Abstract: Techniques and apparatuses for highlighting media through weighting of people or contexts are described. This document describes techniques that allow a user to quickly and easily highlight media, such as through generating a highlight reel. The techniques also enable selection of context and person weightings by which to tailor highlight reels.

Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.

Abstract: Techniques and apparatuses for highlighting media through weighting of people or contexts are described. This document describes techniques that allow a user to quickly and easily highlight media, such as through generating a highlight reel. The techniques also enable selection of context and person weightings by which to tailor highlight reels.

Abstract: Techniques and apparatuses for highlighting media through weighting of people or contexts are described. This document describes techniques that allow a user to quickly and easily highlight media, such as through generating a highlight reel. The techniques also enable selection of context and person weightings by which to tailor highlight reels.

Abstract: Techniques and apparatuses for highlighting media through weighting of people or contexts are described. This document describes techniques that allow a user to quickly and easily highlight media, such as through generating a highlight reel. The techniques also enable selection of context and person weightings by which to tailor highlight reels.

Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.

Abstract: This application generally relates to systems and methods for generating and rendering visualizations of an object or environment using 2D and 3D image data of the object or the environment captured by a mobile device. In one embodiment, a method includes providing, by the system, a representation of a 3D model of an environment from a first perspective of the virtual camera relative to the 3D model, receiving, by the system, input requesting movement of the virtual camera relative to the 3D model, and selecting, by the system, a first 2D image from a plurality of two dimensional images associated with different capture positions and orientations relative to the 3D model based on association of a capture position and orientation of the first 2D image with a second perspective of the virtual camera relative to the 3D model determined based on the movement.

Abstract: This document describes techniques enabling tagging of visual media on a mobile device. In some cases the techniques determine, based on meeting a threshold of manual tagging of a person or object, to “bulk” tag visual media stored on the mobile device. Thus, the techniques can present, in rapid succession, photos and videos with the recognized person or object to enable the user to quickly and easily confirm or reject the recognition. Also, the techniques can present numerous faces for recognized persons or sub-images for recognized objects on a display at one time, thereby enabling quick and easy confirmation or rejection of the recognitions.

Abstract: This application generally relates to systems and methods for generating and rendering visualizations of an object or environment using 2D and 3D image data of the object or the environment captured by a mobile device. In one embodiment, a method includes providing, by the system, a representation of a 3D model of an environment from a first perspective of the virtual camera relative to the 3D model, receiving, by the system, input requesting movement of the virtual camera relative to the 3D model, and selecting, by the system, a first 2D image from a plurality of two dimensional images associated with different capture positions and orientations relative to the 3D model based on association of a capture position and orientation of the first 2D image with a second perspective of the virtual camera relative to the 3D model determined based on the movement.

Abstract: Techniques and apparatuses for generating highlight reels are described. These techniques and apparatuses enable a user to quickly and easily generate a highlight reel to highlight audiovisual media, such as photos and video clips.

Abstract: This document describes techniques enabling tagging of visual media on a mobile device. In some cases the techniques determine, based on meeting a threshold of manual tagging of a person or object, to “bulk” tag visual media stored on the mobile device. Thus, the techniques can present, in rapid succession, photos and videos with the recognized person or object to enable the user to quickly and easily confirm or reject the recognition. Also, the techniques can present numerous faces for recognized persons or sub-images for recognized objects on a display at one time, thereby enabling quick and easy confirmation or rejection of the recognitions.

Abstract: Techniques and apparatuses for generating highlight reels are described. These techniques and apparatuses enable a user to quickly and easily generate a highlight reel to highlight audiovisual media, such as photos and video clips.

Abstract: Techniques apparatuses for grading images and video clips are described. These techniques and apparatuses enable highlights of these graded images and video clips to be more-quickly created, at a high quality, or using fewer resources.

Abstract: Techniques apparatuses for grading images and video clips are described. These techniques and apparatuses enable highlights of these graded images and video clips to be more-quickly created, at a high quality, or using fewer resources.

Abstract: Techniques and apparatuses for generating highlight reels are described. These techniques and apparatuses enable a user to quickly and easily generate a highlight reel to highlight audiovisual media, such as photos and video clips.

Abstract: Techniques and apparatuses for sharing highlight reels are described. This document describes techniques that allow a user to quickly and easily generate a highlight reel. The techniques also enable sharing of these highlight reels, in some cases automatically on creating the highlight reel, such as to persons known to be shown in the highlight reel.

Abstract: Techniques and apparatuses for highlighting media through weighting of people or contexts are described. This document describes techniques that allow a user to quickly and easily highlight media, such as through generating a highlight reel. The techniques also enable selection of context and person weightings by which to tailor highlight reels.