Abstract: One embodiment of the present invention sets forth a technique for dynamically cropping image data transmitted to an endpoint device. The technique includes computing a first visual interest score for a first visual interest region within a digital image based on content included in the first visual interest region, computing a second visual interest score for a second visual interest region within the digital image based on content included in the second visual interest region, and determining that the first visual interest region is preferred over the second visual interest region based on the first visual interest score and the second visual interest score. The technique further includes setting a location within the first visual interest region as a point of visual interest and transmitting the digital image and the location of the point of visual interest to a computing device for displaying a portion of the digital image that includes the point of visual interest.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.

Abstract: The disclosed computer-implemented method may include (1) accessing a video portraying an object within a set of frames, (2) defining a subset of key frames within the video based on movement of the object across the set of frames, (3) generating, for each key frame within the subset of key frames, a spline outlining the object within the key frame, (4) receiving input to adjust, for a selected key frame within the subset of key frames, a corresponding spline, and (5) interpolating the adjusted spline with a spline in a sequentially proximate key frame to define the object in frames between the selected key frame and the sequentially proximate key frame. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In various embodiments, a clip application computes a set of appearance values for an appearance metric based on shot sequences associated with a media content item. Each appearance value in the set of appearance values indicates a prevalence of a first character in a different shot sequence associated with the media content item. The clip application then performs one or more clustering operations on the shot sequences based on the set of appearance values to generate a first shot cluster. Subsequently, the clip application generates a clip for the media content item based on the first shot cluster. The clip application transmits the clip to an endpoint device for display. Advantageously, relative to primarily manual approaches, the clip application can more efficiently and reliably generate clips for media content items.

Abstract: One embodiment of the present invention sets forth a technique for dynamically cropping image data transmitted to an endpoint device. The technique includes computing a first visual interest score for a first visual interest region within a digital image based on content included in the first visual interest region, computing a second visual interest score for a second visual interest region within the digital image based on content included in the second visual interest region, and determining that the first visual interest region is preferred over the second visual interest region based on the first visual interest score and the second visual interest score. The technique further includes setting a location within the first visual interest region as a point of visual interest and transmitting the digital image and the location of the point of visual interest to a computing device for displaying a portion of the digital image that includes the point of visual interest.

Abstract: In various embodiments, a clip application computes a set of appearance values for an appearance metric based on shot sequences associated with a media content item. Each appearance value in the set of appearance values indicates a prevalence of a first character in a different shot sequence associated with the media content item. The clip application then performs one or more clustering operations on the shot sequences based on the set of appearance values to generate a first shot cluster. Subsequently, the clip application generates a clip for the media content item based on the first shot cluster. The clip application transmits the clip to an endpoint device for display. Advantageously, relative to primarily manual approaches, the clip application can more efficiently and reliably generate clips for media content items.

Abstract: One embodiment of the present invention sets forth a technique for dynamically cropping image data transmitted to an endpoint device. The technique includes computing a first visual interest score for a first visual interest region within a digital image based on content included in the first visual interest region, computing a second visual interest score for a second visual interest region within the digital image based on content included in the second visual interest region, and determining that the first visual interest region is preferred over the second visual interest region based on the first visual interest score and the second visual interest score. The technique further includes setting a location within the first visual interest region as a point of visual interest and transmitting the digital image and the location of the point of visual interest to a computing device for displaying a portion of the digital image that includes the point of visual interest.

Abstract: A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.

Abstract: A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.

Abstract: In various embodiments, a clip application computes a set of appearance values for an appearance metric based on shot sequences associated with a media content item. Each appearance value in the set of appearance values indicates a prevalence of a first character in a different shot sequence associated with the media content item. The clip application then performs one or more clustering operations on the shot sequences based on the set of appearance values to generate a first shot cluster. Subsequently, the clip application generates a clip for the media content item based on the first shot cluster. The clip application transmits the clip to an endpoint device for display. Advantageously, relative to primarily manual approaches, the clip application can more efficiently and reliably generate clips for media content items.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include receiving, as an input, segmented video scenes, where each video scene includes a specified length of video content. The method may further include scanning the video scenes to identify objects within the video scene and also determining a relative importance value for the identified objects. The relative importance value may include an indication of which objects are to be included in a cropped version of the video scene. The method may also include generating a video crop that is to be applied to the video scene such that the resulting cropped version of the video scene includes those identified objects that are to be included based on the relative importance value. The method may also include applying the generated video crop to the video scene to produce the cropped version of the video scene. Various other methods systems and computer-readable media are also disclosed.

Abstract: A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.

Abstract: One embodiment of the present invention sets forth a technique for incorporating a text-containing image into a digital. The technique includes analyzing a digital image to determine one or more text-allowed regions included in the digital image, and, for each of the one or more text-allowed regions, computing a numeric value based on a color contrast between pixels of a text-containing image and pixels of the text-allowed region, wherein the text-containing image is to be incorporated into one of the text-allowed regions included in the digital image. The technique further includes selecting a first text-allowed region based at least in part on the numeric value computed for each text-allowed region, and incorporating the text-containing image into the first text-allowed region included in the digital image.

Abstract: A sequence analyzer compares different episodes of an episodic serial to identify portions of a current episode of the serial that have already been played back to a user. Those portions may include introductory material such as credits, or a recap section that includes content from previous episodes. The sequence analyzer parses previous episodes of the serial and selects a representative frame for each shot sequence. The sequence analyzer then generates a fingerprint for each shot sequence based on the associated representative frame. The sequence analyzer compares fingerprints associated with a current episode of the serial to fingerprints associated with one or more previous episodes of the serial to identify shot sequences that have already been played. The user may then skip those repeated sequences via a playback interface.

Abstract: A sequence analyzer compares different episodes of an episodic serial to identify portions of a current episode of the serial that have already been played back to a user. Those portions may include introductory material such as credits, or a recap section that includes content from previous episodes. The sequence analyzer parses previous episodes of the serial and selects a representative frame for each shot sequence. The sequence analyzer then generates a fingerprint for each shot sequence based on the associated representative frame. The sequence analyzer compares fingerprints associated with a current episode of the serial to fingerprints associated with one or more previous episodes of the serial to identify shot sequences that have already been played. The user may then skip those repeated sequences via a playback interface.