Intel Corporation

Samsung Electronics Co., Ltd.

Tahoe Research, Ltd.

Xiaolu Shen

Glen J Anderson

Lidan Zhang

Qiang Li

Wenlong Li

Xiaofeng Tong

Yangzhou Du

Shaohui Jiao

Tae-Hoon Kim

Minje Park

Steven Thomas Holmes

Myung-Ho Ju

Jose E Lorenzo

Mary Smiley

Alok Mishra

Yikai Fang

Xuetao Feng

Qiang Eric Li

Fucen Zeng

Hui Zhang

Jae Joon Han

Jung Bae Kim

Ji Yeun Kim

Biao Wang

Jihyeon Kate Yi

Yeongjae Cheon

Wooju Ryu

Olivier Duchenne

Jihyeon Yi

Margaret E Morris

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. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.

Avatar animation system

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.

Gesture matching mechanism

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.

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.

Positional analysis using computer vision sensor synchronization

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.

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.

Emotion augmented avatar animation

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. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.

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.

Positioning method and apparatus using positioning models

Apparatuses, methods and storage medium associated with 3D face model reconstruction are disclosed herein. In embodiments, an apparatus may include a facial landmark detector, a model fitter and a model tracker. The facial landmark detector may be configured to detect a plurality of landmarks of a face and their locations within each of a plurality of image frames. The model fitter may be configured to generate a 3D model of the face from a 3D model of a neutral face, in view of detected landmarks of the face and their locations within a first one of the plurality of image frames. The model tracker may be configured to maintain the 3D model to track the face in subsequent image frames, successively updating the 3D model in view of detected landmarks of the face and their locations within each of successive ones of the plurality of image frames. In embodiments, the facial landmark detector may include a face detector, an initial facial landmark detector, and one or more facial landmark detection linear regressors. Other embodiments may be described and/or claimed.

3D face model reconstruction apparatus and method

A face tracking apparatus includes: a face region detector; a segmentation unit; an occlusion probability calculator; and a tracking unit. The face region detector is configured to detect a face region based on an input image. The segmentation unit is configured to segment the face region into a plurality of sub-regions. The occlusion probability calculator configured to calculate occlusion probabilities for the plurality of sub-regions. The tracking unit is configured to track a face included in the input image based on the occlusion probabilities.

Apparatuses and methods for face tracking based on calculated occlusion probabilities

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Beijing, China

Xiaolu Shen