Abstract: A text-to-image prompt is processed using a text-to-image machine learning model to obtain a non-immersive (e.g., rectilinear image). The non-immersive image may be enhanced by a superresolution machine learning model and processed with a monoscopic depth estimation model to obtain a depthmap. The non-immersive image and the depthmap may be converted to an immersive projection (e.g., F-theta) and corresponding depth map. The immersive projection may be out-painted. The immersive projection may be used to generate video with simulated camera movement, output on a VR headset, and/or processed to remove a background layer and displayed on an AR headset, or on an holographic glasses-free three-dimensional display.

Abstract: Summaries of audio or audio-video events are created from audio or audio-video recordings based on the needs of a particular user. The summarized events may have shorter timespans than the actual timespans of audio or audio-video recordings. Audio or audio-video recordings may be provided by one or more recording devices or sensors to a network, such as a cloud. A summarizer is provided in the network, and may include an audio marker, an audio enhancer, and an audio compiler. The audio marker tags segments of an audio or audio-video stream using one or more audio detectors based on user preferences. The audio enhancer may enhance the quality of tagged audio segments by enhancing desired sound features and suppressing undesired sound features. The audio compiler compiles the tagged audio segments based on event scores and generates audio or audio-video summaries for the user.