Abstract:
Generally this disclosure describes a video communication system that replaces actual live images of the participating users with animated avatars. A method may include selecting an avatar, initiating communication, capturing an image, detecting a face in the image, extracting features from the face, converting the facial features to avatar parameters, and transmitting at least one of the avatar selection or avatar parameters.
Abstract:
Techniques are disclosed for performing avatar-based video encoding. In some embodiments, a video recording of an individual may be encoded utilizing an avatar that is driven by the facial expression(s) of the individual. In some such cases, the resultant avatar animation may accurately mimic facial expression(s) of the recorded individual. Some embodiments can be used, for example, in video sharing via social media and networking websites. Some embodiments can be used, for example, in video-based communications (e.g., peer-to-peer video calls; videoconferencing). In some instances, use to the disclosed techniques may help to reduce communications bandwidth use, preserve the individual's anonymity, and/or provide enhanced entertainment value (e.g., amusement) for the individual, for example.
Abstract:
Systems and methods may provide for detecting a condition with respect to one or more frames of a video signal associated with a set of facial motion data and modifying, in response to the condition, the set of facial motion data to indicate that the one or more frames lack facial motion data. Additionally, an avatar animation may be initiated based on the modified set of facial motion data. In one example, the condition is one or more of a buffer overflow condition and a tracking failure condition.
Abstract:
Apparatuses, methods and storage medium associated with animating and rendering an avatar are disclosed herein. In embodiments, the apparatus may include a gesture tracker and an animation engine. The gesture tracker may be configured to detect and track a user gesture that corresponds to a canned facial expression, the user gesture including a duration component corresponding to a duration the canned facial expression is to be animated. Further, the gesture tracker may be configured to respond to a detection and tracking of the user gesture, and output one or more animation messages that describe the detected/tracked user gesture or identify the canned facial expression, and the duration. The animation engine may be configured to receive the one or more animation messages, and drive an avatar model, in accordance with the one or more animation messages, to animate the avatar with animation of the canned facial expressions for the duration. Other embodiments may be described and/or claimed.
Abstract:
Systems and methods may provide for identifying one or more facial expressions of a subject in a video signal and generating avatar animation data based on the one or more facial expressions. Additionally, the avatar animation data may be incorporated into an audio file associated with the video signal. In one example, the audio file is sent to a remote client device via a messaging application. Systems and methods may also facilitate the generation of avatar icons and doll animations that mimic the actual facial features and/or expressions of specific individuals.
Abstract:
Methods, systems and apparatuses may provide for technology that selects a player from a plurality of players based on an automated analysis of two-dimensional (2D) video data associated with a plurality of cameras, wherein the selected player is nearest to a projectile depicted in the 2D video data. The technology may also track a location of the selected player over a subsequent plurality of frames in the 2D video data and estimate a location of the projectile based on the location of the selected player over the subsequent plurality of frames.
Abstract:
A system (600) includes multiple cameras (104) disposed about an area (102), a processor (606), and a memory (608) communicatively coupled to the processor. The memory stores instructions that cause the processor to receive a set of video data (602) associated with the cameras. In an embodiment, the set of video data includes a set of image frames associated with a set of ball tracking data (618, 622). In an embodiment, the operations include selecting a first image frame (626) associated with a first change in acceleration and a second image frame (628) associated with a second change in acceleration. In an embodiment, the operations include generating a set of virtual camera actions (630) based on the first image frame and the second image frame.
Abstract:
Method, systems and apparatuses may provide for technology that extracts one or more motion features from filtered position data associated with a projectile in a game and identifies a turning point in a trajectory of the projectile based on the one or more motion features. The technology may also automatically designate the turning point as a highlight moment if one or more of the turning point or the trajectory satisfies a proximity condition with respect to a target area in the game.
Abstract:
An example apparatus is disclosed herein that includes a memory and at least one processor. The at least one processor is to execute instructions to: select a gesture from a database, the gesture including a sequence of poses; translate the selected gesture into an animated avatar performing the selected gesture for display at a display device; display a prompt for the user to perform the selected gesture performed by the animated avatar; capture an image of the user performing the selected gesture; and perform a comparison between a gesture performed by the user in the captured image and the selected gesture to determine whether there is a match between the gesture performed by the user and the selected gesture.
Abstract:
Methods, systems and apparatuses may provide for technology that detects an individual in a real-time multi-camera video feed and generates three-dimensional (3D) skeletal data based on the real-time multi-camera video feed. The technology may also automatically identify a frontal body orientation of an individual based on the 3D skeletal data and one or more anthropometric constraints.