Abstract: Systems and methods are provided that are directed to generating video sequences including physio-realistic avatars. In examples, an albedo for an avatar is received, a sub-surface skin color associated with the albedo is modified based on physiological data associated with physiologic characteristic, and an avatar based on the albedo and the modified sub-surface skin color is rendered. The rendered avatar may then be synthesized in a frame of video. In some examples, a video including the synthesized avatar may be used to train a machine learning model to detect a physiological characteristic. The machine learning model may receive a plurality of video segments, where one or more of the video segments includes a synthetic physio-realistic avatar generated with the physiological characteristic. The machine learning model may be trained using the plurality of video segments. The trained model may be provided to a requesting entity.

Abstract: Aspects of the present disclosure relate to generating an engagement model to predict actions that may have a high probability of maintaining user engagement in-application or causing a user to reengage with the application. To generate the engagement model, an approach has been developed which incorporates features analysis of the application and application users. Users may be grouped based on similar features that are used to generate machine learning engagement models. The output of an engagement model may be a prediction on whether a user will continue to engage with an application. The prediction may be provided to a reengagement model which may output prompts to help increase user engagement with the application. The prompts may be based on an understanding of application users and their preferences.

Abstract: Systems and methods are provided that are directed to generating video sequences including physio-realistic avatars. In examples, an albedo for an avatar is received, a sub-surface skin color associated with the albedo is modified based on physiological data associated with physiologic characteristic, and an avatar based on the albedo and the modified sub-surface skin color is rendered. The rendered avatar may then be synthesized in a frame of video. In some examples, a video including the synthesized avatar may be used to train a machine learning model to detect a physiological characteristic. The machine learning model may receive a plurality of video segments, where one or more of the video segments includes a synthetic physio-realistic avatar generated with the physiological characteristic. The machine learning model may be trained using the plurality of video segments. The trained model may be provided to a requesting entity.

Abstract: Various methods and apparatus relating to estimating and mitigating a stress level of a user are disclosed herein. Methods can include collecting potential stress indicator data from the user interacting with a computing device. The potential stress indicator data can include one or more of environmental data and contextual data associated with the user. Methods can include estimating the stress level of the user based on the potential stress indicator data. Methods can include performing an evaluation of whether to mitigate the stress level of the user via one or more stress mitigation interventions. Methods can include presenting the one or more stress mitigation interventions to the user via a graphical user interface (GUI) when the evaluation indicates that the stress level should be mitigated.

Abstract: Systems and methods for providing a user characteristic to a service provider for a virtual conference with a user are provided. In particular, a computing device may collect raw media data associated with the user during the virtual conference between the user and the service provider. During the virtual conference, the computing device may perform a first processing of the raw media data to extract intermediate user data, wherein the intermediate user data comprises one or more of a physiological signal and a behavioral signal associated the user. The computing device may further transform the raw media data into transformed media data and transmit the transformed media data with the intermediate user data to a server for second processing of the intermediate user data.

Abstract: The present disclosure relate to highlighting audience members with reactions to a presenter of an online meeting. Unlike physical, fact-to-face meeting that enables spontaneous interactions among the presenter and the audiences that are collocated with the presenter, presenting materials during an online meeting raises an issue of the present not being able to see real-time reactions or feedback by the audience members. The present disclosure addresses the issue by dynamically determining one or more audience members who indicate reactions during the online meeting or presentation and displaying faces of the one or more audience members under spotlight to the presenter. The presenter sees faces of the audience members with reactions during the online presentation and responds to the audience members and keep the audience engaged. The spotlight audience server analyzes video frames and determines types of reactions of the audience members.

Abstract: Generally discussed herein are devices, systems, and methods for. A method can include obtaining a normalizing autoencoder, the normalizing autoencoder trained based on first data samples of a template person and second data samples of a variety of people, normalizing, by the normalizing autoencoder, an input data sample by combining dynamic characteristics of a person in the input data sample with static characteristics in the first data samples, to generate normalized data, and providing the normalized data as input to a classifier model to classify the input data based on the dynamic characteristics of the input data and the static characteristics of the first data samples.

Abstract: Systems and methods are provided that are directed to generating video sequences including physio-realistic avatars. In examples, an albedo for an avatar is received, a sub-surface skin color associated with the albedo is modified based on physiological data associated with physiologic characteristic, and an avatar based on the albedo and the modified sub-surface skin color is rendered. The rendered avatar may then be synthesized in a frame of video. In some examples, a video including the synthesized avatar may be used to train a machine learning model to detect a physiological characteristic. The machine learning model may receive a plurality of video segments, where one or more of the video segments includes a synthetic physio-realistic avatar generated with the physiological characteristic. The machine learning model may be trained using the plurality of video segments. The trained model may be provided to a requesting entity.

Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.

Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.