Abstract: Aspects of the disclosure relate to using machine learning for foreground mask estimation. A computing platform may receive a set of images and corresponding ground truth foreground masks. Using the set of images and the corresponding ground truth foreground masks, the computing platform may train the first neural network to distinguish between image foregrounds and backgrounds, which may result in a first set of foreground masks. For each image and based on a corresponding foreground mask, the computing platform may estimate a first background clean plate. Using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, the computing platform may train a second neural network, which may configure the second neural network to output foreground masks based on video input information. The computing platform may deploy, to an implementation computing device, the second neural network.

Abstract: Aspects of the disclosure relate to using machine learning for foreground mask estimation. A computing platform may receive a set of images and corresponding ground truth foreground masks. Using the set of images and the corresponding ground truth foreground masks, the computing platform may train the first neural network to distinguish between image foregrounds and backgrounds, which may result in a first set of foreground masks. For each image and based on a corresponding foreground mask, the computing platform may estimate a first background clean plate. Using the set of images, the first background clean plates, and a set of corresponding ground truth mask images, the computing platform may train a second neural network, which may configure the second neural network to output foreground masks based on video input information. The computing platform may deploy, to an implementation computing device, the second neural network.