Facebook, Inc.

Meta Platforms, Inc.

Rahul Nallamothu

Eric W Hwang

Jason Francis Harrison

Shahid Razzaq

Alan Liu

Saurabh Mitra

Samuel Franklin Pepose

Jeffrey Zhang

In one embodiment, a method includes an intelligent communication device detecting that a person is visible to a camera of the device, determining a first biometric characteristic of the person discernable by the device, associating the first biometric characteristic with a user identifier unique to the person, determining, while the person is identifiable based on the first biometric characteristic, a second biometric characteristic of the person discernable by the device, and associating the second biometric characteristic with the user identifier. The method also includes the device determining that a detected person has a detected biometric characteristic, determining that the detected person is associated with the user identifier by matching the detected biometric characteristic to the first biometric characteristic or the second biometric characteristic, and applying, while the detected person is identifiable based on the detected biometric characteristic, a user-specific customization associated with the user identifier.

User-specific customization of video conferences using multimodal biometric characterization

In one embodiment, a method includes accessing foreground visual data that comprises a set of coordinate points that correspond to a plurality of surface points of a person in an environment; generating a bounding box for the set of coordinate points, wherein the bounding box comprises every coordinate point in the set of coordinate points; providing instructions to collect background visual data for an area in the environment that is outside of the bounding box; and providing the foreground visual data and the background visual data to an intelligent director associated with the computing device.

Automated cinematic decisions based on descriptive models

In one embodiment, a method includes accessing input data from one or more different input sources. The input sources include: one or more cameras, one or more microphones, and a social graph maintained by a social-networking system. Based on the input data, generating a current descriptive model for a current audio-video communication session that comprises one or more descriptive characteristics about (1) an environment associated with the current audio-video communication session, (2) one or more people within the environment, or (3) one or more contextual elements associated with the current audio-video communication session. The method also includes generating one or more instructions for the current audio-video communication session that are based the one or more descriptive characteristics; and sending the one or more instructions to a computing device associated with the one or more cameras and the one or more microphones.

In one embodiment, a method includes identifying, from a set of coordinate points that correspond to a plurality of surface points of a person in an environment, a coordinate point that corresponds to a facial feature of the person; generating a facial structure for a face of the person, wherein the facial structure: covers a plurality of facial features of the person; and substantially matches a pre-determined facial structure; generating a body skeletal structure for the person, wherein the body skeletal structure substantially matches a predetermined body skeletal structure and substantially aligns with the generated facial structure in at least one dimension of a two-dimensional coordinate plane; and associating the generated body skeletal structure and facial structure with the person in the environment.

In one embodiment, a method includes, at a first time during an audio-video communication session: (1) determining that a first participant is located in an environment; (2) locating a first body region of the first participant; (3) generating a first color histogram of the first body region that represents a first distribution of colors of the first body region. The method also includes, at a second time: (1) determining that a second participant is located in the environment; (2) locating a second body region of the second participant; (3) generating a second color histogram of the second body region that represents a second distribution of one or more colors of the second body region. The method also includes determining that the first and second color histograms are substantially alike, and based on the determination, determining that the first participant is the same as the second participant.

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Sunnyvale, CA, United States of America

Rahul Nallamothu