Computing platform using machine learning for foreground mask estimation

A method, comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• receiving a set of images and corresponding ground truth foreground masks;
• training, using the set of images and corresponding ground truth foreground masks, a first neural network to distinguish between image foregrounds and backgrounds, wherein training the first neural network results in a first set of foreground masks each corresponding to an image of the set of images, wherein distinguishing between the image foregrounds and the backgrounds comprises separating an identified subject of a video from a corresponding background, and wherein training the first neural network comprises training the first neural network using a first subset of the set of images corresponding to a first resolution and a first component configuration and a second subset of the set of images corresponding to a second resolution and a second component configuration;
• estimating, for each image of the set of images and based on a corresponding foreground mask, a first background clean plate;
• training, using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, a second neural network, wherein training the second neural network configures the second neural network to output foreground masks based on video input information;
• and
• deploying, to an implementation computing device, the second neural network.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• receive a set of images and corresponding ground truth foreground masks;
• train, using the set of images and corresponding ground truth foreground masks, a first neural network to distinguish between image foregrounds and backgrounds, wherein training the first neural network results in a first set of foreground masks each corresponding to an image of the set of images, wherein distinguishing between the image foregrounds and the backgrounds comprises separating an identified subject of a video from a corresponding background, and wherein training the first neural network comprises training the first neural network using a first subset of the set of images corresponding to a first resolution and a first component configuration and a second subset of the set of images corresponding to a second resolution and a second component configuration;
• estimate, for each image of the set of images and based on a corresponding foreground mask, a first background clean plate;
• train, using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, a second neural network, wherein training the second neural network configures the second neural network to output foreground masks based on video input information;
• and
• deploy, to an implementation computing device, the second neural network.