Adobe, Inc.

Alan Lee Erickson

Sarah Kong

I-Ming Pao

Yuyan Song

Bradee Rae Evans

Hailin Jin

Gregg Darryl Wilensky

Zhaowen Wang

Hyunghwan Byun

Betty Leong

Yukie Takahashi

Nathan Aaron Carr

Kalyan Krishna Sunkavalli

Aseem Agarwala

Russell T Williams

Habib Khalfallah

Seth Shaw

Jan Stoeckmann

Manish Kumar

Guotong Feng

Heather M Dolan

Meredith L Stotzner

Michael J Orts

Scott David Cohen

Jue Wang

Jianming Zhang

Jen-Chan Jeff Chien

Douglas E Meisner

Sarah Stuckey

Troy A Gaul

Timothy N Wright

Christopher P Hondl

Meredith Payne Stotzner

Frederick Mandia

Doug Ahmann

Quynn Megan Le

Dana Michelle Jefferson

Davis Taylor Brown

Systems and methods for non-destructive image editing are described. Embodiments are configured to obtain, via a document editor user interface, a selection input identifying a portion of a first image displayed in the document editor user interface. According to some aspects, the first image is part of a first layer of a multilayer document. Embodiments are further configured to: obtain an image generation text prompt; generate, using an image generation network, a second image based on the first image and the image generation text prompt; and present, via a multilayer window of the document editor user interface, a first element representing the first layer of the multilayer document and a second element representing a second layer of the multilayer document. The first element includes the first image and the second element includes the second image and a mask corresponding to the portion of the first image.

Non-destructive generative image editing

The present disclosure provides systems and methods for image editing. Embodiments of the present disclosure provide an image editing system for perform image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. The original image and the replacement image (e.g., the image including a desirable object or region) include layers of masks. A sky from the replacement image may replace the sky of the image to produce an aesthetically pleasing composite image.

Sky replacement

Systems and methods for image editing are described. Embodiments of the present disclosure provide an image editing system for performing image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. According to some embodiments described herein, thumbnails, region location information, and image metadata from multiple preset images can be stored together and loaded for presentation and selection of a preset image for replacing a region of an image. Once a preset image (e.g., an image with a replacement sky) is selected, a high-resolution version of the image can be loaded and used to generate a composite image.

Sky replacement preset loading

Techniques and systems for component-based digital image synchronization are described. In one example, a digital image is stored as a composite file with a number of separately compressed individual components. Indicators are generated during an editing process of a digital image. The indicators are usable to correlate altered portions of the digital image with compressed components of the composite digital image file. The altered components are then communicated to a synchronization service for synchronization with a remote representation of the digital image file.

Component-based digital image synchronization

Systems and techniques for automatic object replacement in an image include receiving an original image and a preferred image. The original image is automatically segmented into an original image foreground region and an original image object region. The preferred image is automatically segmented into a preferred image foreground region and a preferred image object region. A composite image is automatically composed by replacing the original image object region with the preferred image object region such that the composite image includes the original image foreground region and the preferred image object region. An attribute of the composite image is automatically adjusted.

Automatic object replacement in an image

Embodiments of the present invention are directed at providing a font similarity system. In one embodiment, a new font is detected on a computing device. In response to the detection of the new font, a pre-computed font list is checked to determine whether the new font is included therein. The pre-computed font list including feature representations, generated independently of the computing device, for corresponding fonts. In response to a determination that the new font is absent from the pre-computed font list, a feature representation for the new font is generated. The generated feature representation capable of being utilized for a similarity analysis of the new font. The feature representation is then stored in a supplemental font list to enable identification of one or more fonts installed on the computing device that are similar to the new font. Other embodiments may be described and/or claimed.

Dynamic font similarity

Systems and techniques for identification of fonts include receiving a selection of an area of an image including text, where the selection is received from within an application. The selected area of the image is input to a font matching module within the application. The font matching module identifies one or more fonts similar to the text in the selected area using a convolutional neural network. The one or more fonts similar to the text are displayed within the application and the selection and use of the one or more fonts is enabled within the application.

Identification of fonts in an application

Embodiments of the present invention are directed at providing a font similarity preview for non-resident fonts. In one embodiment, a font is selected on a computing device. In response to the selection of the font, a pre-computed font list is checked to determine what fonts are similar to the selected font. In response to a determination that similar fonts are not local to the computing device, a non-resident font list is sent to a font vendor. The font vendor sends back previews of the non-resident fonts based on entitlement information of a user. Further, a full non-resident font can be synced to the computing device. Other embodiments may be described and/or claimed.

Non-resident font preview

A mesh is a collection of multiple shapes referred to as elements, each of which can share an edge with one or more other elements of the mesh. The mesh is presented to the user on a display, and the user identifies a new element to be added to the mesh. User input is received to manipulate the new element (e.g., move the new element around the display). As the new element is manipulated, various conditions are applied to determine edges of elements existing in the mesh that the new element can be snapped to. Snapping a new element to an edge of an existing element in the mesh refers to adding the new element to the mesh so that the new element and the existing element share the edge. Indications of the edges of existing elements to which the new element can be snapped are provided to the user.

User interface facilitating mesh generation

In various embodiments, methods and systems, for automatically generating guides, are provided. A user can generate guides based on content zones of a wireframe layout or visual element layout. A selection of a content zone having a shape is received. A selection of a guide creation action to generate a grid structure encompassing the shape of the content zone is received. A determination of a boundary set for the shape is made. The boundary set includes a top boundary, a bottom boundary, a left boundary, and a right boundary. The boundary set is based on a data (e.g., vector, pixel, text) perimeter of the shape. The grid structure is automatically generated based on the shape. The grid structure comprises a top horizontal guide, a bottom horizontal guide, a left vertical guide, and right vertical guide. The grid structure is located adjacent to portions of the boundary set of the shape.

Automated generation of guides based on content zone shapes

In some embodiments, an image editing application stores, based on a first selection input, a selection state that identifies a first image portion of a target image as included in a preview image displayed in a mask-based editing interface of the image editing application. An edit to the preview image generated from the selected first image portion is applied in the mask-based editing interface. The image editing application also updates an edit state that tracks the edit applied to the preview image. The image editing application modifies, based on a second selection input received via the mask-based editing interface, the selection state to include a second image portion in the preview image. The edit state is maintained with the applied edit concurrently with modifying the selection state. The image editing application applies the edit to the modified preview image in the mask-based editing interface.

Editing dynamically selected portions of target images in a mask-based editing interface

Dynamic motion path blur techniques are described. In one or more implementations, paths may be specified to constrain a motion blur effect to be applied to a single image. A variety of different techniques may be employed as part of the motion blur effects, including use of curved blur kernel shapes, use of a mesh representation of blur kernel parameter fields to support real time output of the motion blur effect to an image, use of flash effects, blur kernel positioning to support centered or directional blurring, tapered exposure modeling, and null paths.

Dynamic motion path blur user interface

Dynamic motion path blur kernel

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Highlands Ranch, CO, United States of America

Alan Lee Erickson