Adobe, Inc.

Sameer Bhatt

Abhishek Shah

Saurabh Gupta

Mayank Kumar

Nitesh Yadav

Sunil Bannur

Gaurav Priyadarshi

Saurabh Mehta

Jingwan Lu

Matthew David Fisher

Richard Zhang

Nishant Kumar

Vikas Sharma

Ramesh Srinivasaraghavan

Thomas Lane

Ramanarayanan Krishnaiyer

Sreenivas Ramaswamy

Aanchal Somani

Vishu Goyal

Shantanu Agarwal

Vikram Sethi

Sparsh Kumar Sinha

Rupali Arora

Rishub Garg

Anuradha Yadav

Pulkit Jindal

Puikit Jindal

Sreenivas Ramaswarmy

Sonali Gupta

Techniques for generating a digital collage using digital images are described. An image editing system obtains a set of digital images and a collage template to be used for generating a digital collage. The image editing system calculates different arrangement permutations for placing the digital images into digital frames of the collage template, and identifies a region of interest in each of the digital images. The image editing system fits the digital images into the digital frames for each of the arrangement permutations, and calculates a permutation error value for each arrangement permutation based on proportions of the regions of interest for each of the digital images that fit into respective digital frames. The image editing system selects an arrangement permutation with an optimal permutation error value, and uses the selected arrangement permutation to arrange the set of digital images in the collage template to generate the digital collage.

Generating a digital collage using digital images

Methods and systems are provided for an intelligent auto-fill process capable of smartly filling objects on a canvas using selected context-based images. Content related to the canvas is analyzed to determine context tags related to the canvas. The context tags are used to generate a media list comprised of one or more context-based image. The media list is used to automatically fill at least one object on the canvas such that a relevant part of the context-based image is visible. In this way, objects on a canvas can be automatically filled with images related to the context of the canvas.

Intelligent context-based image recommendations

In implementations of adding color to digital images, an image colorization system can display a digital image to be color adjusted in an image editing interface and convert pixel content of the digital image to a LAB color space. The image colorization system can determine a lightness value (L) in the LAB color space of the pixel content of the digital image at a specified point on the digital image, and determine colors representable in an RGB color space based on combinations of A,B value pairs with the lightness value (L) in the LAB color space. The image colorization system can then determine a range of the colors for display in a color gamut in the image editing interface, the range of the colors corresponding to the A,B value pairs with the lightness value (L) of the pixel content at the specified point on the digital image.

Adding color to digital images

A video editing application generates video-filled text based on context-sensitive video segments. For example, the video editing application receives a text selection including multiple characters. A text selection context that identifies a characteristic of the text selection is determined, the context including a category of the text selection and a tag identifying an entity associated with the text selection. Scores are computed for multiple video segments, each score indicating a match between an attribute of the respective video segment and the text selection context. Video segments with attributes that match the context are selected, based on a comparison of each score to a threshold. The video editing application generates a composite video that includes a combination of a selected video segment and a character from the text selection, the combination including an outline of the character and the selected video segment.

Contextualized video segment selection for video-filled text

A digital medium environment is described to improve moving graphical user interface objects using predictive drop zones that are generated based on user input operations. In one example, a user input processing system receives user input, such as selection and movement of a graphical object. The user input processing system monitors the user input to determine velocity, acceleration, location, and direction of the graphical object as moved by the user input. From the monitoring, the user input processing system continuously determines a location for a predicted drop zone in the user interface that represents an ending point for the movement. The predicted drop zone is then rendered on the user interface in real-time until termination of the input, at which point the user input processing system moves the graphical object to the location of the predicted drop zone, rather than to a pointing device location.

Drop zone prediction for user input operations

This application relates generally to computer-implemented methods and systems for computer graphics processing. Specifically, the application involves automatically selecting and blending images based on image aesthetic scores. Computer systems are provided that may blend an automatically selected foreground image with an automatically selected background image to produce an editable photo creation.

Automatically selecting and superimposing images for aesthetically pleasing photo creations

Techniques for automatic creation of media collages are described. In one or more implementations, unwanted frames are identified and removed from items of media content. A media score is then determined for items of media content based on characteristics of an appearance of the items within a plurality of collage templates. A template score is determined for each collage template of the plurality of collage templates by combining the media scores for each media item of the plurality of media items included in a collage template. At least one of the plurality of collage templates is selected based on determined template scores. Then, at least one media collage is outputted based on the selected collage templates.

Automatic creation of media collages

Techniques for advertisement placement prioritization are described. In one or more implementations, a likely line of sight of a viewer of an event to a focus point of the event is determined. Then, a relative location of one or more display devices is identified in relation to the likely line of sight of the viewer. In addition, a priority level is assigned to respective display devices based on corresponding relative locations in relation to the likely line of sight. Further, various demographics and characteristics of audience members can be obtained to determine context-sensitive media content associated with the audience members to present via relatively higher priority display devices. Also, performances of event participants can be determined based on commentary analysis and used to determine context-sensitive media content associated with the event participants to present via the higher priority ad boards.

Advertisement placement prioritization

A computer-implemented method of positioning a video frame within a collage cell includes, for a given one of a plurality of video frames, generating a polygon encompassing a portion of the respective video frame containing at least one visual element. The polygon has a center position corresponding to a first point within the respective video frame. The center position of the polygon of a given frame is then changed to a new center position based at least in part on an average center position of polygons encompassing portions of at least two consecutive video frames containing the visual element(s). The new center position corresponds to a second point within the given video frame. Next, a cropped portion of the given video frame encompassed by the polygon having the new center position is generated and displayed within a collage cell of a graphical user interface.

Automatic positioning of a video frame in a collage cell

Embodiments include a method comprising providing a design interface that depicts a plurality of interface elements of an application under development, each element defined in source code accessible by the application development environment in a declarative markup language. The method can comprise receiving, through the design interface, data representing a selection of a plurality of the interface elements, identifying the declarative expressions corresponding to the selected interface elements, defining a new declarative expression, and generating at least one code segment using the identified declarative expressions. The method an comprise storing the generated code segment in a computer readable medium, with the stored code segment associated with the new declarative expression, so that the new declarative expression can be inserted into the source code in place of the identified declarative expressions and/or can used in a second application under development.

Methods and systems for visual code refactoring

An application development tool can support sampling of data from one or more providers of data that will be queried by the application under development at runtime. The sampling results can be used to provide hints or suggestions at development time so that a developer defining code segments that make reference to data resulting from such queries can produce code segments having the proper context to reference the data of interest. The sampling results can comprise a plurality of parameters and the application development tool can provide a user interface whereby a user can indicate custom type declarations for the returned parameters and/or identify a subset of the parameters returned by a query as being of interest. The custom type declarations and/or identified subset can be used to facilitate more meaningful hinting or suggestion by the application development tool.

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Noida, India

Sameer Bhatt