Abstract: A method including in a first training process, receiving first 2D training images, generating a scene representation based on the first 2D training images using a machine learning model, generating a loss based on comparing the scene representation to a ground-truth scene representation, and training the machine learning model based on the loss, and in a second training process, receiving a first video frame including second 2D training images, receiving a second video frame including third 2D training images the first frame and the second frame being captured sequentially in time, generating a first intermediate prediction based on the first video frame using the machine learning model, generating a second intermediate prediction based on the second video frame using the machine learning model, generating a loss based on the first intermediate prediction and the second intermediate prediction, and training the machine learning model based on the loss.

Abstract: A method including receiving a plurality of two-dimensional (2D) images representing a frame of a streaming three-dimensional (3D) video, generating a plurality of meshes corresponding to one of the plurality of 2D images, generating a synthesized mesh based on the plurality of meshes, generating a left-eye 3D image and depth based on the synthesized mesh, and generating a right-eye 3D image and depth map based on the synthesized mesh, the left-eye 3D image and depth map and the right-eye 3D image and depth map having a viewpoint perspective based on a receiver of the streaming 3D video.

Abstract: A method including generating a plurality of feature maps based on a plurality of images triggered to capture at a same time, the plurality of images having a plurality of view perspectives, generating a layered depth map based on the plurality of feature maps, and generating an image based on the layered depth map and the plurality of images, the image having a view perspective not included in the plurality of view perspectives.