Adobe, Inc.

Anuradha

Subham Gupta

Arnab Sil

Ankur Murarka

Ashutosh Dwivedi

Mustansir Bartanwala

Shatrunjay Pathare

Sneha Agarwal

In implementations of systems for generating and propagating personal masking edits, a computing device implements a mask system to detect a face of a person depicted in a digital image displayed in a user interface of an application for editing digital content. The mask system determines an identifier for the person based on an identifier for the face. Edit data is received describing properties of an editing operation and a type of mask used to modify a particular portion of the person depicted in the digital image. The mask system edits an additional digital image identified based on the identifier of the person using the type of mask and the properties of the editing operation to modify the particular portion of the person as depicted in the additional digital image.

Generating and propagating personal masking edits

In implementations of systems for generating and applying editing presets, a computing device implements a preset system to detect objects depicted in a digital image that is displayed in a user interface of an application for editing digital content. Input data is received describing an edited region of the digital image and properties of an editing operation performed in the edited region. The preset system identifies a particular detected object of the detected objects based on a bounding box of the particular detected object and an area of the edited region. An additional digital image is edited by applying the properties of the editing operation to a detected object that is depicted in the additional digital image based on a classification of the detected object and a classification of the particular detected object.

Generating and applying editing presets

Embodiments are disclosed for determining scene-based editing recommendations for video content. A method of determining scene-based editing recommendations for video content includes receiving an input video comprising video content, dividing the input video into the plurality of scenes based on the video content, identifying a representative frame for each scene, determining a plurality of editing settings for each representative frame, determining editing settings for each scene based on an effectiveness score, and generating an output video using the input video and the editing settings for each scene.

Scene-based edit suggestions for videos

The present disclosure describes systems, non-transitory computer-readable media, and methods for generating object-specific-preset edits to be later applied to other digital images depicting a same object type or applying a previously generated object-specific-preset edit to an object of the same object type within a target digital image. For example, in some cases, the disclosed systems generate an object-specific-preset edit by determining a region of a particular localized edit in an edited digital image, identifying an edited object corresponding to the localized edit, and storing in a digital-image-editing document an object tag for the edited object and instructions for the localized edit. In certain implementations, the disclosed systems further apply such an object-specific-preset edit to a target object in a target digital image by determining transformed-positioning parameters for a localized edit from the object-specific-preset edit to the target object.

Content-specific-preset edits for digital images

Embodiments provide systems, methods, and non-transitory computer storage media for providing search result images based on associations of keywords and depth-levels of an image. In embodiments, depth-levels of an image are identified using depth-map information of the image to identify depth-segments of the image. The depth-segments are analyzed to determine keywords associated with each depth-segment based on objects, features, or content in each depth-segment. An image depth-level data structure is generated by matching keywords generated for the entire image with the keywords at each depth-level and assigning the depth-level to the keyword in the image depth-level data structure for the entire image. The image depth-level data structure may be queried for images that contain keywords and depth-level information that match the keywords and depth-level information specified in a search query.

Methods and systems for depth-aware image searching

Methods and systems are provided for generating auto-complete image suggestions. In embodiments described herein, a user image having an edit state is obtained. An edit state can indicate any edits applied by the user to the user image. For the user image, an auto-complete image suggestion is generated. The auto-complete image suggestion includes a representation of the user image with the user-applied edits as well as a set of supplemental edits. Such supplemental edits can be determined from a pre-edited image identified as similar to the user image.

Auto-complete image suggestions for image editing

Techniques are disclosed for generating tutorial recommendations to users of image editing applications, based on image content. A methodology implementing the techniques according to an embodiment includes using neural networks configured to determine subject matter of a user provided image and to identify objects in the image. The method also includes selecting one or more proposed tutorials from a database of tutorials. The database is indexed by tutorial subject matter and tutorial object content, and the selection is based on a matching of the determined subject matter to the tutorial subject matter and a matching of the identified objects to the tutorial object content. The method further includes calculating an effectiveness score associated with each of the proposed tutorials, the effectiveness score based on application of the proposed tutorial to the image. The method further includes sorting the proposed tutorials for recommendation to the user based on the effectiveness scores.

Content-aware tutorial recommendation

In implementations of content-adaptive guided tutorial generation, a computing device implements a guided tutorial generation module of an image-editing application that generates a content-adaptive guided tutorial based on input content. The content-adaptive guided tutorial provides instructions on how to interact with image-editing controls of the image-editing application, where the editing controls are selectable to initiate modifications to input parameters of the input content. The guided tutorial generation module receives the input content, and creates copy content that replicates content parameters of the input content as modifiable content parameters. The guided tutorial generation module can compute adjustment values for the modifiable content parameters that are selectable to initiate alterations to the copy content, and generates the content-adaptive guided tutorial to indicate how to interact with the editing controls to alter the content parameters based on the adjustment values to produce altered output content.

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Anuradha