Adobe, Inc.

Nipun Jindal

Tanya Jindal

Pramendra Rathi

Pranay Kumar

Oliver Brdiczka

Deep Sinha

Praveen Kumar Dhanuka

Aliakbar Darabi

Nirmal Kumawat

Peter Evan O'Donovan

Alexandru Vasile Costin

Razvan Cotlarciuc

Anand Khanna

Kushith Amerasinghe

Oliver Markus Michael Brdiczka

Arpit Mathur

Cristian-Catalin Buzoiu

Gabriel Boroghina

Dan-Gabriel Ghita

Siddartha Reddy Turpu

Costin-Stefan Ion

Embodiments of the present disclosure include obtaining an input video depicting a change to an image, wherein the input video has a first aspect ratio. Some embodiments compute a cost function for a frame of the input video based on a location of the change. A modified frame corresponding to the frame of the input video is generated based on the cost function. In some examples, the modified frame has a second aspect ratio different from the first aspect ratio. Then, an output video including the modified frame is generated and the output video has the second aspect ratio.

Timelapse video generation

Methods and systems disclosed herein relate generally to systems and methods for analyzing various stroke properties determined from strokes inputted by a user to generate a new glyph set for rendering type characters. A font-generating application receives, via a stroke input on a typographic layer presented on a user interface, strokes that trace a visual appearance of a glyph set comprising one or more glyphs. The font-generating application determines stroke properties for the strokes. The font-generating application constructs a new glyph set from the stroke properties. The font-generating application applies the new glyph set to render, on a user interface, one or more type characters that match a visual appearance of the new glyph set.

Font customization based on stroke properties

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a multi-modal vector and identifies a recommended font corresponding to the source font based on the multi-modal vector. For instance, in one or more embodiments, the disclosed systems receive an indication of a source font and determines font embeddings and glyph metrics embedding. Furthermore, the disclosed system generates, utilizing a multi-modal font machine-learning model, a multi-modal vector representing the source font based on the font embeddings and the glyph metrics embedding.

Generating a multi-modal vector representing a source font and identifying a recommended font utilizing a multi-modal font machine-learning model

An illustrator system accesses a multi-element document including a plurality of elements. The illustrator system selects, from the plurality of elements, a selected element. The illustrator system generates a replacement multi-element document that includes a substitute element in place of the selected element in the multi-element document, wherein the substitute element is different from the selected element. The illustrator system displays, via a user interface with the multi-element document, a preview of the replacement multi-element document providing a view of the replacement multi-element document, wherein the view of the replacement multi-element document is focused to depict the substitute element.

User interface for editing of a composite document through intelligently zoomed previews

Embodiments presented in this disclosure provide for dynamic application of user selected visual accessibility transforms onto glyphs of standard fonts so that, for instance, a user device can present textual content to a user in a form personalized by the user to be more readable. In accordance with some aspects, a user selection of a font transformation is received. A set of initial control points of an initial glyph is transposed based on the font transformation to generate a set of modified control points. A modified glyph is constructed using differential evolution based at least on the set of initial control points and the set of modified control points.

Dynamic differential evolution based control for typeface visual accessibility

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

In implementations of systems for identifying templates based on fonts, a computing device implements an identification system to receive input data describing a selection of a font included in a collection of fonts. The identification system generates an embedding that represents the font in a latent space using a machine learning model trained on training data to generate embeddings for digital templates in the latent space based on intent phrases associated with the digital templates and embeddings for fonts in the latent space based on intent phrases associated with the fonts. A digital template included in a collection of digital templates is identified based on the embedding that represents the font and an embedding that represents the digital template in the latent space. The identification system generates an indication of the digital template for display in a user interface.

Identifying templates based on fonts

Embodiments are disclosed for real time generative audio for brush and canvas interaction in digital drawing. The method may include receiving a user input and a selection of a tool for generating audio for a digital drawing interaction. The method may further include generating intermediary audio data based on the user input and the tool selection, wherein the intermediary audio data includes a pitch and a frequency. The method may further include processing, by a trained audio transformation model and through a series of one or more layers of the trained audio transformation model, the intermediary audio data. The method may further include adjusting the series of one or more layers of the trained audio transformation model to include one or more additional layers to produce an adjusted audio transformation model. The method may further include generating, by the adjusted audio transformation model, an audio sample based on the intermediary audio data.

Real time generative audio for brush and canvas interaction in digital drawing

In implementations of vector-based glyph style transfer, a style transfer system transfers a modification of a modified glyph to an additional glyph. The system identifies the modification by comparing the modified glyph to a corresponding unmodified glyph. In one or more implementations, this includes identifying similar segments of the additional glyph based on a style transfer policy, which defines conditions for transferring the modification to the additional glyph. The system transfers the modification to the additional glyph by mapping the modification to the similar segments.

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Delhi, India

Nipun Jindal