Disclosed is a method including receiving visual input comprising a human within a scene, detecting a pose associated with the human using a trained machine learning model that detects human poses to yield a first output, estimating a shape (and optionally a motion) associated with the human using a trained machine learning model associated that detects shape (and optionally motion) to yield a second output, recognizing the scene associated with the visual input using a trained convolutional neural network which determines information about the human and other objects in the scene to yield a third output, and augmenting reality within the scene by leveraging one or more of the first output, the second output, and the third output to place 2D and/or 3D graphics in the scene.
| Author(s): | Black, M. and Rachlin, E. and Lee, E. and Heron, N. and Loper, M. and Weiss, A. and Smith, D. |
| Number (issue): | US Patent 10,529,137 B1 |
| Year: | 2020 |
| Month: | January |
| Day: | 7 |
| BibTeX Type: | Patent (patent) |
| Electronic Archiving: | grant_archive |
BibTeX
@patent{Black:Augmentation:2020,
title = {Machine learning systems and methods for augmenting images},
abstract = {Disclosed is a method including receiving visual input comprising a human within a scene, detecting a pose associated with the human using a trained machine learning model that detects human poses to yield a first output, estimating a shape (and optionally a motion) associated with the human using a trained machine learning model associated that detects shape (and optionally motion) to yield a second output, recognizing the scene associated with the visual input using a trained convolutional neural network which determines information about the human and other objects in the scene to yield a third output, and augmenting reality within the scene by leveraging one or more of the first output, the second output, and the third output to place 2D and/or 3D graphics in the scene.},
number = {US Patent 10,529,137 B1},
month = jan,
year = {2020},
author = {Black, M. and Rachlin, E. and Lee, E. and Heron, N. and Loper, M. and Weiss, A. and Smith, D.},
month_numeric = {1}
}