Abstract: In one embodiment, the disclosure provides a computer-implemented method for Progressive Subsampled Transmission of image data. In one embodiment, a source computer may: generate a first down-sampled frame by sampling an input image according to a first sampling pattern; generate a first encoded down-sampled frame; transmit the first encoded down-sampled frame to a recipient device to cause the recipient device to display/use a first output frame generated by decoding and up-sampling the first encoded down-sampled frame; generate a second down-sampled frame by sampling the input image according to a second sampling pattern; generate a second encoded down-sampled frame; and transmit the second encoded down-sampled frame to the recipient device to cause the recipient device to display/use a second output frame generated based on the first encoded down-sampled frame and the second encoded down-sampled frame and in accordance with the first sampling pattern and the second sampling pattern.

Abstract: In one embodiment, a method includes a server generating first shading information for visible portions of objects relative to a first viewpoint, storing the first shading information in a texture atlas, and sending the texture atlas to a client device. The method also includes determining a first subset of the visible portions of the objects for which shading information is to be re-generated and a second subset for which elements of the first shading information are to be reused, generating second shading information for the first subset relative to a second viewpoint, updating the texture atlas to include the second shading information for the first subset and the elements of the first shading information for the second subset, and sending the updated texture atlas to the client device. The updated texture atlas is configured for rendering images of the visible portions of the objects from multiple viewpoints.

Abstract: In one embodiment, the disclosure provides a computer-implemented method for Progressive Subsampled Transmission of image data. In one embodiment, a source computer may: generate a first down-sampled frame by sampling an input image according to a first sampling pattern; generate a first encoded down-sampled frame; transmit the first encoded down-sampled frame to a recipient device to cause the recipient device to display/use a first output frame generated by decoding and up-sampling the first encoded down-sampled frame; generate a second down-sampled frame by sampling the input image according to a second sampling pattern; generate a second encoded down-sampled frame; and transmit the second encoded down-sampled frame to the recipient device to cause the recipient device to display/use a second output frame generated based on the first encoded down-sampled frame and the second encoded down-sampled frame and in accordance with the first sampling pattern and the second sampling pattern.

Abstract: A method includes the steps of receiving training data comprising images of an object and associated camera poses from which the images are captured, training, based on the training data, a machine-learning model to take as input a given viewpoint and synthesize an image of a virtual representation of the object viewed from the given viewpoint, generating, for each of predetermined viewpoints surrounding the virtual representation of the object, a view-dependent image of the object as viewed from that viewpoint using the trained machine-learning model, receiving, from a client device, a desired viewpoint from which to view the virtual representation of the object, selecting one or more of the predetermined viewpoints based on the desired viewpoint, and sending, to the client device, the view-dependent images associated with the selected one or more viewpoints for rendering an output image of the virtual representation of the object viewed from the desired viewpoint.

Abstract: A method includes the steps of receiving training data comprising images of an object and associated camera poses from which the images are captured, training, based on the training data, a machine-learning model to take as input a given viewpoint and synthesize an image of a virtual representation of the object viewed from the given viewpoint, generating, for each of predetermined viewpoints surrounding the virtual representation of the object, a view-dependent image of the object as viewed from that viewpoint using the trained machine-learning model, receiving, from a client device, a desired viewpoint from which to view the virtual representation of the object, selecting one or more of the predetermined viewpoints based on the desired viewpoint, and sending, to the client device, the view-dependent images associated with the selected one or more viewpoints for rendering an output image of the virtual representation of the object viewed from the desired viewpoint.

Abstract: Systems and methods for performing foveated rendering are provided. An example system and method may warp a 3D scene based on a fixation point. The system and method may also render the warped 3D scene to generate a first image. The system and method may also unwarp the first image to generate a second image. For example, the first image may have fewer pixels than the second image.

Abstract: Systems and methods for performing foveated rendering are provided. An example system and method may warp a 3D scene based on a fixation point. The system and method may also render the warped 3D scene to generate a first image. The system and method may also unwarp the first image to generate a second image. For example, the first image may have fewer pixels than the second image.