CONSTRUCTING DIGITAL DESIGN GRAPHS FOR GENERATING STRUCTURAL REPRESENTATIONS OF DIGITAL DESIGN DOCUMENTS

    公开(公告)号:US20240403557A1

    公开(公告)日:2024-12-05

    申请号:US18328286

    申请日:2023-06-02

    Applicant: Adobe Inc.

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a design representation to further construct a digital design multigraph and generate a structural representation for a digital design document from the digital design multigraph. For instance, the disclosed systems generate a design representation of a digital design document that includes design properties with multiple digital design elements. In particular, the disclosed systems construct a digital design (multi-) graph from the design representation by generating nodes to represent digital design elements and edges based on relationships between these elements. In addition, the disclosed systems generate a structural representation based on the digital design multigraph for downstream applications. For instance, downstream applications include utilizing the structural representation to select a resizing model from a plurality of resizing models and resizing a digital design document using the structural representation.

    Generating personalized digital design template recommendations

    公开(公告)号:US11989505B2

    公开(公告)日:2024-05-21

    申请号:US17938253

    申请日:2022-10-05

    Applicant: Adobe Inc.

    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.

    UTILIZING A TRANSFORMER-BASED GENERATIVE LANGUAGE MODEL TO GENERATE DIGITAL DESIGN DOCUMENT VARIATIONS

    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.

    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.

    TYPOGRAPHICALLY AWARE IMAGE GENERATION

    公开(公告)号:US20250022186A1

    公开(公告)日:2025-01-16

    申请号:US18351521

    申请日:2023-07-13

    Applicant: ADOBE INC.

    Abstract: Systems and methods for typographically aware image generation are provided. An aspect of the systems and methods includes obtaining a prompt that includes a description of a typographic characteristic of text; encoding the prompt to obtain a prompt encoding; and generating an image that includes the text with the typographic characteristic based on the prompt encoding, wherein the image is generated using an image generation network that is trained to generate images having specific typographic characteristics.

    UTILIZING MACHINE LEARNING TO SELECT RESIZING MODELS IN GENERATING RESIZED DIGITAL DESIGN DOCUMENTS

    公开(公告)号:US20240404000A1

    公开(公告)日:2024-12-05

    申请号:US18328272

    申请日:2023-06-02

    Applicant: Adobe Inc.

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a design representation to further construct a digital design multigraph and generate a structural representation for a digital design document from the digital design multigraph. For instance, the disclosed systems generate a design representation of a digital design document that includes design properties with multiple digital design elements. In particular, the disclosed systems construct a digital design (multi-) graph from the design representation by generating nodes to represent digital design elements and edges based on relationships between these elements. In addition, the disclosed systems generate a structural representation based on the digital design multigraph for downstream applications. For instance, downstream applications include utilizing the structural representation to select a resizing model from a plurality of resizing models and resizing a digital design document using the structural representation.

    GENERATING PERSONALIZED DIGITAL DESIGN TEMPLATE RECOMMENDATIONS

    公开(公告)号:US20240119230A1

    公开(公告)日:2024-04-11

    申请号:US17938253

    申请日:2022-10-05

    Applicant: Adobe Inc.

    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.

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