A computer-implemented method of processing target footage of a target human face includes training an encoder-decoder network comprising an encoder network, a first decoder network, and a second decoder network. The training includes training a first path through the encoder-decoder network including the encoder network and the first decoder network to reconstruct the target footage of the target human face, and training a second path through the encoder-decoder network including the encoder network and the second decoder network to process renders of a synthetic face model exhibiting a range of poses and expressions to determine parameter values for the synthetic face model corresponding to the range of poses and expressions. The method includes processing, using a trained network path comprising or trained using the encoder network and comprising the first decoder network, source data representing the synthetic face model exhibiting a source sequence of expressions, to generate output video data.

Disclosed in the embodiments of the present disclose are a method and device for sending information. A particular embodiment of the method comprises: acquiring user input information input to a user terminal; determining, from a target expression image set, at least one expression image to be sent to the user terminal and matching the user input information, and a presentation order of the at least one expression image; and sending presentation information to the user terminal in response to determining that, during a historical time period, the user terminal presents the at least one expression image according to the presentation order less than or equal to a target number of times, wherein the presentation information is for instructing the user terminal to present the at least one expression image according to the presentation order.

Techniques for machine-trained analysis for multimodal machine learning vehicle manipulation are described. A computing device captures a plurality of information channels, wherein the plurality of information channels includes contemporaneous audio information and video information from an individual. A multilayered convolutional computing system learns trained weights using the audio information and the video information from the plurality of information channels. The trained weights cover both the audio information and the video information and are trained simultaneously. The learning facilitates cognitive state analysis of the audio information and the video information. A computing device within a vehicle captures further information and analyzes the further information using trained weights. The further information that is analyzed enables vehicle manipulation. The further information can include only video data or only audio data. The further information can include a cognitive state metric.

Techniques for user state for user image in media content are described and are implementable to enable a user state of a user to be determined and to control whether a user image is included in media content based on the user state. Generally, the described implementations enable different user states to be defined and utilized to control inclusion of user images with media content.

This disclosure can provide a display device and a display method. The display device includes at least one camera configured to capture an environmental scenario image; a display configured to display a user interface; a controller in communicated with the display, configured to receive a command, input by a user, for obtaining a content recommendation resource associated with content currently displayed in the user interface; determine whether an application corresponding to the content currently displayed in the user interface is an application invoking the at least one camera, and if yes, display a first user interface, where the first user interface displays a first image captured by the at least one camera.

Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.

A method is provided that includes displaying, by a computing device, representations of a plurality of stock videos to a user. The representations are at a still image, a partial clip, and/or a full play of the stock video. Each of the representations include a face outline for insertion of a facial image of a user. When the user has provided a self-image to the computing device, the facial image of the user is inserted in the face outline of the representations. The facial image is extracted from the self-image. The method may include receiving a selection of one of the representations of the plurality of stock videos, and displaying a personalized video including a selected stock video with the facial image positioned within a further face outline corresponding to the face outline of the selected representation.

Provided are methods for automatically adjusting a vehicle seating area based on the characteristics of a passenger. In an example method, a seat adjustment system of a vehicle receives sensor data representing at least one measurement of a user exterior to the vehicle, determines at least one characteristic of the based on the sensor data, determines at least one modification to a seating area of the vehicle based on the at least one characteristic of the user, and causes the seating area to be adjusted in accordance with the at least one modification. Systems and computer program products are also provided.

A travel information sensor senses travel information of a host-vehicle. A biological information sensor senses biological information of a driver. A camera unit senses a facial expression of the driver. A communication unit acquires an agitating degree indicating a degree to which an other-vehicle agitates the driver of the host-vehicle, via a network. An agitated degree calculation unit calculates an agitated degree indicating a degree to which the driver of the host-vehicle is agitated by the other-vehicle. A danger degree determination unit determines a danger degree including whether the driver of the host-vehicle is agitated by the other-vehicle, based on the agitated degree and the agitating degree. A presentation unit warns the host-vehicle of the danger degree if it is determined that the driver of the host-vehicle is agitated by the other-vehicle.

A performance agent training method realized by at least one computer includes observing a first performance of a musical piece by a performer, generating, by a performance agent, performance data of a second performance to be performed in parallel with the first performance, outputting the performance data such that the second performance is performed in parallel with the first performance of the performer, acquiring a degree of satisfaction of the performer with respect to the second performance performed based on the output performance data, and training the performance agent by reinforcement learning, using the degree of satisfaction as a reward.