Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled, at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor.

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled, at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor.

Abstract: Example gesture matching mechanisms are disclosed herein. An example machine readable storage device or disc includes instructions that, when executed, cause programmable circuitry to at least: prompt a user to perform gestures to register the user, randomly select at least one of the gestures for authentication of the user, prompt the user to perform the at least one selected gesture, translate the gesture into an animated avatar for display at a display device, the animated avatar including a face, analyze performance of the gesture by the user, and authenticate the user based on the performance of the gesture.

Abstract: Example gesture matching mechanisms are disclosed herein. An example machine readable storage device or disc includes instructions that, when executed, cause programmable circuitry to at least: prompt a user to perform gestures to register the user, randomly select at least one of the gestures for authentication of the user, prompt the user to perform the at least one selected gesture, translate the gesture into an animated avatar for display at a display device, the animated avatar including a face, analyze performance of the gesture by the user, and authenticate the user based on the performance of the gesture.

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: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

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: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: A mechanism is described to facilitate gesture matching according to one embodiment. A method of embodiments, as described herein, includes selecting a gesture from a database during an authentication phase, translating the selected gesture into an animated avatar, displaying the avatar, prompting a user to perform the selected gesture, capturing a real-time image of the user and comparing the gesture performed by the user in the captured image to the selected gesture to determine whether there is a match.

Abstract: Apparatuses, methods and storage medium associated with emotion augmented animation of avatars are disclosed herein. In embodiments, an apparatus may comprise an animation augmentation engine to receive facial data of a user, analyze the facial data to determine an emotion state of the user, and drive additional animation that supplements animation of the avatar based at least in part on a result of the determination of the emotion state of the user. Other embodiments may be described and/or claimed.

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor.

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor.

Abstract: Apparatuses, methods and storage medium associated with emotion augmented animation of avatars are disclosed herein. In embodiments, an apparatus may comprise an animation augmentation engine to receive facial data of a user, analyze the facial data to determine an emotion state of the user, and drive additional animation that supplements animation of the avatar based at least in part on a result of the determination of the emotion state of the user. Other embodiments may be described and/or claimed.

Abstract: Apparatuses, methods and storage medium associated with emotion augmented animation of avatars are disclosed herein. In embodiments, an apparatus may comprise an animation augmentation engine to receive facial data of a user, analyze the facial data to determine an emotion state of the user, and drive additional animation that supplements animation of the avatar based at least in part on a result of the determination of the emotion state of the user. Other embodiments may be described and/or claimed.

Abstract: System and techniques for positional analysis using computer vision sensor synchronization are described herein. A set of sensor data may be obtained for a participant of an activity. A video stream may be captured in response to detection of a start of the activity in the set of sensor data. The video stream may include images of the participant engaging in the activity. A key stage of the activity may be identified by evaluation of the sensor data. A key frame may be selected from the video stream using a timestamp of the sensor data used to identify the key stage of the activity. A skeletal map may be generated for the participant in the key frame using key points of the participant extracted from the key frame. Instructional data may be selected using the skeletal map. The instructional data may be displayed on a display device.

Abstract: Provided are a positioning method and apparatus. The positioning method includes acquiring a plurality of positioning results including positions of key points of a facial area included in an input image, respectively using a plurality of predetermined positioning models, evaluating the plurality of positioning results using an evaluation model of the positions of the key points, and updating at least one of the plurality of predetermined positioning models and the evaluation model based on a positioning result that is selected, based on a result of the evaluating, from among the plurality of positioning results.

Abstract: A mechanism is described to facilitate gesture matching according to one embodiment. A method of embodiments, as described herein, includes selecting a gesture from a database during an authentication phase, translating the selected gesture into an animated avatar, displaying the avatar, prompting a user to perform the selected gesture, capturing a real-time image of the user and comparing the gesture performed by the user in the captured image to the selected gesture to determine whether there is a match.

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor.

Abstract: Apparatuses, methods and storage medium associated with emotion augmented animation of avatars are disclosed herein. In embodiments, an apparatus may comprise an animation augmentation engine to receive facial data of a user, analyze the facial data to determine an emotion state of the user, and drive additional animation that supplements animation of the avatar based at least in part on a result of the determination of the emotion state of the user. Other embodiments may be described and/or claimed.