Adobe, Inc.

Patrick James Hebron

Christian Thomas Cantrell

Archie Samuel Bagnall

Gregory Cy Muscolino

Christopher James Gammon

Jingwan Lu

Baldo Antonio Faieta

Shabnam Ghadar

Holger Winnemoeller

Ratheesh Kalarot

Cameron Smith

Wei-An Lin

Akhilesh Kumar

Wilson Chan

Sven Olsen

Piotr Walczyszyn

Embodiments are disclosed for performing content linting in a graphic design system. A method of content linting includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, determining a placement context associated with the location in the digital canvas, identifying one or more content rules to the content based on a static analysis of the placement context, and generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas using the one or more content rules.

Content linting in graphic design documents

Embodiments are disclosed for semantically organizing a graphic design document. A method of semantically organizing a graphic design document can include obtaining a document, identifying a plurality of layers associated with the document, determining a plurality of semantic labels associated with the plurality of layers, determining a semantic layer hierarchy of the plurality of layers, and organizing the plurality of layers based at least on the semantic layer hierarchy.

Organizing a graphic design document using semantic layers

Embodiments are disclosed for creating and managing semantic layers in a graphic design system. A method of creating and managing semantic layers includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas, and automatically adding the content to a layer associated with the digital canvas based on a semantic label associated with the content.

Automatically generating semantic layers in a graphic design document

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.

Web-based digital image editing in real time utilizing a latent vector stream renderer and an image modification neural network

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Briarcliff Manor, NY, United States of America

Patrick James Hebron