Verizon Patent and Licensing Inc.

Sourabh Khire

Solmaz Hajmohammadi

Daniel Kopeinigg

Arthur A Van Hoff

Simion Venshtain

Philip Lee

Nithin A Prakash

Andrew Walkingshaw

Ashish Nagpal

Christopher Redmann

Harish Narayan

Suraj Rajpal

Omkar Ghodke

Gaurav Rihan

Trevor Howarth

Sayali G Salvi

Charles LePere

Brian Russ

Haren H Patel

An illustrative framing component of a video conference system identifies, from a plurality of pixels of a video frame, a subset of the pixels of the video frame that depict a participant of a video conference meeting in the video frame. The framing component determines whether the video frame satisfies a set of one or more modification conditions. The framing component modifies, based on the identifying the subset of the pixels and the video frame satisfying the set of one or more modification conditions, the video frame to increase a ratio of the subset of the pixels that depict the participant in the video frame to the plurality of pixels of the video frame. Corresponding methods and systems are also disclosed.

Systems and methods for selectively modifying a video frame of a video conference meeting

A method, a device, and a non-transitory storage medium are described in which a multi-camera switching service is provided. The service may include support of the use of multiple cameras by a user in a video-based communication session. The service may automatically switch between cameras during the session based on switching camera criteria and other configuration data. The service may include facial detection, default camera settings, debouncing, and blending services. The service may also allow the user to transmit a video feed to a subset of participants.

Method and system for automatic camera switching

An exemplary method includes processing, by a virtual avatar processing system, a plurality of video conference recordings generated by a video conference platform to select user image information associated with a plurality of users that participated in video conference sessions represented in the plurality of video conference recordings. The exemplary method further includes generating, based on the user image information, a template model that is generic to a plurality of users, selecting, from the user image information and based on predefined selection criteria, video frames that depict a user included in the plurality of users, and generating a virtual avatar model of the user by applying the selected video frames to the template model. A visual representation of the virtual avatar model may be configured to be used in future video conference sessions in place of a live video image of the user.

Systems and methods for implementing a virtual avatar model for a video conference session

An illustrative video conference management system monitors an encoded video stream corresponding to an original real-time video stream of a video conference meeting. The video conference management system determines context data associated with one or more encoded video frames of the encoded video stream. The context data includes at least one of an encoder metric or a content attribute that indicates an encoding complexity level of the original real-time video stream. The video conference management system adjusts one or more encoded video streams corresponding to the original real-time video stream based on the context data. Corresponding methods and systems are also disclosed.

Systems and methods for adaptive video conferencing

An illustrative volumetric capture system accesses a machine learning model associated with bodies of a particular body type, as well as a two-dimensional (2D) image captured by a capture device located at a real-world scene. The 2D image depicts a body of the particular body type that is present at the real-world scene. Using the machine learning model and based on the 2D image, the volumetric capture system identifies a 2D joint location, from a perspective of the capture device, of a particular joint of the body. The volumetric capture system also generates a three-dimensional (3D) reference model of the body that represents the particular joint of the body at a 3D joint location that is determined based on the 2D joint location identified using the machine learning model. Corresponding methods and systems are also disclosed.

Methods and systems for applying machine learning to volumetric capture of a body in a real-world scene

An exemplary three-dimensional (3D) simulation system accesses a two-dimensional (2D) video image captured by a video capture device and that depicts a bounded real-world scene and a real-world object present within the bounded real-world scene. The 3D simulation system accesses respective 3D models of the bounded real-world scene and the real-world object. Based on the 2D video image, the 3D simulation system tracks a spatial characteristic of the real-world object relative to the bounded real-world scene. Based on the tracked spatial characteristic of the real-world object and the 3D models of the bounded real-world scene and the real-world object, the 3D simulation system generates a 3D simulation of the bounded real-world scene within which the real-world object is simulated in accordance with the tracked spatial characteristic of the real-world object. Corresponding methods and systems are also disclosed.

Methods and systems for performing 3D simulation based on a 2D video image

An illustrative volumetric capture system accesses a two-dimensional ('2D') image captured by a capture device and depicting a first subject of a particular subject type. The volumetric capture system generates a custom three-dimensional ('3D') model of the first subject by identifying a parameter representative of a characteristic of the first subject, applying the parameter to a parametric 3D model to generate a custom mesh, and applying a custom texture based on the 2D image to the custom mesh. The volumetric capture system also accesses a motion capture video depicting motion performed by a second subject of the particular subject type. Based on the motion capture video, the volumetric capture system animates the custom 3D model of the first subject to cause the custom 3D model to perform the motion performed by the second subject. Corresponding methods and systems are also disclosed.

Methods and systems for generating an animated 3D model based on a 2D image

A method includes receiving two-dimensional video streams from a plurality of cameras, the two-dimensional video streams including multiple angles of a sporting event. The method further includes determining boundaries of the sporting event from the two-dimensional video streams. The method further includes identifying a location of a sporting object during the sporting event. The method further includes identifying one or more players in the sporting event. The method further includes identifying poses of each of the one or more players during the sporting event. The method further includes generating a three-dimensional model of the sporting event based on the boundaries of the sporting event, the location of the sporting object during the sporting event, and the poses of each of the one or more players during the sporting event. The method further includes generating a simulation of the three-dimensional model.

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Palo Alto, CA, United States of America

Sourabh Khire