Adobe, Inc.

Peter Evan O'Donovan

Satish Shankar

Adam Portilla

Zhaowen Wang

Baldo Antonio Faieta

Ajinkya Gorakhnath Kale

Jose Ignacio Echevarria Vallespi

Saeid Motiian

Pranav Vineet Aggarwal

Oliver Brdiczka

Midhun Harikumar

Alex Charles Filipkowski

Nipun Jindal

Ritiz Tambi

Venkata Naveen Kumar Yadav Marri

Razvan Cotlarciuc

Abhishek Gulati

Oliver Markus Michael Brdiczka

Siddartha Reddy Turpu

Costin-Stefan Ion

Zegi Gu

Sukriti Verma

David Bourgin

Gregory St Pierre

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.

Utilizing machine learning to select resizing models in generating resized digital design documents

Embodiments are disclosed for machine learning-based generation of recommended layouts. The method includes receiving a set of design elements for performing generative layout recommendation. A number of each type of design element from the set of design elements is determined. A set of recommended layouts are generated using a trained generative layout model and the number and type of design elements. The set of recommended layouts are output.

Machine learning-based layout generation

The present disclosure is directed toward systems and methods for retargeting a user's input digital design based on a selected template digital design. For example, in response to the user's selection of a template digital design, one or more embodiments described herein change various design features of the user's input digital design to match corresponding design features in the selected template digital design. One or more embodiments described herein also provide template digital designs to the user for use in retargeting after a two-step selection process that ensures the provided template digital designs are compatible with the user's input digital design.

Modifying a graphic design to match the style of an input design

Font recommendation techniques are described that provide recommendations of fonts based on a variety of factors, automatically and without user intervention in real time. This is performable in a variety of ways by addressing a wide range of considerations as part of machine learning, examples of which include context, popularity, similarity, customization, and topic compatibility.

Font recommendation

Embodiments of the technology described herein, are an intelligent system that aims to expedite a text design process by providing text design predictions interactively. The system works with a typical text design scenario comprising a background image and one or more text strings as input. In the design scenario, the text string is to be placed on top of the background. The textual design agent may include a location recommendation model that recommends a location on the background image to place the text. The textual design agent may also include a font recommendation model, a size recommendation model, and a color recommendation model. The output of these four models may be combined to generate draft designs that are evaluated as a whole (combination of color, font, and size) for the best designs. The top designs may be output to the user.

Textual design agent

Responsive grid layouts for graphic design are described. In one example, a system of a digital medium environment is configured to generate responsive grid layouts for graphic design. The system includes a layout module implemented at least partially in hardware of the computing device to generate a plurality of grid layouts each having at least one cell configured to be mapped to at least one design element of digital content. The system also includes a scoring module implemented at least partially in hardware of a computing device to assign scores to the plurality of grid layouts based on aesthetic criteria and an output module implemented at least partially in hardware of a computing device to output at least one grid layout of the plurality of grid layouts for recommendation to a user based on the scores.

Responsive grid layouts for graphic design

The present disclosure is directed toward systems and methods for automatically generating combinations of styling values for application to a digital design. Each of the generated styling value combinations includes styling values selected from one or more probabilistically distributed sets of styling values. One or more embodiments described herein utilize a scoring system to ensure that, when applied to the digital design, the styling values included in the probabilistically distributed sets of styling values will make the digital design look professional and aesthetically pleasing.

Procedurally generating sets of probabilistically distributed styling attributes for a digital design

The present disclosure is directed toward systems and methods for determining the optimal placement, color, and size of an overlay in a digital design. For example, in response to determining that a change to an underlying image of a digital design has caused a loss of readability and/or aesthetic value in the digital design due to the placement of the overlay over the now changed underlying image, one or more embodiments described herein utilize a scoring system to identify optimal placement, color, and size for the overlay in relation to the underlying image in the digital design.

Positioning text in digital designs based on an underlying image

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Seattle, WA, United States of America

Peter Evan O'Donovan