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公开(公告)号:US11113887B2
公开(公告)日:2021-09-07
申请号:US16428982
申请日:2019-06-01
Applicant: VERIZON PATENT AND LICENSING INC.
Inventor: Daniel Kopeinigg , Arthur van Hoff , Philip Lee , Solmaz Hajmohammadi , Sourabh Khire , Simion Venshtain , Andrew Walkingshaw
Abstract: 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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12.
公开(公告)号:US20200312011A1
公开(公告)日:2020-10-01
申请号:US16830848
申请日:2020-03-26
Applicant: Verizon Patent and Licensing Inc.
Inventor: Daniel Kopeinigg , Andrew Walkingshaw , Arthur van Hoff , Charles LePere , Christopher Redmann , Philip Lee , Solmaz Hajmohammadi , Sourabh Khire , Simion Venshtain
Abstract: 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.
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