Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: Provided is a framework for generating photorealistic 3D talking faces conditioned only on audio input. In addition, the present disclosure provides associated methods to insert generated faces into existing videos or virtual environments. We decompose faces from video into a normalized space that decouples 3D geometry, head pose, and texture. This allows separating the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. To stabilize temporal dynamics, we propose an auto-regressive approach that conditions the model on its previous visual state. We also capture face illumination in our model using audio-independent 3D texture normalization.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: Provided is a framework for generating photorealistic 3D talking faces conditioned only on audio input. In addition, the present disclosure provides associated methods to insert generated faces into existing videos or virtual environments. We decompose faces from video into a normalized space that decouples 3D geometry, head pose, and texture. This allows separating the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. To stabilize temporal dynamics, we propose an auto-regressive approach that conditions the model on its previous visual state. We also capture face illumination in our model using audio-independent 3D texture normalization.

Abstract: Provided is a framework for generating photorealistic 3D talking faces conditioned only on audio input. In addition, the present disclosure provides associated methods to insert generated faces into existing videos or virtual environments. We decompose faces from video into a normalized space that decouples 3D geometry, head pose, and texture. This allows separating the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. To stabilize temporal dynamics, we propose an auto-regressive approach that conditions the model on its previous visual state. We also capture face illumination in our model using audio-independent 3D texture normalization.

Abstract: A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.

Abstract: A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.

Abstract: A computer-implemented method, computer program product, and computing system is provided for interacting with images having similar content. In an embodiment, a method may include identifying a plurality of photographs as including a common characteristic. The method may also include generating a flipbook media item including the plurality of photographs. The method may further include associating one or more interactive control features with the flipbook media item.

Abstract: A camera captures an image of a user wearing a head mounted device (HMD) that occludes a portion of the user's face. A three-dimensional (3-D) pose that indicates an orientation and a location of the user's face in a camera coordinate system is determined. A representation of the occluded portion of the user's face is determined based on a 3-D model of the user's face. The representation replaces a portion of the HMD in the image based on the 3-D pose of the user's face in the camera coordinate system. In some cases, the 3-D model of the user's face is selected from 3-D models of the user's face stored in a database that is indexed by eye gaze direction. Mixed reality images can be generated by combining virtual reality images, unoccluded portions of the user's face, and representations of an occluded portion of the user's face.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for video segmentation. One of the methods includes receiving a digital video; performing hierarchical graph-based video segmentation on at least one frame of the digital video to generate a boundary representation for the at least one frame; generating a vector representation from the boundary representation for the at least one frame of the digital video, wherein generating the vector representation includes generating a polygon composed of at least three vectors, wherein each vector comprises two vertices connected by a line segment, from a boundary in the boundary representation; linking the vector representation to the at least one frame of the digital video; and storing the vector representation with the at least one frame of the digital video.

Abstract: Images of a plurality of users are captured concurrently with the plurality of users evincing a plurality of expressions. The images are captured using one or more eye tracking sensors implemented in one or more head mounted devices (HMDs) worn by the plurality of first users. A machine learnt algorithm is trained to infer labels indicative of expressions of the users in the images. A live image of a user is captured using an eye tracking sensor implemented in an HMD worn by the user. A label of an expression evinced by the user in the live image is inferred using the machine learnt algorithm that has been trained to predict labels indicative of expressions. The images of the users and the live image can be personalized by combining the images with personalization images of the users evincing a subset of the expressions.

Abstract: An example method is disclosed that includes identifying a training set of images, wherein each image in the training set has an identified bounding box that comprises an object class and an object location for an object in the image. The method also includes segmenting each image of the training set, wherein segments comprise sets of pixels that share visual characteristics, and wherein each segment is associated with an object class. The method further includes clustering the segments that are associated with the same object class, and generating a data structure based on the clustering, wherein entries in the data structure comprise visual characteristics for prototypical segments of objects having the object class and further comprise one or more potential bounding boxes for the objects, wherein the data structure is usable to predict bounding boxes of additional images that include an object having the object class.

Abstract: A camera captures an image of a user wearing a head mounted device (HMD) that occludes a portion of the user's face. A three-dimensional (3-D) pose that indicates an orientation and a location of the user's face in a camera coordinate system is determined. A representation of the occluded portion of the user's face is determined based on a 3-D model of the user's face. The representation replaces a portion of the HMD in the image based on the 3-D pose of the user's face in the camera coordinate system. In some cases, the 3-D model of the user's face is selected from 3-D models of the user's face stored in a database that is indexed by eye gaze direction. Mixed reality images can be generated by combining virtual reality images, unoccluded portions of the user's face, and representations of an occluded portion of the user's face.

Abstract: A camera captures an image of a user wearing a head mounted device (HMD) that occludes a portion of the user's face. A three-dimensional (3-D) pose that indicates an orientation and a location of the user's face in a camera coordinate system is determined. A representation of the occluded portion of the user's face is determined based on a 3-D model of the user's face. The representation replaces a portion of the HMD in the image based on the 3-D pose of the user's face in the camera coordinate system. In some cases, the 3-D model of the user's face is selected from 3-D models of the user's face stored in a database that is indexed by eye gaze direction. Mixed reality images can be generated by combining virtual reality images, unoccluded portions of the user's face, and representations of an occluded portion of the user's face.

Abstract: A camera captures an image of a user wearing a head mounted device (HMD) that occludes a portion of the user's face. A three-dimensional (3-D) pose that indicates an orientation and a location of the user's face in a camera coordinate system is determined. A representation of the occluded portion of the user's face is determined based on a 3-D model of the user's face. The representation replaces a portion of the HMD in the image based on the 3-D pose of the user's face in the camera coordinate system. In some cases, the 3-D model of the user's face is selected from 3-D models of the user's face stored in a database that is indexed by eye gaze direction. Mixed reality images can be generated by combining virtual reality images, unoccluded portions of the user's face, and representations of an occluded portion of the user's face.

Abstract: An easy-to-use online video stabilization system and methods for its use are described. Videos are stabilized after capture, and therefore the stabilization works on all forms of video footage including both legacy video and freshly captured video. In one implementation, the video stabilization system is fully automatic, requiring no input or parameter settings by the user other than the video itself. The video stabilization system uses a cascaded motion model to choose the correction that is applied to different frames of a video. In various implementations, the video stabilization system is capable of detecting and correcting high frequency jitter artifacts, low frequency shake artifacts, rolling shutter artifacts, significant foreground motion, poor lighting, scene cuts, and both long and short videos.

Abstract: A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.