公开(公告)号：US20240135611A1

公开(公告)日：2024-04-25

申请号：US18188671

申请日：2023-03-23

Applicant: ADOBE INC.

Inventor： Alexandru Vasile Costin ,  Oliver Brdiczka ,  Aliakbar Darabi ,  Davis Taylor Brown ,  David Davenport Bourgin

IPC: G06T11/60 ,  G06T3/40 ,  G06T5/50 ,  G06V10/82

CPC classification number: G06T11/60 ,  G06T3/40 ,  G06T5/50 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20221

Abstract: One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining an original image, a scene graph describing elements of the original image, and a description of a modification to the original image. The one or more aspects further include updating the scene graph based on the description of the modification. The one or more aspects further include generating a modified image using an image generation neural network based on the updated scene graph, wherein the modified image incorporates content based on the original image and the description of the modification.

公开(公告)号：US20230305690A1

公开(公告)日：2023-09-28

申请号：US18313529

申请日：2023-05-08

Applicant: Adobe Inc.

Inventor： Oliver Brdiczka ,  Alexandru Vasile Costin ,  Ionut Mironica ,  Vlad-Constantin Lungu-Stan

IPC: G06F3/04845 ,  G06F40/106 ,  G06F40/284 ,  G06T7/60

CPC classification number: G06F3/04845 ,  G06F40/106 ,  G06F40/284 ,  G06T7/60

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a design language model and a generative language model to generate digital design documents with design variations. In particular embodiments, the disclosed systems implement the design language model to tokenize the design of a document into a sequence of language tokens. For example, the disclosed systems tokenize visual elements and a layout of the document—in addition to optional user-added content. The generative language model utilizes the sequence of language tokens to predict a next language token representing a suggested design variation. Based on the predicted language token, the disclosed systems generate a modified digital design document visually portraying the suggested design variation. Further, in one or more embodiments, the disclosed systems perform iterative refinements to the modified digital design document.

公开(公告)号：US20240273285A1

公开(公告)日：2024-08-15

申请号：US18638275

申请日：2024-04-17

Applicant: Adobe Inc.

Inventor： Anand Khanna ,  Oliver Brdiczka ,  Alexandru Vasile Costin

IPC: G06F40/186 ,  G06F40/30 ,  G06N3/08

CPC classification number: G06F40/186 ,  G06F40/30 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides to a user a subset of digital design templates as recommendations based on a creative segment classification and template classifications. For instance, in one or more embodiments, the disclosed systems generate the creative segment classification for the user and determines geo-seasonal intent data. Furthermore, the disclosed system generates template classifications using a machine learning model based on geo-seasonality and creative intent. In doing so, the disclosed system identifies a subset of digital design templates based on the template classifications, geo-seasonal intent data, and the creative segment classification of the user.

公开(公告)号：US20240127577A1

公开(公告)日：2024-04-18

申请号：US17965291

申请日：2022-10-13

Applicant: Adobe Inc.

Inventor： Vlad-Constantin Lungu-Stan ,  Ionut Mironica ,  Oliver Brdiczka ,  Alexandru Vasile Costin

IPC: G06V10/74 ,  G06T11/60

CPC classification number: G06V10/761 ,  G06T11/60

Abstract: In implementations of systems for generating templates using structure-based matching, a computing device implements a template system to receive input data describing a set of digital design elements. The template system represents the input data as a sentence in a design structure language that describes structural relationships between design elements included in the set of digital design elements. An input template embedding is generated based on the sentence in the design structure language. The template system generates a digital template that includes the set of digital design elements for display in a user interface based on the input template embedding.

公开(公告)号：US11907839B2

公开(公告)日：2024-02-20

申请号：US17468511

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Kevin Wampler ,  Jingwan Lu ,  Jakub Fiser ,  Elya Shechtman ,  Aliakbar Darabi ,  Alexandru Vasile Costin

IPC: G06N3/08 ,  G06F3/04845 ,  G06T11/60 ,  G06T3/40 ,  G06T3/00 ,  G06F3/04847 ,  G06N20/20 ,  G06T5/00 ,  G06T5/20 ,  G06T11/00 ,  G06F18/40 ,  G06F18/211 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F3/04845 ,  G06F3/04847 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2163 ,  G06F18/40 ,  G06N3/045 ,  G06N20/20 ,  G06T3/0006 ,  G06T3/0093 ,  G06T3/40 ,  G06T3/4038 ,  G06T3/4046 ,  G06T5/005 ,  G06T5/20 ,  G06T11/001 ,  G06T11/60 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2210/22

Abstract: Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.

公开(公告)号：US20230237251A1

公开(公告)日：2023-07-27

申请号：US17583818

申请日：2022-01-25

Applicant: Adobe Inc.

Inventor： Oliver Brdiczka ,  Sanat Sharma ,  Jayant Kumar ,  Alexandru Vasile Costin ,  Aliakbar Darabi ,  Kushith Amerasinghe

IPC: G06F40/166 ,  G06F40/106 ,  G06V30/413 ,  G06F16/58 ,  G06F16/38

CPC classification number: G06F40/166 ,  G06F40/106 ,  G06V30/413 ,  G06F16/5866 ,  G06F16/38

Abstract: An illustrator system accesses a multi-element document, the multi-element document including a plurality of elements. The illustrator system determines, for each of the plurality of elements, an element-specific topic distribution comprising a ranked list of topics. The illustrator system creates a first aggregated topic distribution from the determined element-specific topic distributions. The illustrator system determines a global intent for the multi-element document, the global intent including one or more terms from the first aggregated topic distribution. The illustrator system queries a database using the global intent to retrieve a substitute element. The illustrator system generates a replacement multi-element document that includes a substitute element in place of an element in the multi-element document The at least one substitute element is different from the element in the displayed multi-element document.

公开(公告)号：US20220122307A1

公开(公告)日：2022-04-21

申请号：US17468511

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Kevin Wampler ,  Jingwan Lu ,  Jakub Fiser ,  Elya Shechtman ,  Aliakbar Darabi ,  Alexandru Vasile Costin

IPC: G06T11/60 ,  G06T3/00 ,  G06T3/40

Abstract: Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.