Abstract: Systems and methods for generating shadows for inserted content are provided. The inserted content may include augmented reality content that is inserted into an image of a physical space. An example includes determining a location to insert content within an image. The content may include a polygonal mesh defined in part by a skeleton that has a plurality of joints. Examples may further include selecting a plurality of selected joints form the plurality of joints. Examples may also include generating a shadow polygon based on the content and determining shadow contributions values for the plurality of selected joints for pixels of the shadow polygon. Examples may also include combining the shadow contribution values from the selected joints to generate shadow magnitude values for the pixels, rendering the shadow polygon using the shadow magnitude values, and overlaying the inserted content on the rendered shadow polygon.

Abstract: A method includes grouping media items associated with a user into segments based on a timestamp associated with each media item and a total number of media items. The method also includes selecting target media from the media items for each of the segments based on media attributes associated with the media item. The method also includes generating a video that includes the target media for each of the segments by generating a first animation that illustrates a first transition from a first item from the target media to a second item from the target media with movement of the first item from an onscreen location to an offscreen location, wherein the first item is adjacent to the second item in the first animation and determining whether the target media includes one or more additional items. The method also includes adding a song to the video.

Abstract: A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Abstract: A system for animated painterly picture generation comprises a processor and a memory storing instructions that, when executed, cause the system to: determine a first set of brush strokes, each brush stroke being based on a two-dimensional vector; generate a first frame from the first set of brush strokes; perturb the first set of brush strokes to create additional sets of brush strokes; and generate additional frames from the additional sets of brush strokes, wherein the first frame and the additional frames form a sequence of frames that are time-coherent.

Abstract: A method includes grouping media items associated with a user into segments based on a timestamp associated with each media item and a total number of media items. The method also includes selecting target media from the media items for each of the segments based on media attributes associated with the media item. The method also includes generating a video that includes the target media for each of the segments by generating a first animation that illustrates a first transition from a first item from the target media to a second item from the target media with movement of the first item from an onscreen location to an offscreen location, wherein the first item is adjacent to the second item in the first animation and determining whether the target media includes one or more additional items. The method also includes adding a song to the video.

Abstract: A system for generating painterly pictures comprises a processor and a memory storing instructions that, when executed, cause the system to receive an input image; add a base layer of brush strokes based on a sampling of underlying pixel values in the input image, the base layer comprising coarse large brush strokes that are well-distributed in a uniform pattern; add a uniform layer of brush strokes iteratively based on a sampling of underlying pixel values in the input image and directionally based on a gradient of the input image; and add a detail layer of brush strokes based on a determined saliency map. The disclosure also includes similar methods and computer program products for generating painterly pictures.

Abstract: A method includes grouping media items associated with a user into segments based on a timestamp associated with each media item and a total number of media items. The method also includes selecting target media from the media items for each of the segments based on media attributes associated with the media item. The method also includes generating a video that includes the target media for each of the segments by generating a first animation that illustrates a first transition from a first item from the target media to a second item from the target media with movement of the first item from an onscreen location to an offscreen location, wherein the first item is adjacent to the second item in the first animation and determining whether the target media includes one or more additional items. The method also includes adding a song to the video.

Abstract: A method includes grouping media items associated with a user into segments based on a timestamp associated with each media item and a total number of media items. The method also includes selecting target media from the media items for each of the segments based on media attributes associated with the media item. The method also includes generating a video that includes the target media for each of the segments by generating a first animation that illustrates a first transition from a first item from the target media to a second item from the target media with movement of the first item from an onscreen location to an offscreen location, wherein the first item is adjacent to the second item in the first animation and determining whether the target media includes one or more additional items. The method also includes adding a song to the video.

Abstract: A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Abstract: A system for animated painterly picture generation comprises a processor and a memory storing instructions that, when executed, cause the system to: determine a first set of brush strokes, each brush strokes being based on a two-dimensional vector; generate a first frame from the first set of brush strokes; perturb the first set of brush strokes to create additional sets of brush strokes; and generate additional frames from the additional sets of brush strokes, wherein the first frame, and the additional frames form a sequence of frames that are time-coherent.

Abstract: A system for generating painterly pictures comprises a processor and a memory storing instructions that, when executed, cause the system to receive an input image; add a base layer of brush strokes based on a sampling of underlying pixel values in the input image, the base layer comprising coarse large brush strokes that are well-distributed in a uniform pattern; add a uniform layer of brush strokes iteratively based on a sampling of underlying pixel values in the input image and directionally based on a gradient of the input image; and add a detail layer of brush strokes based on a determined saliency map. The disclosure also includes similar methods and computer program products for generating painterly pictures.