiments, the object selection system can analyze one or more user inputs to determine an optimal object attribute detection model from multiple specialized and generalized object attribute models. Additionally, the object selection system can utilize the selected object attribute model to detect and select one or more target instances of a query object in an image, where the image includes multiple instances of the query object.

公开(公告)号：US11468550B2

公开(公告)日：2022-10-11

申请号：US16518850

申请日：2019-07-22

Applicant: Adobe Inc.

Inventor： Scott Cohen ,  Zhe Lin ,  Mingyang Ling

IPC: G06T7/00 ,  G06T7/70 ,  G06T7/90 ,  G06T11/60 ,  G06F16/583 ,  G06F40/205

Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects target instances of user-requested objects (e.g., a query object instance) in a digital image. In one or more embodiments, the object selection system can analyze one or more user inputs to determine an optimal object attribute detection model from multiple specialized and generalized object attribute models. Additionally, the object selection system can utilize the selected object attribute model to detect and select one or more target instances of a query object in an image, where the image includes multiple instances of the query object.