Abstract:
Systems, apparatuses and methods include technology that identifies a first neural network, wherein the first neural network is associated with a first training parameter and first population data that are generated during a process to train the first neural network. The technology executes a first neural network process to serve input data with the first neural network, and estimates a first drift of the first neural network based on the first neural network process, the first training parameter and the first population data to determine whether to retrain the first neural network.
Abstract:
Systems and methods are provided that tune a convolutional neural network (CNN) to increase both its accuracy and computational efficiency. In some examples, a computing device storing the CNN includes a CNN tuner that is a hardware and/or software component that is configured to execute a tuning process on the CNN. When executing according to this configuration, the CNN tuner iteratively processes the CNN layer by layer to compress and prune selected layers. In so doing, the CNN tuner identifies and removes links and neurons that are superfluous or detrimental to the accuracy of the CNN.
Abstract:
Techniques related to automatic perspective control of images using vanishing points are discussed. Such techniques may include determining a perspective control vanishing point associated with the image based on lines detected within the image, rotating the image based on the perspective control vanishing point to generate an aligned image, and warping the aligned image based on aligning two lines of the detected lines that meet at the perspective control vanishing point.
Abstract:
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.
Abstract:
Apparatuses, methods and storage medium associated with creating an avatar video are disclosed herein. In embodiments, the apparatus may one or more facial expression engines, an animation-rendering engine, and a video generator. The one or more facial expression engines may be configured to receive video, voice and/or text inputs, and, in response, generate a plurality of animation messages having facial expression parameters that depict facial expressions for a plurality of avatars based at least in part on the video, voice and/or text inputs received. The animation-rendering engine may be configured to receive the one or more animation messages, and drive a plurality of avatar models, to animate and render the plurality of avatars with the facial expression depicted. The video generator may be configured to capture the animation and rendering of the plurality of avatars, to generate a video. Other embodiments may be described and/or claimed.
Abstract:
Apparatuses, methods and storage medium associated with animating and rendering an avatar are disclosed herein. In embodiments, an apparatus may include a facial mesh tracker to receive a plurality of image frames, detect facial action movements of a face and head pose gestures of a head within the plurality of image frames, and output a plurality of facial motion parameters and head pose parameters that depict facial action movements and head pose gestures detected, all in real time, for animation and rendering of an avatar. The facial action movements and head pose gestures may be detected through inter-frame differences for a mouth and an eye, or the head, based on pixel sampling of the image frames. The facial action movements may include opening or closing of a mouth, and blinking of an eye. The head pose gestures may include head rotation such as pitch, yaw, roll, and head movement along horizontal and vertical direction, and the head comes closer or goes farther from the camera. Other embodiments may be described and/or claimed.