Verizon Patent and Licensing Inc.

Other

Daniel Kopeinigg

Arthur A Van Hoff

Andrew Walkingshaw

Solmaz Hajmohammadi

Sourabh Khire

Simion Venshtain

Philip Lee

Thomas M Annau

Christopher Redmann

Charles LePere

Trevor Howarth

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.

Generating three-dimensional content from two-dimensional images

A method includes receiving head-tracking data that describe one or more positions of people while the people are viewing a three-dimensional video. The method further includes generating a probabilistic model of the one or more positions of the people based on the head-tracking data, wherein the probabilistic model identifies a probability of a viewer looking in a particular direction as a function of time. The method further includes generating video segments from the three-dimensional video. The method further includes, for each of the video segments: determining a directional encoding format that projects latitudes and longitudes of locations of a surface of a sphere onto locations on a plane, determining a cost function that identifies a region of interest on the plane based on the probabilistic model, and generating optimal segment parameters that minimize a sum-over position for the region of interest.

Probabilistic model to compress images for three-dimensional video

The disclosure includes a system and method for stitching images. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive a group of images captured at a particular time by a group of camera modules, the group of camera modules configured to form camera pairs with respective overlap fields of view; calibrate the group of images; generate pairs of disparity maps associated with the camera pairs, the generating including generating a corresponding pair of disparity maps for each camera pair from the camera pairs; apply morphed rendering to create morphed images from the group of images; and construct a left panoramic image and a right panoramic image from the morphed images.

Image stitching to form a three dimensional panoramic image

The disclosure includes a system and method for stitching image frames. The system includes a processor and a memory storing instructions that, when executed, cause the system to: receive image frames that are captured by two or more camera modules at a particular time; interpolate a virtual camera between a first set of camera modules from the two or more camera modules; determine a set of disparity maps between the first set of camera modules; generate, based on the set of disparity maps, a virtual camera image associated with the particular time for the virtual camera from a set of image frames captured by the first set of camera modules at the particular time; and construct a left panoramic image and a right panoramic image associated with the particular time from the image frames captured by the two or more camera modules and the virtual camera image.

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Palo Alto, CA, United States of America

Daniel Kopeinigg