Microsoft Technology Licensing, LLC

Javier Hernandez Rivera

Daniel J McDuff

Mary P Czerwinski

Erroll William Wood

Tadas Baltrusaitis

Zhengyou Zhang

Zicheng Liu

Michael John Conrad

Kyle James Krum

Geoff Hulten

Kael R Rowan

Jin A Suh

David DeBarr

Prasanth Murali

Mohammad Akram

Daniel Mcduff

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.

Human characteristic normalization with an autoencoder

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.

Generating physio-realistic avatars for training non-contact models to recover physiological characteristics

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.

Highlighting expressive participants in an online meeting

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.

Estimating engagement of consumers of presented content

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Cambridge, MA, United States of America

Javier Hernandez Rivera