Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a modified digital image depicting a transparent object utilizing a transparency properties neural network. For example, the disclosed system accesses a trimap for a source digital image depicting a transparent object. The disclosed system utilizes the trimap of the source digital image and the source digital image to generate an alpha matte and a refractive flow. Specifically, the disclosed system generates the alpha matte and refractive flow by utilizing a transparency properties neural network. Furthermore, the disclosed system generates the modified digital image depicting the transparent object (from the source digital image) within a background of a target digital image, by modifying a portion of the background of a target digital image behind the transparent object utilizing the alpha matte and the refractive flow.

Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that performs layer-aware and object-aware comments in a digital design document. For example, the disclosed systems receive a comment (that mentions an object) for a digital design document that includes one or more layers and one or more objects. Moreover, the disclosed systems generate an association between the comment, a layer of the one or more layers, and the object of the one or more objects. Further, the disclosed systems provide for display in the digital design document, an annotation on the object in the layer of the digital design document.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently identifying digital images that portray counterfeit products. The disclosed system can store key values and properties of authentic graphic elements (logos, trademarks, designs, etc.,). For example, the disclosed system can determine authentic graphic features comprising authentic shape features, authentic color features, and authentic text features corresponding with a graphic element. The disclosed system can further extract a graphic element from a digital image for comparison with the authentic graphic element. For instance, the disclosed system can extract graphic features of the graphic element, the graphic features comprising shape features, color features, and text features from the graphic element.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting changes to a point of interest between a selected version and a previous version of a digital image and providing a summary of the changes to the point of interest. For example, the disclosed system provides for display a selected version of a digital image and detects a point of interest within the selected version of the digital image. The disclosed system determines image modifications to the point of interest (e.g., tracks changes to the point of interest) to generate a summary of the image modifications. Moreover, the summary can indicate further information concerning image modifications applied to the selected point of interest, such as timestamp, editor, or author information.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that can generate contextual identifiers indicating context for frames of a video and utilize those contextual identifiers to generate translations of text corresponding to such video frames. By analyzing a digital video file, the disclosed systems can identify video frames corresponding to a scene and a term sequence corresponding to a subset of the video frames. Based on images features of the video frames corresponding to the scene, the disclosed systems can utilize a contextual neural network to generate a contextual identifier (e.g. a contextual tag) indicating context for the video frames. Based on the contextual identifier, the disclosed systems can subsequently apply a translation neural network to generate a translation of the term sequence from a source language to a target language. In some cases, the translation neural network also generates affinity scores for the translation.

Abstract: Techniques are disclosed for generating image recommendations to facilitate the sale of a product. An example methodology includes identifying a product category associated with an image of the product provided by the seller, and a product sub-category associated with the product image. The method further includes retrieving one or more images of for-sale items. The retrieval is based on a search of for-sale listings using the identified product category and the identified product sub-category. The method further includes clustering the retrieved images of for-sale items into groups, each group associated with a perspective viewpoint of the for-sale item. The method further includes providing a selected image from each group as an image recommendation. The selection is based on a value score associated with each of the images of the for-sale items. A graphical status indicating completeness of the seller's image set is updated in response to recommended images being adopted.

Abstract: Systems and techniques for a design-aware image search are described. The design-aware image search techniques described herein capture a design on an item to determine additional items with similar or matching designs. An image of the item is used to create an edge image of a design, and shape descriptors are generated describing features of the edge image. These shape descriptors are compared to shape descriptors associated with other images to locate images that have similar or matching designs as compared with the input image. The design-aware image search system may use these relationships to generate a search result with images or products having a design similar to the design on the input image.

Abstract: Methods, systems, and computer storage media for providing tool tutorials based on tutorial information that is dynamically integrated into tool tutorial shells using graphics editing system operations in a graphics editing systems. In operation, an image is received in association with a graphics editing application. Tool parameters (e.g., image-specific tool parameters) are generated based on processing the image. The tool parameters are generated for a graphics editing tool of the graphics editing application. The graphics editing tool (e.g., object removal tool or spot healing tool) can be a premium version of a simplified version of the graphics editing tool in a freemium application service. Based on the tool parameters and the image, a tool tutorial data file is generated by incorporating the tool parameters and the image into a tool tutorial shell. The tool tutorial data file can be selectively rendered in an integrated interface of the graphics editing application.

Abstract: Methods, systems, and computer storage media for providing tool tutorials based on tutorial information that is dynamically integrated into tool tutorial shells using graphics editing system operations in a graphics editing systems. In operation, an image is received in association with a graphics editing application. Tool parameters (e.g., image-specific tool parameters) are generated based on processing the image. The tool parameters are generated for a graphics editing tool of the graphics editing application. The graphics editing tool (e.g., object removal tool or spot healing tool) can be a premium version of a simplified version of the graphics editing tool in a freemium application service. Based on the tool parameters and the image, a tool tutorial data file is generated by incorporating the tool parameters and the image into a tool tutorial shell. The tool tutorial data file can be selectively rendered in an integrated interface of the graphics editing application.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a modified digital image depicting a transparent object utilizing a transparency properties neural network. For example, the disclosed system accesses a trimap for a source digital image depicting a transparent object. The disclosed system utilizes the trimap of the source digital image and the source digital image to generate an alpha matte and a refractive flow. Specifically, the disclosed system generates the alpha matte and refractive flow by utilizing a transparency properties neural network. Furthermore, the disclosed system generates the modified digital image depicting the transparent object (from the source digital image) within a background of a target digital image, by modifying a portion of the background of a target digital image behind the transparent object utilizing the alpha matte and the refractive flow.

Abstract: An image generation system generates images of objects under different lighting conditions. An image of an object and lighting conditions for an output image are received. The lighting conditions may specify, for instance, a location and/or color of one or more light sources. The image of the object is decomposed into a shading component and a reflectance component. A machine learning model takes the reflectance component and specified lighting conditions as input, and generates an output image of the object under the specified lighting conditions. In some configurations, the machine learning model may be trained on images of objects labeled with object classes, and the output image may be generated by also providing an object class of the object in the image as input to the machine learning model.

Abstract: Some implementations include methods for communicating features of images to visually impaired users. An image to be displayed on a touch sensitive screen of a computing device may include one or more objects. Each of the one or more objects may be associated with a bounding box. A contact with the image may be detected via the touch sensitive screen. The contact may be determined to be within a bounding box associated with a first object of the one or more objects. Responsive to detecting the contact to be within the bounding box associated with the first object, a caption of the first object may be caused to become audible and the touch sensitive screen may be caused to vibrate based on a vibration pattern unique to the first object.

Abstract: Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: A system identifies a video comprising frames associated with content tags. The system detects features for each frame of the video. The system identifies, based on the detected features, scenes of the video. The system determines, for each frame for each scene, a frame score that indicates a number of content tags that match the other frames within the scene. The system selects, for each scene, a set of key frames that represent the scene based on the determined frame scores. The system receives a search query comprising a keyword. The system generates, for display, search results responsive to the search query including a dynamic preview of the video. The dynamic preview comprises an arrangement of frames of the video corresponding to each scene of the video. Each of the arrangement of frames is selected from the selected set of key frames representing the respective scene of the video.

Abstract: A media edit point selection process can include a media editing software application programmatically converting speech to text and storing a timestamp-to-text map. The map correlates text corresponding to speech extracted from an audio track for the media clip to timestamps for the media clip. The timestamps correspond to words and some gaps in the speech from the audio track. The probability of identified gaps corresponding to a grammatical pause by the speaker is determined using the timestamp-to-text map and a semantic model. Potential edit points corresponding to grammatical pauses in the speech are stored for display or for additional use by the media editing software application. Text can optionally be displayed to a user during media editing.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating a composite image comprising objects in positions from two or more different digital images. In one or more embodiments, the disclosed system receives a sequence of images and identifies objects within the sequence of images. In one example, the disclosed system determines a target position for a first object based on detecting user selection of the first object in the target position from a first image. The disclosed system can generate a fixed object image comprising the first object in the target position. The disclosed system can generate preview images comprising the fixed object image with the second object sequencing through a plurality of positions as seen in the sequence of images. Based on a second user selection of a desired preview image, the disclosed system can generate the composite image.

Abstract: Embodiments of the present invention are directed towards providing contextual tags for an image based on a contextual analysis of associated images captured in the same environment as the image. To determine contextual tags, content tags can be determined for images. The determined content tags can be associated with categories based on a contextual classification of the content tags. These associated content tags can then be designated as contextual tags for a respective category. To associate these contextual tags with the images, the images can be iterated through based on how the images relate to the contextual tags. For instance, when an image is associated with a category, the contextual tags classified into that category can be assigned to that image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting changes to a point of interest between a selected version and a previous version of a digital image and providing a summary of the changes to the point of interest. For example, the disclosed system provides for display a selected version of a digital image and detects a point of interest within the selected version of the digital image. The disclosed system determines image modifications to the point of interest (e.g., tracks changes to the point of interest) to generate a summary of the image modifications. Moreover, the summary can indicate further information concerning image modifications applied to the selected point of interest, such as timestamp, editor, or author information.